import copy
from colossalai.shardformer import ShardConfig, ShardFormer
def build_model(
model_fn,
enable_fused_normalization=False,
enable_tensor_parallelism=False,
enable_flash_attention=False,
enable_jit_fused=False,
):
# create new model
org_model = model_fn()
# shard model
shard_config = ShardConfig(
enable_fused_normalization=enable_fused_normalization,
enable_tensor_parallelism=enable_tensor_parallelism,
enable_flash_attention=enable_flash_attention,
enable_jit_fused=enable_jit_fused,
inference_only=True,
)
model_copy = copy.deepcopy(org_model)
shard_former = ShardFormer(shard_config=shard_config)
sharded_model, shared_params = shard_former.optimize(model_copy)
return org_model.cuda(), sharded_model.cuda()
def run_infer(original_model, sharded_model, data_gen_fn, output_transform_fn):
# prepare input
data = data_gen_fn()
data = {k: v.cuda() for k, v in data.items()}
# run forward
org_output = original_model(**data)
org_output = output_transform_fn(org_output)
shard_output = sharded_model(**data)
shard_output = output_transform_fn(shard_output)
return org_output, shard_output
