0f7853c6a1
* prelim. * add hf convertion fn. * mlp. * change name. * fix bug. * inverse permute. * change comment. * revert style changes. * fix. * add doc. * revert. * enable load safe. * fix safe load. * fix import. * fix typing-related lints. * fix ckpt loading logic. * make single gpu work. * test with parallel. * ckpt format. * enable pretrained state dict. * remove unused imports. * remove unused. * mark idea related.
219 lines
10 KiB
Python
219 lines
10 KiB
Python
# Copyright (c) 2023, Tri Dao.
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import json
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import math
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import os
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import re
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from collections import OrderedDict
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from pathlib import Path
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from typing import Union
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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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state_dict.pop("transformer.rope.freqs", None)
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return state_dict
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def remap_state_dict_hf_llama(state_dict, config):
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# Embedding
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def key_mapping_emb(key):
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return re.sub(r'^model.embed_tokens.', '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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# LM head
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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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# 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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# MLP
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for l in range(config.n_layer):
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# Fusing weights this way based on difference in the following:
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# https://github.com/huggingface/transformers/blob/b42010bb1d3cbf262d27e0a328661885be46dfdb/src/transformers/models/llama/modeling_llama.py#L220
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# https://github.com/Dao-AILab/flash-attention/blob/c60851a8253257eb970e06a022c82517a8033e8c/flash_attn/modules/mlp.py#L115
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w1 = state_dict.pop(f'model.layers.{l}.mlp.gate_proj.weight')
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w3 = state_dict.pop(f'model.layers.{l}.mlp.up_proj.weight')
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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'^model.layers.(\d+).mlp.down_proj.',
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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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# LayerNorm
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def key_mapping_ln(key):
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key = re.sub(r'^model.norm.', r'transformer.ln_f.', key)
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key = re.sub(r'^model.layers.(\d+).input_layernorm.', r'transformer.layers.\1.norm1.', key)
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key = re.sub(r'^model.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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def inv_permute(w):
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# Inverse of permute implemented in:
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# https://github.com/huggingface/transformers/blob/b42010bb1d3cbf262d27e0a328661885be46dfdb/src/transformers/models/llama/convert_llama_weights_to_hf.py#L114
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return w.reshape(
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config.n_head, 2, config.n_embd // config.n_head // 2, config.n_embd
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).transpose(1, 2).reshape(config.n_embd, config.n_embd)
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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'model.layers.{l}.self_attn.q_proj.weight')
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Wk = state_dict.pop(f'model.layers.{l}.self_attn.k_proj.weight')
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Wv = state_dict.pop(f'model.layers.{l}.self_attn.v_proj.weight')
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state_dict[f'transformer.layers.{l}.mixer.Wqkv.weight'] = torch.cat(
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[inv_permute(Wq), inv_permute(Wk), Wv], dim=0
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)
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# We don't store these
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state_dict.pop(f'model.layers.{l}.self_attn.rotary_emb.inv_freq', None)
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def key_mapping_attn(key):
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return re.sub(r'^model.layers.(\d+).self_attn.o_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 config_from_meta_checkpoint(checkpoint_path: Union[str, os.PathLike], 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 config_from_hf_checkpoint(checkpoint_path: Union[str, os.PathLike], model_name: str) -> LlamaConfig:
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return LlamaConfig.from_pretrained(Path(checkpoint_path) / f'{model_name}-hf' / "config.json")
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def config_from_checkpoint(
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checkpoint_path: Union[str, os.PathLike], model_name: str, checkpoint_format="meta"
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) -> LlamaConfig:
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if checkpoint_format == "meta":
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return config_from_meta_checkpoint(checkpoint_path, model_name)
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else:
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return config_from_hf_checkpoint(checkpoint_path, model_name)
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def state_dicts_from_checkpoint(checkpoint_path: Union[str, os.PathLike], model_name: str) -> list[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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