mirror of https://github.com/hpcaitech/ColossalAI
39 lines
1.1 KiB
Python
39 lines
1.1 KiB
Python
|
import copy
|
||
|
|
|
||
|
|
from colossalai.shardformer import ShardConfig, ShardFormer
|
||
|
|
|
||
|
|
|
||
|
|
def build_model(world_size, model_fn):
|
||
|
|
# create new model
|
||
|
|
org_model = model_fn().cuda()
|
||
|
|
|
||
|
|
# shard model
|
||
|
|
shard_config = ShardConfig(tensor_parallel_size=world_size)
|
||
|
|
model_copy = copy.deepcopy(org_model)
|
||
|
|
shard_former = ShardFormer(shard_config=shard_config)
|
||
|
|
shard_former.init_distributed()
|
||
|
|
sharded_model = shard_former.shard_model(model_copy)
|
||
|
|
|
||
|
|
return org_model, sharded_model
|
||
|
|
|
||
|
|
|
||
|
|
def run_forward(original_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn):
|
||
|
|
# prepare input
|
||
|
|
data = data_gen_fn()
|
||
|
|
data = {k: v.cuda() for k, v in data.items()}
|
||
|
|
|
||
|
|
# switch to train mode
|
||
|
|
original_model.train()
|
||
|
|
sharded_model.train()
|
||
|
|
|
||
|
|
# run forward
|
||
|
|
org_output = original_model(**data)
|
||
|
|
org_output = output_transform_fn(org_output)
|
||
|
|
org_loss = loss_fn(org_output)
|
||
|
|
|
||
|
|
shard_output = sharded_model(**data)
|
||
|
|
shard_output = output_transform_fn(shard_output)
|
||
|
|
shard_loss = loss_fn(shard_output)
|
||
|
|
|
||
|
|
return org_output, org_loss, shard_output, shard_loss
|