mirror of https://github.com/hpcaitech/ColossalAI
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.
106 lines
3.8 KiB
106 lines
3.8 KiB
import copy
|
|
import os
|
|
from itertools import product
|
|
|
|
import torch
|
|
from peft import LoraConfig
|
|
from torch import distributed as dist
|
|
from torch.optim import AdamW
|
|
|
|
import colossalai
|
|
from colossalai.booster import Booster
|
|
from colossalai.booster.plugin import LowLevelZeroPlugin, TorchDDPPlugin
|
|
from colossalai.testing import check_state_dict_equal, clear_cache_before_run, rerun_if_address_is_in_use, spawn
|
|
from tests.kit.model_zoo import model_zoo
|
|
from tests.test_checkpoint_io.utils import shared_tempdir
|
|
|
|
|
|
@clear_cache_before_run()
|
|
def check_fwd_bwd(model_fn, data_gen_fn, output_transform_fn, loss_fn, task_type):
|
|
model = model_fn()
|
|
lora_config = LoraConfig(task_type=task_type, r=8, lora_alpha=32, lora_dropout=0.1)
|
|
|
|
test_plugins = [TorchDDPPlugin(), LowLevelZeroPlugin()]
|
|
test_configs = [
|
|
{
|
|
"lora_config": lora_config,
|
|
"quantize": False,
|
|
},
|
|
{
|
|
"lora_config": lora_config,
|
|
"quantize": True,
|
|
},
|
|
]
|
|
for plugin, test_config in product(test_plugins, test_configs):
|
|
# checkpoint loaded model
|
|
model_save = model_fn()
|
|
model_load = copy.deepcopy(model_save)
|
|
|
|
optimizer = AdamW(model.parameters(), lr=0.001)
|
|
criterion = loss_fn
|
|
|
|
booster = Booster(plugin=plugin)
|
|
model_save = booster.enable_lora(model_save, **test_config)
|
|
model_save, optimizer, criterion, _, _ = booster.boost(model_save, optimizer, criterion)
|
|
|
|
with shared_tempdir() as tempdir:
|
|
lora_ckpt_path = os.path.join(tempdir, "ckpt")
|
|
booster.save_lora_as_pretrained(model_save, lora_ckpt_path)
|
|
dist.barrier()
|
|
|
|
# The Lora checkpoint should be small in size
|
|
checkpoint_size_mb = os.path.getsize(os.path.join(lora_ckpt_path, "adapter_model.bin")) / (1024 * 1024)
|
|
assert checkpoint_size_mb < 1
|
|
|
|
model_load = booster.enable_lora(model_load, pretrained_dir=lora_ckpt_path, **test_config)
|
|
model_load, _, _, _, _ = booster.boost(model_load)
|
|
|
|
check_state_dict_equal(model_save.state_dict(), model_load.state_dict())
|
|
|
|
# test fwd bwd correctness
|
|
test_model = model_load
|
|
model_copy = copy.deepcopy(model_load)
|
|
|
|
data = data_gen_fn()
|
|
data = {
|
|
k: v.to("cuda") if torch.is_tensor(v) or "Tensor" in v.__class__.__name__ else v for k, v in data.items()
|
|
}
|
|
|
|
output = test_model(**data)
|
|
output = output_transform_fn(output)
|
|
loss = criterion(output)
|
|
|
|
booster.backward(loss, optimizer)
|
|
optimizer.clip_grad_by_norm(1.0)
|
|
optimizer.step()
|
|
|
|
for (n1, p1), (n2, p2) in zip(test_model.named_parameters(), model_copy.named_parameters()):
|
|
if "lora_" in n1:
|
|
# lora modules require gradients, thus updated
|
|
assert p1.requires_grad
|
|
assert not torch.testing.assert_close(p1.to(p2.device).to(p2.dtype), p2, atol=5e-3, rtol=5e-3)
|
|
else:
|
|
if not p1.requires_grad:
|
|
torch.testing.assert_close(p1.to(p2.device).to(p2.dtype), p2, atol=5e-3, rtol=5e-3)
|
|
|
|
|
|
def run_lora_test():
|
|
sub_model_zoo = model_zoo.get_sub_registry("transformers_llama")
|
|
for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
|
|
task_type = None
|
|
if name == "transformers_llama_for_casual_lm":
|
|
task_type = "CAUSAL_LM"
|
|
if name == "transformers_llama_for_sequence_classification":
|
|
task_type = "SEQ_CLS"
|
|
check_fwd_bwd(model_fn, data_gen_fn, output_transform_fn, loss_fn, task_type)
|
|
|
|
|
|
def run_dist(rank, world_size, port):
|
|
colossalai.launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
|
|
run_lora_test()
|
|
|
|
|
|
@rerun_if_address_is_in_use()
|
|
def test_torch_ddp_lora():
|
|
spawn(run_dist, 2)
|