# Copyright (c) 2023, Tri Dao. import json import math import os import re from collections import OrderedDict from pathlib import Path from typing import Union import torch import torch.nn.functional as F from transformers import GPT2Config, LlamaConfig def remap_state_dict_meta_llama(state_dict, config): def key_mapping_layers(key): return f'transformer.{key}' if not key.startswith('output.') else 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.tok_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(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('output.weight') # Need to recompute vocab_size since LLaMa 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+).attention_norm.', r'transformer.layers.\1.norm1.', key) key = re.sub(r'^transformer.layers.(\d+).ffn_norm.', 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}.feed_forward.w1.weight') w3 = state_dict.pop(f'transformer.layers.{l}.feed_forward.w3.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+).feed_forward.w2.', r'transformer.layers.\1.mlp.fc2.', key) state_dict = OrderedDict((key_mapping_mlp(k), v) for k, v in state_dict.items()) # Attention for l in range(config.n_layer): Wq = state_dict.pop(f'transformer.layers.{l}.attention.wq.weight') Wk = state_dict.pop(f'transformer.layers.{l}.attention.wk.weight') Wv = state_dict.pop(f'transformer.layers.{l}.attention.wv.weight') state_dict[f'transformer.layers.{l}.mixer.Wqkv.weight'] = torch.cat([Wq, Wk, Wv], dim=0) # We don't store these state_dict.pop(f'transformer.layers.{l}.attention.inner_attention.rope.freqs', None) def key_mapping_attn(key): return re.sub(r'^transformer.layers.(\d+).attention.wo.', r'transformer.layers.\1.mixer.out_proj.', key) state_dict = OrderedDict((key_mapping_attn(k), v) for k, v in state_dict.items()) state_dict.pop("transformer.rope.freqs", None) return state_dict def remap_state_dict_hf_llama(state_dict, config): # Embedding def key_mapping_emb(key): return re.sub(r'^model.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]) ) # LM head 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 LLaMa 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]) ) # MLP for l in range(config.n_layer): # Fusing weights this way based on difference in the following: # https://github.com/huggingface/transformers/blob/b42010bb1d3cbf262d27e0a328661885be46dfdb/src/transformers/models/llama/modeling_llama.py#L220 # https://github.com/Dao-AILab/flash-attention/blob/c60851a8253257eb970e06a022c82517a8033e8c/flash_attn/modules/mlp.py#L115 w1 = state_dict.pop(f'model.layers.{l}.mlp.gate_proj.weight') w3 = state_dict.pop(f'model.layers.{l}.mlp.up_proj.weight') state_dict[f'transformer.layers.{l}.mlp.fc1.weight'] = torch.cat([w3, w1], dim=0) def key_mapping_mlp(key): return re.sub(r'^model.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()) # LayerNorm def key_mapping_ln(key): key = re.sub(r'^model.norm.', r'transformer.ln_f.', key) key = re.sub(r'^model.layers.(\d+).input_layernorm.', r'transformer.layers.\1.norm1.', key) key = re.sub(r'^model.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()) def inv_permute(w): # Inverse of permute implemented in: # https://github.com/huggingface/transformers/blob/b42010bb1d3cbf262d27e0a328661885be46dfdb/src/transformers/models/llama/convert_llama_weights_to_hf.py#L114 return w.reshape( config.n_head, 2, config.n_embd // config.n_head // 2, config.n_embd ).transpose(1, 2).reshape(config.n_embd, config.n_embd) # Attention for l in range(config.n_layer): Wq = state_dict.pop(f'model.layers.{l}.self_attn.q_proj.weight') Wk = state_dict.pop(f'model.layers.{l}.self_attn.k_proj.weight') Wv = state_dict.pop(f'model.layers.{l}.self_attn.v_proj.weight') state_dict[f'transformer.layers.{l}.mixer.Wqkv.weight'] = torch.cat( [inv_permute(Wq), inv_permute(Wk), Wv], dim=0 ) # We don't store these state_dict.pop(f'model.layers.{l}.self_attn.rotary_emb.inv_freq', None) def key_mapping_attn(key): return re.sub(r'^model.layers.(\d+).self_attn.o_proj.', r'transformer.layers.\1.mixer.out_proj.', key) state_dict = OrderedDict((key_mapping_attn(k), v) for k, v in state_dict.items()) return state_dict def config_from_meta_checkpoint(checkpoint_path: Union[str, os.PathLike], model_name: str) -> LlamaConfig: """Load a LlamaConfig from a checkpoint path.""" with open(Path(checkpoint_path) / model_name / 'params.json') as f: params = json.load(f) config = LlamaConfig(hidden_size=params['dim'], intermediate_size=None, num_attention_heads=params['n_heads'], num_hidden_layers=params['n_layers'], rms_norm_eps=params['norm_eps']) return config def config_from_hf_checkpoint(checkpoint_path: Union[str, os.PathLike], model_name: str) -> LlamaConfig: return LlamaConfig.from_pretrained(Path(checkpoint_path) / f'{model_name}-hf' / "config.json") def config_from_checkpoint( checkpoint_path: Union[str, os.PathLike], model_name: str, checkpoint_format="meta" ) -> LlamaConfig: if checkpoint_format == "meta": return config_from_meta_checkpoint(checkpoint_path, model_name) else: return config_from_hf_checkpoint(checkpoint_path, model_name) def state_dicts_from_checkpoint(checkpoint_path: Union[str, os.PathLike], model_name: str) -> list[dict]: # Need to sort, otherwise we mess up the ordering and the weights are wrong return [torch.load(path, map_location='cpu') for path in sorted((Path(checkpoint_path) / model_name).glob('consolidated.*.pth'))] def llama_config_to_gpt2_config(llama_config: LlamaConfig) -> GPT2Config: return GPT2Config( vocab_size=llama_config.vocab_size, n_positions=0, # No absolute position embedding n_embd=llama_config.hidden_size, n_layer=llama_config.num_hidden_layers, n_head=llama_config.num_attention_heads, n_inner=llama_config.intermediate_size, activation_function='swiglu', # Hardcode since HF calls it 'silu' # Llama doesn't have dropout, idk if it's because they only release the inference code resid_pdrop=0.0, embd_pdrop=0.0, attn_pdrop=0.0, layer_norm_epsilon=llama_config.rms_norm_eps, initializer_range=llama_config.initializer_range, bos_token_id=llama_config.bos_token_id, eos_token_id=llama_config.eos_token_id, # These are new arguments not in the original GPT2Config pad_token_id=llama_config.pad_token_id, # Idk if this does anything rms_norm=True, rotary_emb_fraction=1.0, rotary_emb_interleaved=True, tie_word_embeddings=False, qkv_proj_bias=False, out_proj_bias=False, mlp_fc1_bias=False, mlp_fc2_bias=False, )