o ©Ì³iKã@s¼ddlmZddlZddlmZdddddd d d d d dddœ Z    ddeeejfdededededeeejff dd„Z   ddeeejfdedededeeejff dd„Z dS)é)ÚDictN)Úget_mapped_keyztok_embeddings.weightzlayers.{}.attn.q_proj.weightzlayers.{}.attn.k_proj.weightzlayers.{}.attn.v_proj.weightz!layers.{}.attn.output_proj.weightzlayers.{}.mlp.w1.weightzlayers.{}.mlp.w3.weightzlayers.{}.mlp.w2.weightzlayers.{}.sa_norm.scalezlayers.{}.mlp_norm.scalez norm.scalez output.weight) zmodel.embed_tokens.weightz'model.layers.{}.self_attn.q_proj.weightz'model.layers.{}.self_attn.k_proj.weightz'model.layers.{}.self_attn.v_proj.weightz'model.layers.{}.self_attn.o_proj.weightz$model.layers.{}.mlp.gate_proj.weightz"model.layers.{}.mlp.up_proj.weightz$model.layers.{}.mlp.down_proj.weightz&model.layers.{}.input_layernorm.weightz/model.layers.{}.post_attention_layernorm.weightzmodel.norm.weightz score.weighté éÚ state_dictÚ num_headsÚ num_kv_headsÚdimÚhead_dimÚreturnc s‚i}ˆdur ˆ|‰‡‡fdd„}| ¡D])\}}|dkrqd|vr't|tƒ} d|vr1|||ƒ}n d|vr:|||ƒ}||| <q|S)aã Convert a state dict from HF's format to torchtune's format, which contains the weights of a reward model (i.e. a classifier with a single class). State dicts from multiple checkpoint files should be consolidated into a single state dict before calling this function. The logic is identical to :func:`~torchtune.models.convert_weights.hf_to_tune`, but with a different mapping. Eg of HF-format state dict can be found in the ``Ray2333/reward-model-Mistral-7B-instruct-Unified-Feedback`` repo in HF. Args: state_dict (Dict[str, torch.Tensor]): State dict in HF's format. num_heads (int): Number of heads in the model. num_kv_heads (int): Number of heads in the key/value projection layers. dim (int): Dimension of the model. head_dim (int): Dimension of the head. If not provided, it will be calculated as dim // num_heads. Returns: Dict[str, torch.Tensor]: State dict in torchtune's format. Ncs(| |dˆdˆ¡ dd¡ ˆ|ˆ¡S©Néé©ÚviewÚ transposeÚreshape©ÚtÚn_heads©r r ©úY/home/ubuntu/.local/lib/python3.10/site-packages/torchtune/rlhf/utils/_convert_weights.pyÚ_permute=ó ýz#reward_hf_to_tune.._permutez score.biaszrotary_emb.inv_freqÚq_projÚk_proj)ÚitemsrÚ_REWARD) rrrr r Úconverted_state_dictrÚkeyÚvalueÚnew_keyrrrÚreward_hf_to_tunes    r#c szi}dd„t ¡Dƒ}ˆ|‰‡‡fdd„}| ¡D] \}}t||ƒ} d|vr-|||ƒ}n d|vr6|||ƒ}||| <q|S)ap Convert a state dict from torchtune's format to Hugging Face's format for a reward model. This function takes a state dictionary in torchtune's format, which contains the weights of a reward model (i.e. a classifier with a single class), and converts it into a format that can be loaded into a Hugging Face model. The logic is identical to :func:`~torchtune.models.convert_weights.tune_to_hf`, but with a different mapping. Args: state_dict (Dict[str, torch.Tensor]): State dict in torchtune's format. num_heads (int, optional): Number of heads in the model. Defaults to 32. num_kv_heads (int, optional): Number of heads in the key/value projection layers. Defaults to 32. dim (int, optional): Dimension of the model. Defaults to 4096. Returns: Dict[str, torch.Tensor]: State dict in Hugging Face's format. cSsi|]\}}||“qSrr)Ú.0ÚkÚvrrrÚ lsz%reward_tune_to_hf..cs(| |ˆddˆ¡ dd¡ ˆ|ˆ¡Sr rrrrrrorz#reward_tune_to_hf.._permuterr)rrr) rrrr rÚinverted_mapping_dictrr r!r"rrrÚreward_tune_to_hfTs    r))rrrN)rrr) ÚtypingrÚtorchÚ torchtune.models.convert_weightsrrÚstrÚTensorÚintr#r)rrrrÚs^  ôû ÿþýüû  ú9ü ÿþýü û