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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,
)
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