mirror of https://github.com/hpcaitech/ColossalAI
aibig-modeldata-parallelismdeep-learningdistributed-computingfoundation-modelsheterogeneous-traininghpcinferencelarge-scalemodel-parallelismpipeline-parallelism
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
32 lines
1.2 KiB
32 lines
1.2 KiB
1 year ago
|
import os
|
||
|
|
|
||
|
|
from transformers import BertForPreTraining, LlamaForCausalLM
|
||
|
|
|
||
|
|
import colossalai.interface.pretrained as pretrained_utils
|
||
|
|
from colossalai.lazy import LazyInitContext
|
||
|
|
|
||
|
|
|
||
|
|
def test_lazy_from_pretrained():
|
||
|
|
# test from cached file, unsharded
|
||
|
|
model = BertForPreTraining.from_pretrained("prajjwal1/bert-tiny")
|
||
|
|
with LazyInitContext():
|
||
|
|
deffered_model = BertForPreTraining.from_pretrained("prajjwal1/bert-tiny")
|
||
|
|
pretrained_path = pretrained_utils.get_pretrained_path(deffered_model)
|
||
|
|
assert os.path.isfile(pretrained_path)
|
||
|
|
for p, lazy_p in zip(model.parameters(), deffered_model.parameters()):
|
||
|
|
assert p.shape == lazy_p.shape
|
||
|
|
|
||
|
|
# test from local file, sharded
|
||
|
|
llama_path = os.environ["LLAMA_PATH"]
|
||
|
|
model = LlamaForCausalLM.from_pretrained(llama_path)
|
||
|
|
with LazyInitContext():
|
||
|
|
deffered_model = LlamaForCausalLM.from_pretrained(llama_path)
|
||
|
|
pretrained_path = pretrained_utils.get_pretrained_path(deffered_model)
|
||
|
|
assert os.path.isfile(pretrained_path)
|
||
|
|
for p, lazy_p in zip(model.parameters(), deffered_model.parameters()):
|
||
|
|
assert p.shape == lazy_p.shape
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
test_lazy_from_pretrained()
|