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convert_gptneox_to_hf.py
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import torch
import torch.nn as nn
import argparse
from transformers import GPTNeoXForCausalLM
from transformers import AutoConfig, AutoTokenizer
from transformers.modeling_utils import no_init_weights
import os
def create_empty_gptneox(config):
import torch
import torch.nn as nn
_reset_parameters_linear = nn.Linear.reset_parameters
def dummy(*args, **kargs):
pass
nn.Linear.reset_parameters = dummy
# 1. disable init for faster initialization
# 2. avoid tie token embeddings with lm_head, as we train them separately.
with no_init_weights(_enable=True):
model = GPTNeoXForCausalLM(config).eval()
nn.Linear.reset_parameters = _reset_parameters_linear
return model
def load_decentralized_checkpoint(model, checkpoint_path, n_stages=2, n_layer_per_stage=14):
input_path = checkpoint_path
assert n_stages * n_layer_per_stage >= len(model.gpt_neox.layers)
# assert model.lm_head.weight.data is not model.transformer.wte.weight.data
if n_stages > 1:
for i in range(n_stages):
print(f'loading stage {i}')
checkpoint = torch.load(os.path.join(input_path, f'prank_{i}_checkpoint.pt'), map_location=torch.device("cpu"))
if i == 0:
_tmp = {k[len(f"{0}."):]:v for k,v in checkpoint.items() if k.startswith(f"0.")}
# torch.save(_tmp, os.path.join(output_path, f'pytorch_embs.pt'))
model.gpt_neox.embed_in.weight.data[:] = _tmp['embed_in.weight']
for j in range(n_layer_per_stage):
_tmp = {k[len(f"{j+1}."):]:v for k,v in checkpoint.items() if k.startswith(f"{j+1}.")}
if len(_tmp) == 0:
break
# torch.save(_tmp, os.path.join(output_path, f'pytorch_{j}.pt'))
model.gpt_neox.layers[j].load_state_dict(_tmp)
elif i == n_stages - 1:
for j in range(n_layer_per_stage):
_tmp = {k[len(f"{j}."):]:v for k,v in checkpoint.items() if k.startswith(f"{j}.")}
if len(_tmp) == 0:
break
# torch.save(_tmp, os.path.join(output_path, f'pytorch_{i*n_layer_per_stage + j}.pt'))
model.gpt_neox.layers[i*n_layer_per_stage + j].load_state_dict(_tmp)
if i*n_layer_per_stage + j == len(model.gpt_neox.layers) - 1:
j += 1
break
_tmp = {k[len(f"{j}."):]:v for k,v in checkpoint.items() if k.startswith(f"{j}.")}
if len(_tmp) == 0:
break
# torch.save(_tmp, os.path.join(output_path, f'pytorch_lm_head.pt'))
model.gpt_neox.final_layer_norm.weight.data[:] = _tmp['final_layer_norm.weight']
model.gpt_neox.final_layer_norm.bias.data[:] = _tmp['final_layer_norm.bias']
model.embed_out.weight.data[:] = _tmp['embed_out.weight']
if 'embed_out.bias' in _tmp:
model.embed_out.bias.data[:] = _tmp['embed_out.bias']
else:
for j in range(n_layer_per_stage):
_tmp = {k[len(f"{j}."):]:v for k,v in checkpoint.items() if k.startswith(f"{j}.")}
if len(_tmp) == 0:
break
# torch.save(_tmp, os.path.join(output_path, f'pytorch_{i*n_layer_per_stage + j}.pt'))
model.gpt_neox.layers[i*n_layer_per_stage + j].load_state_dict(_tmp)
else:
for i in range(n_stages):
print(f'loading stage {i}')
checkpoint = torch.load(os.path.join(input_path, f'prank_{i}_checkpoint.pt'), map_location=torch.device("cpu"))
_tmp = {k[len(f"{0}."):]:v for k,v in checkpoint.items() if k.startswith(f"0.")}
# torch.save(_tmp, os.path.join(output_path, f'pytorch_embs.pt'))
model.gpt_neox.embed_in.weight.data[:] = _tmp['embed_in.weight']
for j in range(n_layer_per_stage):
_tmp = {k[len(f"{j+1}."):]:v for k,v in checkpoint.items() if k.startswith(f"{j+1}.")}
if len(_tmp) == 0:
break
# torch.save(_tmp, os.path.join(output_path, f'pytorch_{j}.pt'))
model.gpt_neox.layers[j].load_state_dict(_tmp)
if i*n_layer_per_stage + j == len(model.gpt_neox.layers) - 1:
j += 1
break
_tmp = {k[len(f"{j+1}."):]:v for k,v in checkpoint.items() if k.startswith(f"{j+1}.")}
if len(_tmp) == 0:
break
print(_tmp.keys())
# torch.save(_tmp, os.path.join(output_path, f'pytorch_lm_head.pt'))
model.gpt_neox.final_layer_norm.weight.data[:] = _tmp['final_layer_norm.weight']
model.gpt_neox.final_layer_norm.bias.data[:] = _tmp['final_layer_norm.bias']
model.embed_out.weight.data[:] = _tmp['embed_out.weight']
if 'embed_out.bias' in _tmp:
model.embed_out.bias.data[:] = _tmp['embed_out.bias']
return model
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Convert HF checkpoints')
parser.add_argument('--config-name', type=str, default='EleutherAI/gpt-neox-20b',
help='config-name')
parser.add_argument('--ckpt-path', type=str, default=None,
help='ckpt-path')
parser.add_argument('--save-path', type=str, default=None,
help='save-path')
parser.add_argument('--n-stages', type=int, default=8,
help='pipeline group size')
parser.add_argument('--n-layer-per-stage', type=int, default=6,
help='n layers per GPU device')
parser.add_argument('--fp16', default=False, action='store_true')
args = parser.parse_args()
assert args.ckpt_path is not None
assert args.save_path is not None
if not os.path.exists(args.save_path):
os.mkdir(args.save_path)
print('loading config...')
config = AutoConfig.from_pretrained(args.config_name)
print('loaded config.')
print('loading tokenizer...')
tokenizer = AutoTokenizer.from_pretrained(args.config_name)
print('loaded tokenizer.')
print('creating empty model...')
model = create_empty_gptneox(config)
if args.fp16:
model = model.half()
print('created empty model.')
print('loading model ckpt...')
load_decentralized_checkpoint(
model, args.ckpt_path, n_stages=args.n_stages, n_layer_per_stage=args.n_layer_per_stage,
)
print('loaded model ckpt.')
print('saving HF model...')
model.save_pretrained(args.save_path)
print(f'saved HF model to `{args.save_path}`')
config.save_pretrained(args.save_path)
tokenizer.save_pretrained(args.save_path)