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@danielhanchen danielhanchen commented Apr 8, 2025

Make sure to read the contributing guidelines before submitting a PR

Summary by CodeRabbit

  • New Features
    • Enhanced the mapping configuration to support a new generation model architecture (llama4), expanding overall system versatility.
    • Revised mapping definitions now improve compatibility with a broader range of model components.
    • These updates contribute to smoother performance and more robust integration for advanced model variations.
    • The improvements lay a solid foundation for future enhancements and increased adaptability.

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coderabbitai bot commented Apr 8, 2025

Walkthrough

The pull request extends the tensor mapping configuration in the gguf-py/gguf/tensor_mapping.py file. It adds several new tensor mappings to the TensorNameMap class, updating both the general mappings (mappings_cfg) and block-specific mappings (block_mappings_cfg). These changes are introduced to support additional tensor names for the "llama4" model architecture.

Changes

File Change Summary
gguf-py/gguf/tensor_mapping.py - Added new tensor mappings ("language_model.model.embed_tokens", "language_model.lm_head", "language_model.model.norm") in TensorNameMap.mappings_cfg.
- Added multiple new block mappings (e.g., input_layernorm, self_attn.q_proj, self_attn.k_proj, self_attn.v_proj, self_attn.o_proj, post_attention_layernorm, and various feed_forward configurations) in TensorNameMap.block_mappings_cfg for supporting the "llama4" model architecture.

Sequence Diagram(s)

sequenceDiagram
  participant U as User
  participant TM as TensorNameMap

  U->>TM: Request tensor mapping for a given key
  alt Lookup in mappings_cfg
    TM->>TM: Retrieve mapping (embed_tokens, lm_head, norm)
  end
  alt Lookup in block_mappings_cfg
    TM->>TM: Retrieve mapping (layers: input_layernorm, self_attn, feed_forward, etc.)
  end
  TM-->>U: Return the mapped configuration
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Poem

I'm a rabbit in the code so bright,
Hopping through mappings by day and night.
New tensor names now dance in the air,
With llama4 support, they shine with flair!
Crunching carrots of code with every bite,
Happy hops as the changes take flight.
🥕✨


📜 Recent review details

Configuration used: CodeRabbit UI
Review profile: CHILL
Plan: Pro

📥 Commits

Reviewing files that changed from the base of the PR and between 656babd and b14a449.

📒 Files selected for processing (1)
  • gguf-py/gguf/tensor_mapping.py (16 hunks)
🧰 Additional context used
🧬 Code Definitions (1)
gguf-py/gguf/tensor_mapping.py (1)
gguf-py/gguf/constants.py (1)
  • MODEL_TENSOR (295-431)
🔇 Additional comments (5)
gguf-py/gguf/tensor_mapping.py (5)

33-33: Tensor mapping for Llama4 token embeddings looks good

The tensor mapping added for Llama4's token embeddings follows the appropriate pattern with the "language_model.model.embed_tokens" path, which is distinct from but similar to other Llama variants.


71-71: Tensor mapping for Llama4 output layer looks good

The "language_model.lm_head" tensor mapping for the output layer is properly added and maintains the consistent naming pattern used by other model architectures.


94-94: Tensor mapping for Llama4 output normalization looks good

The added "language_model.model.norm" tensor mapping for output normalization follows the consistent pattern with other model architectures.


136-136: Tensor mappings for Llama4 attention components look good

The tensor mappings for attention components (input layernorm, query/key/value/output projections, and post-attention layernorm) follow the expected naming pattern for Llama4 architecture. These mappings maintain consistency with other model architectures while accounting for Llama4's specific naming structure.

Also applies to: 176-176, 191-191, 205-205, 232-232, 270-270


290-290: Tensor mappings for Llama4 feed-forward and MoE components look good

The added mappings properly support Mixture of Experts (MoE) architecture components including:

  • Router tensor for expert routing
  • Standard feed-forward projections (up/gate/down)
  • Expert-specific projections
  • Shared expert projections

These mappings follow the consistent pattern with other MoE model architectures like Mixtral while maintaining Llama4's specific naming structure.

Also applies to: 328-328, 337-337, 343-343, 364-364, 373-373, 379-379, 408-408, 418-418, 424-424

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@github-actions github-actions bot added the python label Apr 8, 2025
@ngxson
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ngxson commented Apr 10, 2025

@danielhanchen oh sorry I didn't notice this PR. Thanks a lot!!

I'll "forward" this PR to upstream llama.cpp (doing a git cherry-pick)

@ngxson ngxson changed the base branch from master to xsn/llama4_mapping April 10, 2025 09:37
@ngxson ngxson merged commit 8ed60e3 into ngxson:xsn/llama4_mapping Apr 10, 2025
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2 participants