Add ModernBERT model #435
Add ModernBERT model #435
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Pull request overview
This PR adds comprehensive support for ModernBERT, a recent encoder model that modernizes BERT with architectural improvements including RoPE position embeddings, alternating local/global attention, gated linear units (GeGLU), and pre-normalization. The implementation follows established patterns in the codebase and includes proper model-to-HuggingFace parameter mappings.
- Full implementation of ModernBERT model with four architectures:
:base,:for_masked_language_modeling,:for_sequence_classification, and:for_token_classification - Special attention architecture with alternating local (window-based) and global attention layers, each with distinct RoPE theta values
- Test coverage for base and MLM architectures with validation against reference outputs
Reviewed changes
Copilot reviewed 4 out of 4 changed files in this pull request and generated no comments.
| File | Description |
|---|---|
| lib/bumblebee/text/modernbert.ex | Core implementation including encoder with alternating attention patterns, gated FFN, RMS normalization, mean pooling for sequence classification, and tied embeddings for MLM head |
| lib/bumblebee/text/pre_trained_tokenizer.ex | Adds ModernBERT special token configuration (UNK, SEP, PAD, CLS, MASK) |
| lib/bumblebee.ex | Registers ModernBERT model architectures and tokenizer type mapping |
| test/bumblebee/text/modernbert_test.exs | Integration tests for :base and :for_masked_language_modeling architectures with output validation |
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| outputs.hidden_state[[.., 1..3, 1..3]], | ||
| Nx.tensor([ | ||
| [[-0.4497, -2.436, 0.0269], [0.8374, -1.6001, -0.0694], [0.8867, 0.7041, 0.0353]] | ||
| ]) |
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Same comment as in #434 (comment).
The values I get from Python:
tensor([[[ 1.2332, -0.7295, 0.1871],
[ 0.5687, -0.0640, 0.0617],
[ 0.3401, -3.6260, 0.0752]]], grad_fn=<SliceBackward0>)
| # Note: sequence_classification and token_classification tests are skipped | ||
| # because the tiny-random test models have incompatible head structures. | ||
| # The architectures work correctly with production models. |
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What do you mean by "incompatible head structures"? It should handle tiny models as any other pretrained checkpoint.
2 participants
This adds support for ModernBERT, a recent encoder model that improves on BERT with a few architectural changes:
Supported architectures:
:base:for_masked_language_modeling:for_sequence_classification:for_token_classificationThe MLM head uses tied embeddings (shares weights with the input token embeddings).
Reference: https://arxiv.org/abs/2412.13663