Conformer-Transducer XXLarge Finetuning Error: size mismatch for encoder.layers.N.conv.depthwise_conv.weight
State: closed | Pull request: no
Labels: name: bug

name: stale

**Describe the bug**

I am trying to finetune the XXL Conformer-Transducer model from a base .nemo checkpoint that I downloaded from https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_en_conformer_transducer_xxlarge

**Steps/Code to reproduce bug**

I run the following command:

```
HYDRA_FULL_ERROR=1 python speech_to_text_rnnt_bpe.py --config-path=configs --config-name=conformer_transducer_bpe model.train_ds.batch_size=4 model.validation_ds.batch_size=4 model.test_ds.batch_size=4 trainer.devices=-1 trainer.accelerator=gpu +init_from_nemo_model=non-finetuned/models/stt_en_conformer_transducer_xxlarge.nemo ~model.encoder.stochastic_depth_drop_prob ~model.encoder.stochastic_depth_mode ~model.encoder.stochastic_depth_start_layer
```

I am using the same hyperparameters in my YAML config file as those recommended for the XLarge model, since there are no explicit recommendations for the XXLarge model.
My config YAML file (conformer_transducer_bpe.yaml) has the following content:
```
# It contains the default values for training a Conformer-Transducer ASR model, large size (~120M) with Transducer loss and sub-word encoding.

# Architecture and training config:
# Default learning parameters in this config are set for effective batch size of 2K. To train it with smaller effective
# batch sizes, you may need to re-tune the learning parameters or use higher accumulate_grad_batches.
# Here are the recommended configs for different variants of Conformer-Transducer, other parameters are the same as in this config file.
#
#  +--------------+---------+---------+----------+------------------+--------------+--------------------------+-----------------+
#  | Model        | d_model | n_heads | n_layers | conv_kernel_size | weight_decay | pred_hidden/joint_hidden | pred_rnn_layers |
#  +==============+=========+========+===========+==================+==============+==========================+=================+
#  | Small   (14M)|   176   |    4   |    16     |       31         |     0.0      |           320            |        1        |
#  +--------------+---------+--------+-----------+------------------+--------------+--------------------------+-----------------+
#  | Medium  (32M)|   256   |    4   |    16     |       31         |     1e-3     |           640            |        1        |
#  +--------------+---------+--------+-----------+------------------+--------------+--------------------------+-----------------+
#  | Large  (120M)|   512   |    8   |    17     |       31         |     1e-3     |           640            |        1        |
#  +--------------+---------+--------+-----------+------------------+--------------+--------------------------+-----------------+
#  | XLarge (644M)|  1024   |    8   |    24     |        5         |     1e-3     |           640            |        2        |
#  +--------------+---------+--------+-----------+------------------+--------------+--------------------------+-----------------+  

# You may find more info about Conformer-Transducer here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/models.html#conformer-transducer
# Pre-trained models of Conformer-Transducer can be found here: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/results.html
# The checkpoint of the large model trained on NeMo ASRSET with this recipe can be found here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_transducer_large

name: "Conformer-Transducer-BPE-Large"

model:
  sample_rate: 16000
  compute_eval_loss: false # eval samples can be very long and exhaust memory. Disable computation of transducer loss during validation/testing with this flag.
  log_prediction: true # enables logging sample predictions in the output during training
  skip_nan_grad: false

  model_defaults:
    enc_hidden: ${model.encoder.d_model}
    pred_hidden: 640
    joint_hidden: 640

  train_ds:
    manifest_filepath: "/workspace/datasets/myst_w2v2_asr/train_manifest.json"
    sample_rate: ${model.sample_rate}
    batch_size: 16 # you may increase batch_size if your memory allows
    shuffle: true
    num_workers: 8
    pin_memory: true
    use_start_end_token: false
    trim_silence: false
