[WIP] [SmolLM3] Add Backbone and CausalLM #2327
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Summary of Changes
Hello @DavidLandup0, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!
This pull request, currently a Work In Progress, introduces core utility functions for the SmolLM3 model. It lays the groundwork for the model's backbone and causal language model components by implementing essential attention mechanisms, including helpers for Rotary Positional Embeddings and Grouped Query Attention, culminating in a comprehensive eager attention forward pass.
Highlights
- New Utility File: A new file
keras_hub/src/models/smollm3/smollm3_utils.pyhas been added to house foundational utility functions for the SmolLM3 model. - Rotary Positional Embeddings (RoPE): Helper functions
rotate_half(lines 4-7) andapply_rotary_pos_emb(lines 10-15) are introduced to facilitate the application of Rotary Positional Embeddings to query and key tensors. - Grouped Query Attention (GQA) / Multi-Query Attention (MQA): The
repeat_kvfunction (lines 18-25) is added to support Grouped Query Attention (GQA) or Multi-Query Attention (MQA) by repeating key/value states as needed. - Eager Attention Mechanism: An
eager_attention_forwardfunction (lines 28-52) is implemented, providing a complete attention pass including scaling, causal masking, softmax, and dropout, designed for eager execution.
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Code Review
This PR introduces utility functions for a new SmolLM3 model. The changes are a work in progress. My review focuses on ensuring correctness, robustness, and maintainability.
I've identified a critical issue in rotate_half that could cause problems on the JAX backend, a high-severity issue in eager_attention_forward regarding the unconditional application of dropout, and a medium-severity issue about the lack of docstrings for the new public functions.
Providing this feedback now should help in finalizing the implementation.
| x1 = x[..., : x.shape[-1] // 2] | ||
| x2 = x[..., x.shape[-1] // 2 :] | ||
| return ops.concatenate((-x2, x1), axis=-1) |
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This implementation of rotate_half is susceptible to a known bug with XLA compilation on JAX when using ops.concatenate, as noted in keras_hub/src/layers/modeling/rotary_embedding.py. To ensure backend compatibility and robustness, adopt the safer implementation pattern used in RotaryEmbedding.
| x1 = x[..., : x.shape[-1] // 2] | |
| x2 = x[..., x.shape[-1] // 2 :] | |
| return ops.concatenate((-x2, x1), axis=-1) | |
| x1, x2 = ops.split(x, 2, axis=-1) | |
| half_rot_tensor = ops.stack((-x2, x1), axis=-2) | |
| return ops.reshape(half_rot_tensor, ops.shape(x)) |
| value, | ||
| attention_mask, | ||
| scaling: float, | ||
| dropout: float = 0.0, |
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The eager_attention_forward function should include a training parameter to conditionally apply dropout. This ensures correct behavior during both training and inference.
dropout: float = 0.0,
training: bool = False,| attn_weights = ops.add(attn_weights, causal_mask) | ||
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| attn_weights = ops.softmax(attn_weights, axis=-1) | ||
| attn_weights = random.dropout(attn_weights, rate=dropout) |
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Dropout should only be applied during training to prevent non-deterministic behavior and degraded model performance during inference. Apply dropout conditionally based on the training parameter.
attn_weights = random.dropout(attn_weights, rate=dropout) if training else attn_weights
| layer_types, | ||
| mlp_bias, | ||
| rms_norm_epsilon, | ||
| layer_norm_epsilon, |
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Usually there's some of these terms (like the epsilon's and rope theta) that have a consistent value across all the presets we care about, and we give them defaults here. Not super important, just for people that wanted an easier time making a custom small version of the arch or something like that.
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| def eager_attention_forward( |
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this is a hf/transforms-ism we'd rather not inherit. perfer just keeping the code on the layer directly, fine to use a private helper to decompose it a bit more.
Description of the change
WIP
Colab Notebook
Checklist