code: def remap_state_dict_hf_baichuan(state_dict, config):
      def key_mapping_layers(key):
          return re.sub(r"^model.", "transformer.", key)
  
      state_dict = OrderedDict(
          (key_mapping_layers(k), v) for k, v in state_dict.items()
      )
  
      # Word embedding
      def key_mapping_emb(key):
          return re.sub(
              r"^transformer.embed_tokens.",
              "transformer.embeddings.word_embeddings.",
              key,
          )
  
      state_dict = OrderedDict(
          (key_mapping_emb(k), v) for k, v in state_dict.items()
      )
      word_embeddings = state_dict.pop(
          "transformer.embeddings.word_embeddings.weight"
      )
      # It's possible that vocab_size is padded to be a multiple of 8, for example.
      pad_vocab_size_multiple = getattr(
          config, "pad_vocab_size_multiple", 1
      )
      vocab_size = (
          math.ceil(word_embeddings.shape[0] / pad_vocab_size_multiple)
          * pad_vocab_size_multiple
      )
      state_dict[
          "transformer.embeddings.word_embeddings.weight"
      ] = F.pad(
          word_embeddings,
          (0, 0, 0, vocab_size - word_embeddings.shape[0]),
      )
      if getattr(config, "tie_word_embeddings"):
          state_dict["lm_head.weight"] = state_dict[
              "transformer.embeddings.word_embeddings.weight"
          ]
      else:
          output_embeddings = state_dict.pop("lm_head.weight")
          # Need to recompute vocab_size since Baichuan shards the word embeddings and output embeddings
          # differently.
          vocab_size = (
              math.ceil(
                  output_embeddings.shape[0] / pad_vocab_size_multiple
              )
              * pad_vocab_size_multiple
          )
          # It's possible that vocab_size is padded to be a multiple of 8, for example.
          state_dict["lm_head.weight"] = F.pad(
              output_embeddings,
              (0, 0, 0, vocab_size - output_embeddings.shape[0]),
          )
  
      # LayerNorm
      def key_mapping_ln(key):
          key = re.sub(r"^transformer.norm.", r"transformer.ln_f.", key)
          key = re.sub(
              r"^transformer.layers.(\d+).input_layernorm.",
              r"transformer.layers.\1.norm1.",
              key,
          )
          key = re.sub(
              r"^transformer.layers.(\d+).post_attention_layernorm.",
              r"transformer.layers.\1.norm2.",
              key,
          )
          return key
  
      state_dict = OrderedDict(
          (key_mapping_ln(k), v) for k, v in state_dict.items()
      )
  
      # MLP
      for l in range(config.n_layer):
          w1 = state_dict.pop(
              f"transformer.layers.{l}.mlp.gate_proj.weight"
          )
          w3 = state_dict.pop(
              f"transformer.layers.{l}.mlp.up_proj.weight"
          )
          # Our ordering is different
          state_dict[
              f"transformer.layers.{l}.mlp.fc1.weight"
          ] = torch.cat([w3, w1], dim=0)
  
      def key_mapping_mlp(key):
          return re.sub(
              r"^transformer.layers.(\d+).mlp.down_proj.",
              r"transformer.layers.\1.mlp.fc2.",
              key,
          )
  
      state_dict = OrderedDict(
          (key_mapping_mlp(k), v) for k, v in state_dict.items()
      )
  
      # Attention
      def key_mapping_attn(key):
          key = re.sub(
              r"^transformer.layers.(\d+).self_attn.W_pack.",
              r"transformer.layers.\1.mixer.Wqkv.",
              key,
          )
          key = re.sub(
              r"^transformer.layers.(\d+).self_attn.o_proj.",
              r"transformer.layers.\1.mixer.out_proj.",
              key,
          )
          return key
  
      state_dict = OrderedDict(
          (key_mapping_attn(k), v) for k, v in state_dict.items()
      )
      for l in range(config.n_layer):
          # pop rotary_emb.inv_freq from state dict
          state_dict.pop(
              f"transformer.layers.{l}.self_attn.rotary_emb.inv_freq",
              None,
          )
      return state_dict
  
file_path: flash-attention/flash_attn/models/baichuan.py
imports: import math
  import json
  import re
  from pathlib import Path
  from collections import OrderedDict
  import torch
  import torch.nn.functional
  from einops import rearrange
  from transformers import GPT2Config, AutoConfig, PretrainedConfig
label_desc: Use image node_id 1 for calling a global function with example usage: remap_state_dict_hf_baichuan(state_dict, config) and returns: state_dict, re, re, key, re, key