    max_duration: 16.7 # it is set for LibriSpeech, you may need to update it for your dataset
    min_duration: 0.1
    # tarred datasets
    is_tarred: false
    tarred_audio_filepaths: null
    shuffle_n: 2048
    # bucketing params
    bucketing_strategy: "synced_randomized"
    bucketing_batch_size: null

  validation_ds:
    manifest_filepath: "/workspace/datasets/myst_w2v2_asr/test_manifest.json"
    sample_rate: ${model.sample_rate}
    batch_size: 16
    shuffle: false
    num_workers: 8
    pin_memory: true
    use_start_end_token: false

  test_ds:
    manifest_filepath: "/workspace/datasets/myst_w2v2_asr/test_manifest.json"
    sample_rate: ${model.sample_rate}
    batch_size: 16
    shuffle: false
    num_workers: 8
    pin_memory: true
    use_start_end_token: false

  # You may find more detail on how to train a tokenizer at: /scripts/tokenizers/process_asr_text_tokenizer.py
  tokenizer:
    dir: "/workspace/projects/nemo_asr/tokenizers/myst_w2v2_asr/tokenizer_spe_unigram_v1024"  # path to directory which contains either tokenizer.model (bpe) or vocab.txt (for wpe)
    type: bpe  # Can be either bpe (SentencePiece tokenizer) or wpe (WordPiece tokenizer)

  preprocessor:
    _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor
    sample_rate: ${model.sample_rate}
    normalize: "per_feature"
    window_size: 0.025
    window_stride: 0.01
    window: "hann"
    features: 80
    n_fft: 512
    frame_splicing: 1
    dither: 0.00001
    pad_to: 0

  spec_augment:
    _target_: nemo.collections.asr.modules.SpectrogramAugmentation
    freq_masks: 2 # set to zero to disable it
    time_masks: 10 # set to zero to disable it
    freq_width: 27
    time_width: 0.05

  encoder:
    _target_: nemo.collections.asr.modules.ConformerEncoder
    feat_in: ${model.preprocessor.features}
    feat_out: -1 # you may set it if you need different output size other than the default d_model
    n_layers: 24
    d_model: 1024

    # Sub-sampling parameters
    subsampling: striding # vggnet, striding, stacking or stacking_norm, dw_striding
    subsampling_factor: 4 # must be power of 2 for striding and vggnet
    subsampling_conv_channels: -1 # set to -1 to make it equal to the d_model
    causal_downsampling: false

    # Reduction parameters: Can be used to add another subsampling layer at a given position.
    # Having a 2x reduction will speedup the training and inference speech while keeping similar WER.
    # Adding it at the end will give the best WER while adding it at the beginning will give the best speedup.
    reduction: null # pooling, striding, or null
    reduction_position: null # Encoder block index or -1 for subsampling at the end of encoder
    reduction_factor: 1

    # Feed forward module's params
    ff_expansion_factor: 4

    # Multi-headed Attention Module's params
    self_attention_model: rel_pos # rel_pos or abs_pos
    n_heads: 8 # may need to be lower for smaller d_models
    # [left, right] specifies the number of steps to be seen from left and right of each step in self-attention
    att_context_size: [-1, -1] # -1 means unlimited context
    att_context_style: regular # regular or chunked_limited
    xscaling: true # scales up the input embeddings by sqrt(d_model)
    untie_biases: true # unties the biases of the TransformerXL layers
    pos_emb_max_len: 5000

    # Convolution module's params
    conv_kernel_size: 5
    conv_norm_type: 'batch_norm' # batch_norm or layer_norm or groupnormN (N specifies the number of groups)
    # conv_context_size can be"causal" or a list of two integers while conv_context_size[0]+conv_context_size[1]+1==conv_kernel_size
    # null means [(kernel_size-1)//2, (kernel_size-1)//2], and 'causal' means [(kernel_size-1), 0]
    conv_context_size: null

    ### regularization
    dropout: 0.1 # The dropout used in most of the Conformer Modules
    dropout_pre_encoder: 0.1 # The dropout used before the encoder
    dropout_emb: 0.0 # The dropout used for embeddings
    dropout_att: 0.1 # The dropout for multi-headed attention modules

    # set to non-zero to enable stochastic depth
    stochastic_depth_drop_prob: 0.0
    stochastic_depth_mode: linear  # linear or uniform
    stochastic_depth_start_layer: 1

  decoder:
    _target_: nemo.collections.asr.modules.RNNTDecoder
    normalization_mode: null # Currently only null is supported for export.
    random_state_sampling: false # Random state sampling: https://arxiv.org/pdf/1910.11455.pdf
