96 lines
4.1 KiB
Python
96 lines
4.1 KiB
Python
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# 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 transformers import GPT2Config, GPTJConfig
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def remap_state_dict_hf_gptj(state_dict, config):
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def key_mapping_layers(key):
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return re.sub(r'^transformer.h.', 'transformer.layers.', 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.wte.', '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('lm_head.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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return re.sub(r'^transformer.layers.(\d+).ln_1.', r'transformer.layers.\1.norm1.', 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.fc_in.', r'transformer.layers.\1.mlp.fc1.', key)
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key = re.sub(r'^transformer.layers.(\d+).mlp.fc_out.', 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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Wq = state_dict.pop(f'transformer.layers.{l}.attn.q_proj.weight')
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Wk = state_dict.pop(f'transformer.layers.{l}.attn.k_proj.weight')
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Wv = state_dict.pop(f'transformer.layers.{l}.attn.v_proj.weight')
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state_dict[f'transformer.layers.{l}.mixer.Wqkv.weight'] = torch.cat(
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[Wq, Wk, Wv], dim=0
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)
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# We don't store these biases
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state_dict.pop(f'transformer.layers.{l}.attn.bias')
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state_dict.pop(f'transformer.layers.{l}.attn.masked_bias')
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def key_mapping_attn(key):
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return re.sub(r'^transformer.layers.(\d+).attn.out_proj.',
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r'transformer.layers.\1.mixer.out_proj.', 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 gptj_config_to_gpt2_config(gptj_config: GPTJConfig) -> GPT2Config:
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headdim = gptj_config.n_embd // gptj_config.n_head
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return GPT2Config(
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vocab_size=gptj_config.vocab_size,
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n_positions=0, # No absolute position embedding
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n_embd=gptj_config.n_embd,
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n_layer=gptj_config.n_layer,
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n_head=gptj_config.n_head,
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n_inner=gptj_config.n_inner,
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activation_function=gptj_config.activation_function,
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resid_pdrop=gptj_config.resid_pdrop,
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embd_pdrop=gptj_config.embd_pdrop,
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attn_pdrop=gptj_config.attn_pdrop,
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layer_norm_epsilon=gptj_config.layer_norm_epsilon,
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initializer_range=gptj_config.initializer_range,
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bos_token_id=gptj_config.bos_token_id,
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eos_token_id=gptj_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=True,
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parallel_block_tied_norm=True,
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rotary_emb_fraction=gptj_config.rotary_dim / headdim,
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rotary_emb_interleaved=True,
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tie_word_embeddings=False,
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qkv_proj_bias=False,
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out_proj_bias=False,
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)
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