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.
139 lines
3.8 KiB
139 lines
3.8 KiB
import importlib
|
|
import os
|
|
import shutil
|
|
import sys
|
|
|
|
import pytest
|
|
import torch
|
|
import torch.distributed as dist
|
|
from transformers.models.llama import LlamaConfig
|
|
|
|
import colossalai
|
|
from colossalai.booster import Booster
|
|
from colossalai.booster.plugin.moe_hybrid_parallel_plugin import MoeHybridParallelPlugin
|
|
from colossalai.moe.manager import MOE_MANAGER
|
|
from colossalai.testing import rerun_if_address_is_in_use, spawn
|
|
|
|
sys.path.append(os.path.join(
|
|
os.path.dirname(os.path.dirname(os.path.dirname(__file__))),
|
|
"examples/language/openmoe",
|
|
))
|
|
|
|
OpenMoeForCausalLM = importlib.import_module("model.modeling_openmoe").OpenMoeForCausalLM
|
|
set_openmoe_args = importlib.import_module("model.modeling_openmoe").set_openmoe_args
|
|
OpenMoeForCausalLMPolicy = importlib.import_module("model.openmoe_policy").OpenMoeForCausalLMPolicy
|
|
|
|
|
|
def get_config():
|
|
config = LlamaConfig(
|
|
vocab_size=300,
|
|
hidden_size=16,
|
|
intermediate_size=32,
|
|
num_hidden_layers=4,
|
|
num_attention_heads=2,
|
|
head_dim=4,
|
|
dropout_rate=0.0,
|
|
hidden_act="swiglu",
|
|
)
|
|
set_openmoe_args(config, num_experts=16, moe_layer_interval=1)
|
|
return config
|
|
|
|
|
|
def get_model(parallel):
|
|
config = get_config()
|
|
model = OpenMoeForCausalLM(config)
|
|
|
|
if parallel == None:
|
|
plugin = MoeHybridParallelPlugin(
|
|
tp_size=1,
|
|
pp_size=1,
|
|
zero_stage=0,
|
|
custom_policy=OpenMoeForCausalLMPolicy(),
|
|
)
|
|
elif parallel == "zero_ep":
|
|
plugin = MoeHybridParallelPlugin(
|
|
tp_size=1,
|
|
pp_size=1,
|
|
zero_stage=2,
|
|
custom_policy=OpenMoeForCausalLMPolicy(),
|
|
)
|
|
elif parallel == "hybrid":
|
|
plugin = MoeHybridParallelPlugin(
|
|
tp_size=1,
|
|
pp_size=2,
|
|
zero_stage=1,
|
|
microbatch_size=1,
|
|
custom_policy=OpenMoeForCausalLMPolicy(),
|
|
)
|
|
booster = Booster(plugin=plugin)
|
|
model, _, _, _, _ = booster.boost(model=model)
|
|
return model, booster
|
|
|
|
|
|
def _test_moe_checkpoint(parallel, shard):
|
|
if parallel == None:
|
|
MOE_MANAGER.setup(
|
|
seed=42,
|
|
parallel=None,
|
|
)
|
|
elif parallel == "zero2_ep":
|
|
MOE_MANAGER.setup(
|
|
seed=42,
|
|
parallel="EP",
|
|
)
|
|
elif parallel == "hybrid":
|
|
MOE_MANAGER.setup(
|
|
seed=42,
|
|
parallel="EP",
|
|
mode="fixed",
|
|
fixed_dp_size=1,
|
|
fixed_ep_size=2,
|
|
fixed_pp_size=2,
|
|
)
|
|
model1, booster1 = get_model(parallel)
|
|
model2, booster2 = get_model(parallel)
|
|
|
|
if shard:
|
|
booster1.save_model(model1, "./tmp_ckpt", shard=True, size_per_shard=1)
|
|
booster2.load_model(model2, "./tmp_ckpt")
|
|
else:
|
|
booster1.save_model(model1, "tmp_ckpt.pth")
|
|
booster2.load_model(model2, "tmp_ckpt.pth")
|
|
|
|
state1 = model1.state_dict()
|
|
state2 = model2.state_dict()
|
|
for k, v in state1.items():
|
|
u = state2.get(k)
|
|
assert torch.equal(u.data, v.data)
|
|
|
|
if dist.get_rank() == 0:
|
|
if shard:
|
|
shutil.rmtree("./tmp_ckpt")
|
|
else:
|
|
os.remove("tmp_ckpt.pth")
|
|
|
|
|
|
def _run_dist(rank, world_size, port, parallel, shard):
|
|
colossalai.launch(
|
|
config=dict(),
|
|
rank=rank,
|
|
world_size=world_size,
|
|
host="localhost",
|
|
port=port,
|
|
backend="nccl",
|
|
)
|
|
_test_moe_checkpoint(parallel, shard)
|
|
|
|
|
|
@pytest.mark.dist
|
|
@pytest.mark.parametrize("world_size", [4])
|
|
@pytest.mark.parametrize("parallel", [None, "zero_ep", "hybrid"])
|
|
@pytest.mark.parametrize("shard", [True, False])
|
|
@rerun_if_address_is_in_use()
|
|
def test_moe_checkpoint(world_size, parallel, shard):
|
|
spawn(_run_dist, world_size, parallel=parallel, shard=shard)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
test_moe_checkpoint(world_size=4, parallel="hybrid", shard=True)
|