    blank_as_pad: true # This flag must be set in order to support exporting of RNNT models + efficient inference.

    prednet:
      pred_hidden: ${model.model_defaults.pred_hidden}
      pred_rnn_layers: 2
      t_max: null
      dropout: 0.2

  joint:
    _target_: nemo.collections.asr.modules.RNNTJoint
    log_softmax: null  # 'null' would set it automatically according to CPU/GPU device
    preserve_memory: false  # dramatically slows down training, but might preserve some memory

    # Fuses the computation of prediction net + joint net + loss + WER calculation
    # to be run on sub-batches of size `fused_batch_size`.
    # When this flag is set to true, consider the `batch_size` of *_ds to be just `encoder` batch size.
    # `fused_batch_size` is the actual batch size of the prediction net, joint net and transducer loss.
    # Using small values here will preserve a lot of memory during training, but will make training slower as well.
    # An optimal ratio of fused_batch_size : *_ds.batch_size is 1:1.
    # However, to preserve memory, this ratio can be 1:8 or even 1:16.
    # Extreme case of 1:B (i.e. fused_batch_size=1) should be avoided as training speed would be very slow.
    fuse_loss_wer: true
    fused_batch_size: 16

    jointnet:
      joint_hidden: ${model.model_defaults.joint_hidden}
      activation: "relu"
      dropout: 0.2

  decoding:
    strategy: "greedy_batch" # can be greedy, greedy_batch, beam, tsd, alsd.

    # greedy strategy config
    greedy:
      max_symbols: 10

    # beam strategy config
    beam:
      beam_size: 2
      return_best_hypothesis: False
      score_norm: true
      tsd_max_sym_exp: 50  # for Time Synchronous Decoding
      alsd_max_target_len: 2.0  # for Alignment-Length Synchronous Decoding

  loss:
    loss_name: "default"

    warprnnt_numba_kwargs:
      # FastEmit regularization: https://arxiv.org/abs/2010.11148
      # You may enable FastEmit to reduce the latency of the model for streaming
      fastemit_lambda: 0.0  # Recommended values to be in range [1e-4, 1e-2], 0.001 is a good start.
      clamp: -1.0  # if > 0, applies gradient clamping in range [-clamp, clamp] for the joint tensor only.

  # Adds Gaussian noise to the gradients of the decoder to avoid overfitting
  variational_noise:
    start_step: 0
    std: 0.0

  optim:
    name: adamw
    lr: 3.0
    # optimizer arguments
    betas: [0.9, 0.98]
    weight_decay: 1e-3

    # scheduler setup
    sched:
      name: NoamAnnealing
      d_model: ${model.encoder.d_model}
      # scheduler config override
      warmup_steps: 40000
      warmup_ratio: null
      min_lr: 1e-6


trainer:
  devices: -1 # number of GPUs, -1 would use all available GPUs
  num_nodes: 1
  max_epochs: 1000
  max_steps: -1 # computed at runtime if not set
  val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations
  accelerator: auto
  strategy: ddp
  accumulate_grad_batches: 1
  gradient_clip_val: 0.0
  precision: 32 # Should be set to 16 for O1 and O2 to enable the AMP.
  log_every_n_steps: 10  # Interval of logging.
  enable_progress_bar: True
  resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc.
  num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it
  check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs
  sync_batchnorm: true
  enable_checkpointing: False  # Provided by exp_manager
  logger: false  # Provided by exp_manager
  benchmark: false # needs to be false for models with variable-length speech input as it slows down training


exp_manager:
  exp_dir: null
  name: ${name}
  create_tensorboard_logger: true
  create_checkpoint_callback: true
  checkpoint_callback_params:
    # in case of multiple validation sets, first one is used
    monitor: "val_wer" # the objective function that determines model performance
    mode: "min"
    save_top_k: 2 # number of best performing .ckpt files to save (last epoch is always saved as a .ckpt file separately)
    save_best_model: true # saves best model as nemo file instead of the default last checkpoint
    always_save_nemo: True # saves the checkpoints as nemo files instead of PTL checkpoints (last checkpoint is saved by default)
  resume_if_exists: false
  resume_ignore_no_checkpoint: false

  create_wandb_logger: true
  wandb_logger_kwargs:
    name: ${name}
    project: "Conformer-Transducer-ASR"
```

