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.
120 lines
3.8 KiB
120 lines
3.8 KiB
from copy import deepcopy
|
|
|
|
import pytest
|
|
import torch
|
|
import torch.distributed as dist
|
|
from torch.nn.parallel import DistributedDataParallel as DDP
|
|
from transformers.models.mixtral.configuration_mixtral import MixtralConfig
|
|
from transformers.models.mixtral.modeling_mixtral import MixtralModel
|
|
|
|
import colossalai
|
|
from colossalai.booster.booster import Booster
|
|
from colossalai.booster.plugin.moe_hybrid_parallel_plugin import MoeHybridParallelPlugin
|
|
from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
|
|
from colossalai.testing.random import seed_all
|
|
from tests.test_moe.moe_utils import assert_loose_close
|
|
|
|
NUM_BATCH = 4
|
|
NUM_TOK_PER_BATCH, NUM_EXPERTS = 7, 4
|
|
HIDDEN_SIZE_PER_HEAD = 4
|
|
NUM_HEADS = 2
|
|
TOP_K = 1
|
|
|
|
|
|
@parameterize("stage", [1])
|
|
@parameterize("ep_size", [2, 4])
|
|
def run_zero_with_original_model(stage: int, ep_size: int):
|
|
dtype = torch.bfloat16
|
|
|
|
rank = torch.distributed.get_rank()
|
|
torch.cuda.set_device(dist.get_rank())
|
|
|
|
plugin = MoeHybridParallelPlugin(
|
|
pp_size=1, tp_size=1, ep_size=ep_size, zero_stage=stage, overlap_communication=False, initial_scale=1
|
|
)
|
|
booster = Booster(plugin=plugin)
|
|
|
|
seed_all(10086)
|
|
|
|
config = MixtralConfig(
|
|
hidden_size=HIDDEN_SIZE_PER_HEAD * NUM_HEADS,
|
|
intermediate_size=HIDDEN_SIZE_PER_HEAD * NUM_HEADS * 2,
|
|
num_hidden_layers=2,
|
|
num_attention_heads=NUM_HEADS,
|
|
num_key_value_heads=NUM_HEADS,
|
|
num_local_experts=NUM_EXPERTS,
|
|
num_experts_per_tok=TOP_K,
|
|
)
|
|
|
|
torch_model = MixtralModel(config).to(dtype).cuda()
|
|
|
|
zero_model = deepcopy(torch_model).to(dtype)
|
|
zero_optimizer = torch.optim.SGD(zero_model.parameters(), lr=1)
|
|
|
|
zero_model, zero_optimizer, _, _, _ = booster.boost(zero_model, zero_optimizer)
|
|
|
|
ddp_model = DDP(
|
|
torch_model.cuda(),
|
|
process_group=plugin.dp_group,
|
|
find_unused_parameters=True, # important for torch ddp, not all experts are routed
|
|
).cuda()
|
|
ddp_optimizer = torch.optim.SGD(ddp_model.parameters(), lr=1)
|
|
|
|
# create different input
|
|
seed_all(1453 + rank)
|
|
|
|
ddp_model.train()
|
|
zero_model.train()
|
|
for _ in range(2):
|
|
# zero-dp forward
|
|
input_data = torch.rand(
|
|
NUM_BATCH, NUM_TOK_PER_BATCH, HIDDEN_SIZE_PER_HEAD * NUM_HEADS, requires_grad=True
|
|
).cuda()
|
|
zero_output = zero_model(inputs_embeds=input_data.to(dtype)).last_hidden_state.mean()
|
|
# zero-dp backward
|
|
zero_optimizer.backward(zero_output)
|
|
|
|
# torch-ddp forward
|
|
ddp_output = ddp_model(inputs_embeds=input_data.to(dtype)).last_hidden_state.mean()
|
|
assert_loose_close(zero_output, ddp_output, dtype=dtype)
|
|
# torch-ddp backward
|
|
ddp_output.backward()
|
|
|
|
# check grad
|
|
name_to_p = {n: p for n, p in ddp_model.named_parameters()}
|
|
for n, p in zero_model.named_parameters():
|
|
zero_grad = zero_optimizer.get_param_grad(p)
|
|
if name_to_p[n].grad is None:
|
|
name_to_p[n].grad = torch.zeros_like(name_to_p[n].data)
|
|
continue
|
|
assert_loose_close(zero_grad, name_to_p[n].grad, dtype=dtype, name=n)
|
|
|
|
# zero-dp step
|
|
zero_optimizer.step()
|
|
|
|
# original model step
|
|
ddp_optimizer.step()
|
|
|
|
# check updated param
|
|
for n, p in zero_model.named_parameters():
|
|
assert_loose_close(p.data, name_to_p[n].data, dtype=dtype, name=n)
|
|
|
|
print(f"{dist.get_rank()} test passed")
|
|
|
|
|
|
def run_dist(rank, world_size, port):
|
|
colossalai.launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
|
|
run_zero_with_original_model()
|
|
|
|
|
|
@pytest.mark.skip("tested in corresponding sharderformer")
|
|
@pytest.mark.dist
|
|
@pytest.mark.parametrize("world_size", [4])
|
|
@rerun_if_address_is_in_use()
|
|
def test_moe_ep_zero(world_size):
|
|
spawn(run_dist, world_size)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
test_moe_ep_zero(world_size=4)
|