Adding Conformer encoder I/O-styled Transformer encoder

[tango4j]

What does this PR do?

Follow up work after the initial TF encoder PR (#15661). Many NeMo Speech AI maintainers are asking for the new Transformer Encoder implementations to have pre-encode, positional encoding features in conformer encoder.

Aligns the ASR TransformerEncoder module with the offline ConformerEncoder module surface while preserving Transformer-specific attention parameters and behavior.

Streaming encoder and adapter implementations are not included in this PR. These features will be added later on.

Tested LibriSpeech training with Transformer + CTC (BPE). Added transformer_ctc_bpe.yaml with the default configurations.

Collection: ASR

Changelog

• Updated TransformerEncoder to inherit NeMo module/export/access mixins and expose Conformer-style input/output type metadata.
• Added Conformer-style offline encoder utilities, including input_example, forward_for_export, forward_internal, bypass_pre_encode, feat_out, positional encoding, pad mask toggling, stochastic depth, and inter-CTC tensor capture.
• Added Conformer-style pre-encoder options while preserving the Transformer-native FeatureStacking path as subsampling="feature_stacking".
• Moved FeatureStacking into the shared ASR subsampling module so it can be imported from nemo.collections.asr.parts.submodules.subsampling.
• Added self_attention_model mirroring Conformer's positional-encoding switch: "rel_pos" (default), "abs_pos", and "no_pos" (None is accepted as a YAML alias for "no_pos").
• Implemented Transformer-XL relative PE on FlexAttention — (b)+(d) bias via a score_mod closure, (c) bias folded as Q + pos_bias_u; rel-shift is shared with ConformerEncoder via RelPositionMultiHeadAttention.rel_shift.
• Added Transformer encoder tests mirroring relevant Conformer encoder test procedures for stochastic depth and bypass pre-encode behavior.
• Added self_attention_model tests, including a T != n_heads regression for pos_bias_{u,v} broadcasting.
• Updated Transformer encoder tests to use typed-module keyword arguments and validate output lengths.
• Wrapped CPU forward tests in torch.no_grad() so FlexAttention's CPU path doesn't raise under model.train().

Usage

from nemo.collections.asr.modules.transformer_encoder import TransformerEncoder

encoder = TransformerEncoder(
    feat_in=128,
    d_model=512,
    n_heads=8,
    n_layers=17,
    subsampling="feature_stacking",
    subsampling_factor=4,
    self_attention_model="rel_pos",  # one of "rel_pos" | "abs_pos" | "no_pos" (or None)
)

encoded, encoded_len = encoder(audio_signal=features, length=feature_lengths)

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Additional Information

Follow up work after the initial TF encoder PR (#15661)

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[stevehuang52]

Shall we set default xscaling to False, since we already know that the layernorm will zero-out the effect of xscaling?

[tango4j]

Thanks for pointing this out. Setting this with default to False.

[tango4j]

/ok to test 5398604

[github-actions]

[🤖]: Hi @tango4j 👋,

We wanted to let you know that a CICD pipeline for this PR just finished successfully.

So it might be time to merge this PR or get some approvals.

[nithinraok]

Thanks Taejin!

Have you had a chance to run training with this? Does it converges similarly with positional embedding enabled and how the results compare to previous runs.

[tango4j]

Thanks Taejin!

Have you had a chance to run training with this? Does it converges similarly with positional embedding enabled and how the results compare to previous runs.

@KunalDhawan is working on using this PR part to train his MoE transformer experiments.
If time allows, I will also try Librispeech-only training with several setups to do sanity check (to test the points you mentioned)

Recently, after doing some survey, It appeared to me that convnet frontend and positional encoding can affect the performance a lot. So I think we need to test these two configurations separately (ablations).

[tango4j]

@nithinraok
I have tested relative positional encoding with training job. Compared with FastConformer CTC vs Transformer CTC.
Having rel_pos in the Transformer encoder affects the performance a lot (10~20% more error). Need to set this as default.

Also figured that Filterbank Stacking feature is equally good as dw_striding (3 level convnet frontend). Better switching to Filterbank stacking to make this model low precision friendly.

@ipmedenn @KunalDhawan @stevehuang52
Now, transformer is fully equipped with relative positional encoding.

[tango4j]

/ok to test 6725930

[github-actions]

[🤖]: Hi @tango4j 👋,

We wanted to let you know that a CICD pipeline for this PR just finished successfully.

So it might be time to merge this PR or get some approvals.

[nithinraok]

could you also move https://github.com/NVIDIA-NeMo/NeMo/blob/main/examples/asr/conf/fastconformer/transformer_stacking_tdt_bpe.yaml to this folder. examples/asr/conf/transformer/

[nithinraok]

Please remove support for all these

[nithinraok]

Let's drop ConvSubsampling from this PR.

This module lives under ASR and profiling shows it underperforms FeatureStacking on training throughput and complicates inference-tool support, with no accuracy upside. Removing it keeps the PR focused and avoids carrying forward a suboptimal path. Users can add this later if they found it useful for other tasks