Feat/refactoring: migration to Transformers v5, removing custom MoE backends, and misc. improvements#9
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Feat/refactoring: migration to Transformers v5, removing custom MoE backends, and misc. improvements#9
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Overview
This PR replaces the custom MoE backend system (vLLM, SGLang, FlashInfer, HF -- also noting that newer version of these were quite buggy) with the recently released
transformersv5's built-inQwen3MoeExpertsandQwen3MoeTopKRouter. This drops ~250 lines of backend-specific code and lets transformers handle kernel dispatch automatically.Additionally, several changes to the overall QOL, including linting, bug fixes, and documentation, have been added.
The older version that included the backends code will be left open at the
backendsbrach for future reference.Major Changes
Model (
rnd/modeling_rnd.py)RND1SparseMoeBlockto useQwen3MoeExperts+Qwen3MoeTopKRouterinstead of manually routing tokens through per-expert MLPsrnd1in transformers'_MODEL_TO_CONVERSION_PATTERNso per-expert checkpoint weights are automatically fused into the 3D tensor format during loadinginv_freqrecomputation infrom_pretrained(these buffers are non-persistent and not stored in safetensors)RND1DecoderLayerandRND1Attentionby removing backend-conditional RMSNorm class selection_init_weightswith a no-op (weights always comAdditionally, several changes to the overall QOL including linting, bug fixes, and documentation have been added.e from a checkpoint)Config (
rnd/configuration_rnd.py)moe_backendparameterrope_parametersformatDemo script (
demo_rnd_generation.py)--moe_backendwith--experts-implementation(optional, auto-detected when not set)--top-k,--num-steps, etc.) for consistency with other projectsProject setup
transformers>=5.0.0,torch>=2.8uvxcommands)