108 lines
4.9 KiB
Python
108 lines
4.9 KiB
Python
# Copyright (c) 2023, Tri Dao.
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import math
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import re
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from collections import OrderedDict
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import torch
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import torch.nn.functional as F
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from einops import rearrange
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from transformers import GPT2Config, GPTNeoXConfig
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def remap_state_dict_hf_gpt_neox(state_dict, config):
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def key_mapping_layers(key):
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return re.sub(r'^gpt_neox.', 'transformer.', key)
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state_dict = OrderedDict((key_mapping_layers(k), v) for k, v in state_dict.items())
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# Word embedding
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def key_mapping_emb(key):
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return re.sub(r'^transformer.embed_in.', 'transformer.embeddings.word_embeddings.', key)
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state_dict = OrderedDict((key_mapping_emb(k), v) for k, v in state_dict.items())
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word_embeddings = state_dict.pop('transformer.embeddings.word_embeddings.weight')
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# It's possible that vocab_size is padded to be a multiple of 8, for example.
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pad_vocab_size_multiple = getattr(config, 'pad_vocab_size_multiple', 1)
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vocab_size = (math.ceil(config.vocab_size / pad_vocab_size_multiple) * pad_vocab_size_multiple)
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state_dict['transformer.embeddings.word_embeddings.weight'] = F.pad(
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word_embeddings, (0, 0, 0, vocab_size - word_embeddings.shape[0])
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)
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if getattr(config, 'tie_word_embeddings'):
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state_dict['lm_head.weight'] = state_dict['transformer.embeddings.word_embeddings.weight']
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else:
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output_embeddings = state_dict.pop('embed_out.weight')
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# It's possible that vocab_size is padded to be a multiple of 8, for example.
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state_dict['lm_head.weight'] = F.pad(
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output_embeddings, (0, 0, 0, vocab_size - output_embeddings.shape[0])
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)
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# LayerNorm
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def key_mapping_ln(key):
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key = re.sub(r'^transformer.final_layer_norm.', r'transformer.ln_f.', key)
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key = re.sub(r'^transformer.layers.(\d+).input_layernorm.', r'transformer.layers.\1.norm1.', key)
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key = re.sub(r'^transformer.layers.(\d+).post_attention_layernorm.', r'transformer.layers.\1.norm2.', key)
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return key
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state_dict = OrderedDict((key_mapping_ln(k), v) for k, v in state_dict.items())
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# MLP
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def key_mapping_mlp(key):
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key = re.sub(r'^transformer.layers.(\d+).mlp.dense_h_to_4h.', r'transformer.layers.\1.mlp.fc1.', key)
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key = re.sub(r'^transformer.layers.(\d+).mlp.dense_4h_to_h.', r'transformer.layers.\1.mlp.fc2.', key)
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return key
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state_dict = OrderedDict((key_mapping_mlp(k), v) for k, v in state_dict.items())
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# Attention
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for l in range(config.n_layer):
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# We don't store these biases
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state_dict.pop(f'transformer.layers.{l}.attention.bias')
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state_dict.pop(f'transformer.layers.{l}.attention.masked_bias')
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# GPT-NeoX stores Wqkv as ((nheads 3 headdim), hidden_dim)
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# while we store Wqkv as ((3 nheads headdim), hidden_dim)
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headdim = config.hidden_size // config.num_attention_heads
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Wqkv = state_dict.pop(f'transformer.layers.{l}.attention.query_key_value.weight')
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state_dict[f'transformer.layers.{l}.mixer.Wqkv.weight'] = rearrange(
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Wqkv, '(nheads three headdim) ... -> (three nheads headdim) ...',
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three=3, headdim=headdim
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)
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bqkv = state_dict.pop(f'transformer.layers.{l}.attention.query_key_value.bias')
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state_dict[f'transformer.layers.{l}.mixer.Wqkv.bias'] = rearrange(
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bqkv, '(nheads three headdim) -> (three nheads headdim)',
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three=3, headdim=headdim
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)
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def key_mapping_attn(key):
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key = re.sub(r'^transformer.layers.(\d+).attention.dense.',
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r'transformer.layers.\1.mixer.out_proj.', key)
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key = re.sub(r'^transformer.layers.(\d+).attention.rotary_emb.',
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r'transformer.layers.\1.mixer.rotary_emb.', key)
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return key
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state_dict = OrderedDict((key_mapping_attn(k), v) for k, v in state_dict.items())
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return state_dict
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def gpt_neox_config_to_gpt2_config(gpt_neox_config: GPTNeoXConfig) -> GPT2Config:
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assert gpt_neox_config.rotary_emb_base == 10000
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return GPT2Config(
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vocab_size=gpt_neox_config.vocab_size,
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n_positions=0, # No absolute position embedding
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n_embd=gpt_neox_config.hidden_size,
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n_layer=gpt_neox_config.num_hidden_layers,
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n_head=gpt_neox_config.num_attention_heads,
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n_inner=gpt_neox_config.intermediate_size,
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activation_function=gpt_neox_config.hidden_act,
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resid_pdrop=0.0, # No dropout
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embd_pdrop=0.0,
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attn_pdrop=0.0,
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layer_norm_epsilon=gpt_neox_config.layer_norm_eps,
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initializer_range=gpt_neox_config.initializer_range,
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bos_token_id=gpt_neox_config.bos_token_id,
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eos_token_id=gpt_neox_config.eos_token_id,
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# These are new arguments not in the original GPT2Config
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prenorm=True,
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parallel_block=gpt_neox_config.use_parallel_residual,
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parallel_block_tied_norm=False,
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rotary_emb_fraction=gpt_neox_config.rotary_pct,
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tie_word_embeddings=gpt_neox_config.tie_word_embeddings,
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)
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