**Output**
I get the following error:
```
Traceback (most recent call last):
  File "speech_to_text_rnnt_bpe.py", line 133, in <module>
    main()  # noqa pylint: disable=no-value-for-parameter
  File "/usr/local/lib/python3.8/dist-packages/nemo/core/config/hydra_runner.py", line 105, in wrapper
    _run_hydra(
  File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py", line 389, in _run_hydra
    _run_app(
  File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py", line 452, in _run_app
    run_and_report(
  File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py", line 216, in run_and_report
    raise ex
  File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py", line 213, in run_and_report
    return func()
  File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py", line 453, in <lambda>
    lambda: hydra.run(
  File "/usr/local/lib/python3.8/dist-packages/hydra/_internal/hydra.py", line 132, in run
    _ = ret.return_value
  File "/usr/local/lib/python3.8/dist-packages/hydra/core/utils.py", line 260, in return_value
    raise self._return_value
  File "/usr/local/lib/python3.8/dist-packages/hydra/core/utils.py", line 186, in run_job
    ret.return_value = task_function(task_cfg)
  File "speech_to_text_rnnt_bpe_custom.py", line 114, in main
    asr_model.maybe_init_from_pretrained_checkpoint(cfg)
  File "/usr/local/lib/python3.8/dist-packages/lightning_utilities/core/rank_zero.py", line 27, in wrapped_fn
    return fn(*args, **kwargs)
  File "/usr/local/lib/python3.8/dist-packages/nemo/core/classes/modelPT.py", line 1220, in maybe_init_from_pretrained_checkpoint
    self.load_state_dict(restored_model.state_dict(), strict=False)
  File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 2001, in load_state_dict
    raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for EncDecRNNTBPEModel:
        size mismatch for encoder.layers.0.conv.depthwise_conv.weight: copying a param with shape torch.Size([1024, 1, 31]) from checkpoint, the shape in current model is torch.Size([1024, 1, 5]).
        size mismatch for encoder.layers.1.conv.depthwise_conv.weight: copying a param with shape torch.Size([1024, 1, 31]) from checkpoint, the shape in current model is torch.Size([1024, 1, 5]).
        size mismatch for encoder.layers.2.conv.depthwise_conv.weight: copying a param with shape torch.Size([1024, 1, 31]) from checkpoint, the shape in current model is torch.Size([1024, 1, 5]).
        size mismatch for encoder.layers.3.conv.depthwise_conv.weight: copying a param with shape torch.Size([1024, 1, 31]) from checkpoint, the shape in current model is torch.Size([1024, 1, 5]).
        size mismatch for encoder.layers.4.conv.depthwise_conv.weight: copying a param with shape torch.Size([1024, 1, 31]) from checkpoint, the shape in current model is torch.Size([1024, 1, 5]).
        size mismatch for encoder.layers.5.conv.depthwise_conv.weight: copying a param with shape torch.Size([1024, 1, 31]) from checkpoint, the shape in current model is torch.Size([1024, 1, 5]).
        size mismatch for encoder.layers.6.conv.depthwise_conv.weight: copying a param with shape torch.Size([1024, 1, 31]) from checkpoint, the shape in current model is torch.Size([1024, 1, 5]).
        size mismatch for encoder.layers.7.conv.depthwise_conv.weight: copying a param with shape torch.Size([1024, 1, 31]) from checkpoint, the shape in current model is torch.Size([1024, 1, 5]).
