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# Copyright (c) 2023, Tri Dao.
import math
import json
import re
from pathlib import Path
from collections import OrderedDict
import torch
import torch.nn.functional as F
from einops import rearrange
from transformers import GPT2Config, AutoConfig, PretrainedConfig
def remap_state_dict_hf_btlm(state_dict, config):
# Word embedding and position embedding
def key_mapping_pos_emb(key):
return re.sub(r"^transformer.wpe.", "transformer.embeddings.position_embeddings.", key)
if "transformer.wpe.weight" in state_dict:
state_dict = OrderedDict((key_mapping_pos_emb(k), v) for k, v in state_dict.items())
word_embeddings = state_dict.pop("transformer.wte.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])
)
state_dict["lm_head.weight"] = state_dict["transformer.embeddings.word_embeddings.weight"]
# LayerNorm
def key_mapping_ln(key):
key = re.sub(r"^transformer.ln_f.(weight|bias)", r"transformer.ln_f.\1", key)
key = re.sub(r"^transformer.h.(\d+).ln_(1|2).(weight|bias)", r"transformer.layers.\1.norm\2.\3", key)
return key
state_dict = OrderedDict((key_mapping_ln(k), v) for k, v in state_dict.items())
# MLP
for d in range(config.num_hidden_layers):
W1 = state_dict.pop(f"transformer.h.{d}.mlp.c_fc.weight")
W3 = state_dict.pop(f"transformer.h.{d}.mlp.c_fc2.weight")
state_dict[f"transformer.layers.{d}.mlp.fc1.weight"] = torch.cat([W1.t(), W3.t()], dim=0)
b1 = state_dict.pop(f"transformer.h.{d}.mlp.c_fc.bias")
b3 = state_dict.pop(f"transformer.h.{d}.mlp.c_fc2.bias")
state_dict[f"transformer.layers.{d}.mlp.fc1.bias"] = torch.cat([b1, b3], dim=0)
W2 = state_dict.pop(f"transformer.h.{d}.mlp.c_proj.weight")
state_dict[f"transformer.layers.{d}.mlp.fc2.weight"] = W2.t()
def key_mapping_mlp(key):
key = re.sub(r"^transformer.h.(\d+).mlp.c_proj.bias", r"transformer.layers.\1.mlp.fc2.bias", key)
return key
state_dict = OrderedDict((key_mapping_mlp(k), v) for k, v in state_dict.items())
# Attention
for d in range(config.num_hidden_layers):
Wqkv = state_dict.pop(f"transformer.h.{d}.attn.c_attn.weight")
state_dict[f"transformer.layers.{d}.mixer.Wqkv.weight"] = Wqkv.t()
Wout = state_dict.pop(f"transformer.h.{d}.attn.c_proj.weight")
state_dict[f"transformer.layers.{d}.mixer.out_proj.weight"] = Wout.t()
state_dict.pop(f"transformer.relative_pe.slopes") # We don't store the Alibi slopes
def key_mapping_attn(key):
key = re.sub(r"^transformer.h.(\d+).attn.c_attn.bias", r"transformer.layers.\1.mixer.Wqkv.bias", key)
key = re.sub(
r"^transformer.h.(\d+).attn.c_proj.bias", r"transformer.layers.\1.mixer.out_proj.bias", key
)
return key
state_dict = OrderedDict((key_mapping_attn(k), v) for k, v in state_dict.items())
return state_dict
def btlm_config_to_gpt2_config(btlm_config: PretrainedConfig) -> GPT2Config:
return GPT2Config(
vocab_size=btlm_config.vocab_size,
n_positions=0 if btlm_config.position_embedding_type == "alibi" else btlm_config.n_positions,
n_embd=btlm_config.hidden_size,
n_layer=btlm_config.num_hidden_layers,
n_head=btlm_config.num_attention_heads,
n_inner=btlm_config.n_inner,
activation_function=btlm_config.activation_function,
resid_pdrop=btlm_config.resid_pdrop,
embd_pdrop=btlm_config.embd_pdrop,
attn_pdrop=btlm_config.attn_pdrop,
layer_norm_epsilon=btlm_config.layer_norm_epsilon,
initializer_range=btlm_config.initializer_range,
bos_token_id=btlm_config.bos_token_id,
eos_token_id=btlm_config.eos_token_id,
# These are new arguments not in the original GPT2Config
use_alibi=btlm_config.position_embedding_type == "alibi",
use_flash_attn=btlm_config.position_embedding_type == "alibi", # Alibi code path requires flash_attn
mup_width_scale=btlm_config.mup_width_scale,
mup_embeddings_multiplier=btlm_config.mup_embeddings_scale,
mup_output_multiplier=btlm_config.mup_output_alpha,
mup_scale_qk_dot_by_d=btlm_config.mup_scale_qk_dot_by_d,
mlp_multiple_of=1,
)
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