[Model] Exaone4 Support

[roycho96]

Summary

Add Liger kernel support for EXAONE4 models (LG AI Research's EXAONE 4.0 series).

Changes

• Add src/liger_kernel/transformers/model/exaone4.py with fused linear cross entropy forward
• Add apply_liger_kernel_to_exaone4() function in monkey_patch.py
• Register in __init__.py
• Add revert_liger_kernel_to_exaone4() in test/utils.py
• Add convergence tests in test/convergence/bf16/ and test/convergence/fp32/

Supported Kernels

• RMSNorm (including QK-Norm in attention)
• SwiGLU MLP
• RoPE
• Fused Linear Cross Entropy

Note on in_place=False for RMSNorm

EXAONE4 requires in_place=False for RMSNorm due to its attention implementation pattern:

# EXAONE4 pattern - separate assignment
query_states = self.q_proj(hidden_states).view(...).transpose(1, 2)
query_states = self.q_norm(query_states)  # reassignment to same variable

# vs Qwen3 pattern - chained
query_states = self.q_norm(self.q_proj(hidden_states).view(...)).transpose(1, 2)

The view/transpose operations share storage with the original tensor. In-place modification corrupts the autograd graph, causing NaN gradients.

References

• Model: https://huggingface.co/LGAI-EXAONE/EXAONE-4.0-1.2B

Testing Done

• Hardware Type: H100
• run make test to ensure correctness
• run make checkstyle to ensure code style
• run make test-convergence to ensure convergence

[shimizust]

Thanks for the contribution!