        size mismatch for encoder.layers.8.conv.depthwise_conv.weight: copying a param with shape torch.Size([1024, 1, 31]) from checkpoint, the shape in current model is torch.Size([1024, 1, 5]).
        size mismatch for encoder.layers.9.conv.depthwise_conv.weight: copying a param with shape torch.Size([1024, 1, 31]) from checkpoint, the shape in current model is torch.Size([1024, 1, 5]).
        size mismatch for encoder.layers.10.conv.depthwise_conv.weight: copying a param with shape torch.Size([1024, 1, 31]) from checkpoint, the shape in current model is torch.Size([1024, 1, 5]).
        size mismatch for encoder.layers.11.conv.depthwise_conv.weight: copying a param with shape torch.Size([1024, 1, 31]) from checkpoint, the shape in current model is torch.Size([1024, 1, 5]).
        size mismatch for encoder.layers.12.conv.depthwise_conv.weight: copying a param with shape torch.Size([1024, 1, 31]) from checkpoint, the shape in current model is torch.Size([1024, 1, 5]).
        size mismatch for encoder.layers.13.conv.depthwise_conv.weight: copying a param with shape torch.Size([1024, 1, 31]) from checkpoint, the shape in current model is torch.Size([1024, 1, 5]).
        size mismatch for encoder.layers.14.conv.depthwise_conv.weight: copying a param with shape torch.Size([1024, 1, 31]) from checkpoint, the shape in current model is torch.Size([1024, 1, 5]).
        size mismatch for encoder.layers.15.conv.depthwise_conv.weight: copying a param with shape torch.Size([1024, 1, 31]) from checkpoint, the shape in current model is torch.Size([1024, 1, 5]).
        size mismatch for encoder.layers.16.conv.depthwise_conv.weight: copying a param with shape torch.Size([1024, 1, 31]) from checkpoint, the shape in current model is torch.Size([1024, 1, 5]).
        size mismatch for encoder.layers.17.conv.depthwise_conv.weight: copying a param with shape torch.Size([1024, 1, 31]) from checkpoint, the shape in current model is torch.Size([1024, 1, 5]).
        size mismatch for encoder.layers.18.conv.depthwise_conv.weight: copying a param with shape torch.Size([1024, 1, 31]) from checkpoint, the shape in current model is torch.Size([1024, 1, 5]).
        size mismatch for encoder.layers.19.conv.depthwise_conv.weight: copying a param with shape torch.Size([1024, 1, 31]) from checkpoint, the shape in current model is torch.Size([1024, 1, 5]).
        size mismatch for encoder.layers.20.conv.depthwise_conv.weight: copying a param with shape torch.Size([1024, 1, 31]) from checkpoint, the shape in current model is torch.Size([1024, 1, 5]).
        size mismatch for encoder.layers.21.conv.depthwise_conv.weight: copying a param with shape torch.Size([1024, 1, 31]) from checkpoint, the shape in current model is torch.Size([1024, 1, 5]).
        size mismatch for encoder.layers.22.conv.depthwise_conv.weight: copying a param with shape torch.Size([1024, 1, 31]) from checkpoint, the shape in current model is torch.Size([1024, 1, 5]).
        size mismatch for encoder.layers.23.conv.depthwise_conv.weight: copying a param with shape torch.Size([1024, 1, 31]) from checkpoint, the shape in current model is torch.Size([1024, 1, 5]).
```

**Expected behavior**

Should run finetuning successfully, which is the case when I tested with the Conformer-Transducer Small, Medium, Large, and XLarge .nemo models.

**Environment details**

Docker container created from image: nvcr.io/nvidia/nemo:23.02

**Additional context**

GPU model: quad A6000