Coverage for transformer_lens/model_bridge/supported_architectures/minimax_m2.py: 100%

1"""MiniMax-M2 architecture adapter.""" 

2 

3from typing import Any 

4 

5from transformer_lens.model_bridge.architecture_adapter import ArchitectureAdapter 

6from transformer_lens.model_bridge.generalized_components import ( 

7 BlockBridge, 

8 EmbeddingBridge, 

9 LinearBridge, 

10 MoEBridge, 

11 MoERouterBridge, 

12 PositionEmbeddingsAttentionBridge, 

13 RMSNormalizationBridge, 

14 RotaryEmbeddingBridge, 

15 UnembeddingBridge, 

16) 

17 

18 

19class MiniMaxM2ArchitectureAdapter(ArchitectureAdapter): 

20 """Architecture adapter for MiniMaxM2ForCausalLM models -- Qwen3-MoE-like, but with 

21 full-width (not per-head) Q/K norm and a sigmoid + e_score_correction_bias router.""" 

22 

23 def __init__(self, cfg: Any) -> None: 

24 """Initialize the MiniMax-M2 architecture adapter.""" 

25 super().__init__(cfg) 

26 

27 self._set_rms_rotary_defaults() 

28 # Verified against MiniMaxAI/MiniMax-M2: tokenizer does not prepend BOS. 

29 self.cfg.default_prepend_bos = False 

30 

31 # QKVO rearrangements; MoE expert and router weights pass through unchanged 

32 self.weight_processing_conversions = { 

33 **self._qkvo_weight_conversions(), 

34 } 

35 

36 self.component_mapping = { 

37 "embed": EmbeddingBridge(name="model.embed_tokens"), 

38 "rotary_emb": RotaryEmbeddingBridge(name="model.rotary_emb", config=self.cfg), 

39 "blocks": BlockBridge( 

40 name="model.layers", 

41 submodules={ 

42 "ln1": RMSNormalizationBridge(name="input_layernorm", config=self.cfg), 

43 "ln2": RMSNormalizationBridge(name="post_attention_layernorm", config=self.cfg), 

44 "attn": PositionEmbeddingsAttentionBridge( 

45 name="self_attn", 

46 config=self.cfg, 

47 submodules={ 

48 "q": LinearBridge(name="q_proj"), 

49 "k": LinearBridge(name="k_proj"), 

50 "v": LinearBridge(name="v_proj"), 

51 "o": LinearBridge(name="o_proj"), 

52 # Full-width (all-heads) RMSNorm, applied pre-reshape. 

53 "q_norm": RMSNormalizationBridge(name="q_norm", config=self.cfg), 

54 "k_norm": RMSNormalizationBridge(name="k_norm", config=self.cfg), 

55 }, 

56 requires_attention_mask=True, 

57 requires_position_embeddings=True, 

58 ), 

59 "mlp": MoEBridge( 

60 name="mlp", 

61 config=self.cfg, 

62 submodules={ 

63 "gate": MoERouterBridge(name="gate"), 

64 }, 

65 ), 

66 }, 

67 ), 

68 "ln_final": RMSNormalizationBridge(name="model.norm", config=self.cfg), 

69 "unembed": UnembeddingBridge(name="lm_head", config=self.cfg), 

70 }