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| # Copyright (c) 2023, Tri Dao. | |
| import math | |
| import re | |
| from collections import OrderedDict | |
| import torch | |
| import torch.nn.functional as F | |
| from transformers import GPT2Config, GPTJConfig | |
| def remap_state_dict_hf_gptj(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.wte.", "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): | |
| return re.sub(r"^transformer.layers.(\d+).ln_1.", r"transformer.layers.\1.norm1.", 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.fc_in.", r"transformer.layers.\1.mlp.fc1.", key | |
| ) | |
| key = re.sub( | |
| r"^transformer.layers.(\d+).mlp.fc_out.", r"transformer.layers.\1.mlp.fc2.", key | |
| ) | |
| return 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}.attn.q_proj.weight") | |
| Wk = state_dict.pop(f"transformer.layers.{l}.attn.k_proj.weight") | |
| Wv = state_dict.pop(f"transformer.layers.{l}.attn.v_proj.weight") | |
| state_dict[f"transformer.layers.{l}.mixer.Wqkv.weight"] = torch.cat([Wq, Wk, Wv], dim=0) | |
| # We don't store these biases | |
| state_dict.pop(f"transformer.layers.{l}.attn.bias") | |
| state_dict.pop(f"transformer.layers.{l}.attn.masked_bias") | |
| def key_mapping_attn(key): | |
| return re.sub( | |
| r"^transformer.layers.(\d+).attn.out_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 gptj_config_to_gpt2_config(gptj_config: GPTJConfig) -> GPT2Config: | |
| headdim = gptj_config.n_embd // gptj_config.n_head | |
| return GPT2Config( | |
| vocab_size=gptj_config.vocab_size, | |
| n_positions=0, # No absolute position embedding | |
| n_embd=gptj_config.n_embd, | |
| n_layer=gptj_config.n_layer, | |
| n_head=gptj_config.n_head, | |
| n_inner=gptj_config.n_inner, | |
| activation_function=gptj_config.activation_function, | |
| resid_pdrop=gptj_config.resid_pdrop, | |
| embd_pdrop=gptj_config.embd_pdrop, | |
| attn_pdrop=gptj_config.attn_pdrop, | |
| layer_norm_epsilon=gptj_config.layer_norm_epsilon, | |
| initializer_range=gptj_config.initializer_range, | |
| bos_token_id=gptj_config.bos_token_id, | |
| eos_token_id=gptj_config.eos_token_id, | |
| # These are new arguments not in the original GPT2Config | |
| prenorm=True, | |
| parallel_block=True, | |
| parallel_block_tied_norm=True, | |
| rotary_emb_fraction=gptj_config.rotary_dim / headdim, | |
| rotary_emb_interleaved=True, | |
| tie_word_embeddings=False, | |
| qkv_proj_bias=False, | |
| out_proj_bias=False, | |
| lm_head_bias=True, | |
| ) | |