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.
81 lines
2.9 KiB
81 lines
2.9 KiB
import pytest
|
|
import torch
|
|
import torch.distributed as dist
|
|
from utils import shared_tempdir
|
|
|
|
import colossalai
|
|
from colossalai.booster import Booster
|
|
from colossalai.booster.plugin import GeminiPlugin, LowLevelZeroPlugin, TorchDDPPlugin
|
|
from colossalai.nn.optimizer import HybridAdam
|
|
from colossalai.testing import (
|
|
check_state_dict_equal,
|
|
clear_cache_before_run,
|
|
parameterize,
|
|
rerun_if_address_is_in_use,
|
|
spawn,
|
|
)
|
|
from tests.kit.model_zoo import model_zoo
|
|
|
|
|
|
@clear_cache_before_run()
|
|
@parameterize("model_name", ["transformers_gpt"])
|
|
@parameterize("plugin_type", ["ddp", "zero", "gemini"])
|
|
def exam_from_pretrained(plugin_type: str, model_name: str, shard=True, size_per_shard=32):
|
|
(model_fn, data_gen_fn, output_transform_fn, loss_fn, _) = next(
|
|
iter(model_zoo.get_sub_registry(model_name).values())
|
|
)
|
|
criterion = loss_fn
|
|
|
|
if plugin_type == "ddp":
|
|
plugin = TorchDDPPlugin()
|
|
elif plugin_type == "zero":
|
|
plugin = LowLevelZeroPlugin(stage=2, max_norm=1.0, initial_scale=32)
|
|
elif plugin_type == "gemini":
|
|
plugin = GeminiPlugin(precision="fp16", initial_scale=32)
|
|
else:
|
|
raise ValueError(f"Plugin with type {plugin_type} is invalid, please check your argument.")
|
|
|
|
booster = Booster(plugin=plugin)
|
|
|
|
model = model_fn().cuda()
|
|
model_huggingface_cls = model.__class__
|
|
optimizer = HybridAdam(model.parameters(), lr=0.001)
|
|
model, optimizer, criterion, _, _ = booster.boost(model, optimizer, criterion)
|
|
|
|
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 = model(**data)
|
|
loss = criterion(output)
|
|
|
|
booster.backward(loss, optimizer)
|
|
optimizer.step()
|
|
|
|
with shared_tempdir() as tempdir:
|
|
model_ckpt_path = f"{tempdir}/model"
|
|
booster.save_model(model, model_ckpt_path, shard=shard, size_per_shard=size_per_shard)
|
|
dist.barrier()
|
|
|
|
new_model = model_huggingface_cls.from_pretrained(model_ckpt_path)
|
|
new_model = new_model.cuda()
|
|
new_optimizer = HybridAdam(new_model.parameters(), lr=0.001)
|
|
new_model, new_optimizer, criterion, _, _ = booster.boost(new_model, new_optimizer, criterion)
|
|
|
|
if plugin_type == "gemini":
|
|
check_state_dict_equal(model.state_dict(only_rank_0=False), new_model.state_dict(only_rank_0=False), False)
|
|
else:
|
|
check_state_dict_equal(model.unwrap().state_dict(), new_model.unwrap().state_dict(), False)
|
|
dist.barrier()
|
|
|
|
|
|
def run_dist(rank, world_size, port):
|
|
config = {}
|
|
colossalai.launch(config=config, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
|
|
exam_from_pretrained()
|
|
|
|
|
|
@pytest.mark.dist
|
|
@pytest.mark.parametrize("world_size", [2])
|
|
@rerun_if_address_is_in_use()
|
|
def test_huggingface_compatibility(world_size):
|
|
spawn(run_dist, world_size)
|