code: def remap_state_dict_hf_falcon(state_dict, config):
      def key_mapping_layers(key):
          return re.sub(r"^transformer.h.", "transformer.layers.", 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.word_embeddings.",
              "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(config.vocab_size / 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")
          # 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]),
          )
          output_embeddings_bias = state_dict.pop("lm_head.bias")
          state_dict["lm_head.bias"] = F.pad(
              output_embeddings_bias,
              (0, vocab_size - output_embeddings_bias.shape[0]),
          )
  
      # LayerNorm
      def key_mapping_ln(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,
          )
          key = re.sub(
              r"^transformer.layers.(\d+).ln_attn.",
              r"transformer.layers.\1.norm1.",
              key,
          )
          key = re.sub(
              r"^transformer.layers.(\d+).ln_mlp.",
              r"transformer.layers.\1.norm2.",
              key,
          )
          return key
  
      state_dict = OrderedDict(
          (key_mapping_ln(k), v) for k, v in state_dict.items()
      )
  
      # MLP
      def key_mapping_mlp(key):
          key = re.sub(
              r"^transformer.layers.(\d+).mlp.dense_h_to_4h.",
              r"transformer.layers.\1.mlp.fc1.",
              key,
          )
          key = re.sub(
              r"^transformer.layers.(\d+).mlp.dense_4h_to_h.",
              r"transformer.layers.\1.mlp.fc2.",
              key,
          )
          return key
  
      state_dict = OrderedDict(
          (key_mapping_mlp(k), v) for k, v in state_dict.items()
      )
  
      def key_mapping_attn(key):
          key = re.sub(
              r"^transformer.layers.(\d+).self_attention.query_key_value.",
              r"transformer.layers.\1.mixer.Wqkv.",
              key,
          )
          key = re.sub(
              r"^transformer.layers.(\d+).self_attention.dense.",
              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()
      )
      n_head = config.n_head
      n_head_kv = getattr(config, "n_head_kv", 1)
      headdim = config.hidden_size // n_head
      for l in range(config.n_layer):
          # The weights are stored in a different layout compared to our implementation
          Wqkv = rearrange(
              state_dict.pop(
                  f"transformer.layers.{l}.mixer.Wqkv.weight"
              ),
              "(group ratio headdim) ... -> group ratio headdim ...",
              ratio=n_head // n_head_kv + 2,
              headdim=headdim,
          )
          Wq = rearrange(
              Wqkv[:, :-2],
              "group ratio headdim ... -> (group ratio headdim) ...",
          )
          Wk = rearrange(
              Wqkv[:, [-2]],
              "group ratio headdim ... -> (group ratio headdim) ...",
          )
          Wv = rearrange(
              Wqkv[:, [-1]],
              "group ratio headdim ... -> (group ratio headdim) ...",
          )
          state_dict[
              f"transformer.layers.{l}.mixer.Wqkv.weight"
          ] = torch.cat([Wq, Wk, Wv], dim=0)
  
      return state_dict
  
file_path: flash-attention/flash_attn/models/falcon.py
imports: import math
  import re
  from collections import OrderedDict
  import torch
  import torch.nn.functional
  from einops import rearrange
  from transformers import FalconConfig, GPT2Config
label_desc: Use image node_id 1 for calling a global function with example usage: remap_state_dict_hf_falcon(state_dict, config) and returns: state_dict, re, re, key, key, key