125 lines
5.6 KiB
Python
125 lines
5.6 KiB
Python
# Copyright (c) 2023, Tri Dao.
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import math
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import json
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import re
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from pathlib import Path
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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, LlamaConfig
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def remap_state_dict_meta_llama(state_dict, config):
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def key_mapping_layers(key):
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return f'transformer.{key}' if not key.startswith('output.') else 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.tok_embeddings.', '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(word_embeddings.shape[0] / pad_vocab_size_multiple)
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* 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('output.weight')
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# Need to recompute vocab_size since LLaMa shards the word embeddings and output embeddings
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# differently.
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vocab_size = (math.ceil(output_embeddings.shape[0] / pad_vocab_size_multiple)
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* pad_vocab_size_multiple)
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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.norm.', r'transformer.ln_f.', key)
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key = re.sub(r'^transformer.layers.(\d+).attention_norm.', r'transformer.layers.\1.norm1.', key)
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key = re.sub(r'^transformer.layers.(\d+).ffn_norm.', 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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for l in range(config.n_layer):
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w1 = state_dict.pop(f'transformer.layers.{l}.feed_forward.w1.weight')
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w3 = state_dict.pop(f'transformer.layers.{l}.feed_forward.w3.weight')
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# Our ordering is different
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state_dict[f'transformer.layers.{l}.mlp.fc1.weight'] = torch.cat([w3, w1], dim=0)
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def key_mapping_mlp(key):
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return re.sub(r'^transformer.layers.(\d+).feed_forward.w2.',
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r'transformer.layers.\1.mlp.fc2.', 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}.attention.wq.weight')
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Wk = state_dict.pop(f'transformer.layers.{l}.attention.wk.weight')
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Wv = state_dict.pop(f'transformer.layers.{l}.attention.wv.weight')
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state_dict[f'transformer.layers.{l}.mixer.Wqkv.weight'] = torch.cat([Wq, Wk, Wv], dim=0)
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# We don't store these
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state_dict.pop(f'transformer.layers.{l}.attention.inner_attention.rope.freqs', None)
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def key_mapping_attn(key):
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return re.sub(r'^transformer.layers.(\d+).attention.wo.',
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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 config_from_checkpoint(checkpoint_path: str, model_name: str) -> LlamaConfig:
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"""Load a LlamaConfig from a checkpoint path."""
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with open(Path(checkpoint_path) / model_name / 'params.json') as f:
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params = json.load(f)
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config = LlamaConfig(hidden_size=params['dim'], intermediate_size=None,
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num_attention_heads=params['n_heads'],
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num_hidden_layers=params['n_layers'],
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rms_norm_eps=params['norm_eps'])
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return config
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def state_dicts_from_checkpoint(checkpoint_path: str, model_name: str) -> dict:
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# Need to sort, otherwise we mess up the ordering and the weights are wrong
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return [torch.load(path, map_location='cpu')
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for path in sorted((Path(checkpoint_path) / model_name).glob('consolidated.*.pth'))]
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def llama_config_to_gpt2_config(llama_config: LlamaConfig) -> GPT2Config:
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return GPT2Config(
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vocab_size=llama_config.vocab_size,
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n_positions=0, # No absolute position embedding
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n_embd=llama_config.hidden_size,
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n_layer=llama_config.num_hidden_layers,
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n_head=llama_config.num_attention_heads,
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n_inner=llama_config.intermediate_size,
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activation_function='swiglu', # Hardcode since HF calls it 'silu'
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# Llama doesn't have dropout, idk if it's because they only release the inference code
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resid_pdrop=0.0,
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embd_pdrop=0.0,
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attn_pdrop=0.0,
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layer_norm_epsilon=llama_config.rms_norm_eps,
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initializer_range=llama_config.initializer_range,
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bos_token_id=llama_config.bos_token_id,
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eos_token_id=llama_config.eos_token_id,
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# These are new arguments not in the original GPT2Config
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pad_token_id=llama_config.pad_token_id, # Idk if this does anything
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rms_norm=True,
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rotary_emb_fraction=1.0,
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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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mlp_fc1_bias=False,
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mlp_fc2_bias=False,
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)
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