test_moe_zero_init.py

from functools import partial

import pytest
import torch
import torch.multiprocessing as mp
import torch.nn as nn

import colossalai
from colossalai.context import MOE_CONTEXT
from colossalai.logging import get_dist_logger
from colossalai.nn import CheckpointModule
from colossalai.nn.layer import MoeModule
from colossalai.testing import parameterize, rerun_if_address_is_in_use
from colossalai.utils import free_port, get_current_device
from colossalai.zero.legacy.init_ctx import ZeroInitContext
from colossalai.zero.legacy.shard_utils import BucketTensorShardStrategy, TensorShardStrategy
from tests.test_zero.common import CONFIG


class MoeModel(nn.Module):

    def __init__(self, checkpoint: bool = False):

        class TestSubModule(CheckpointModule):

            def __init__(self):
                super().__init__(checkpoint)
                expert_cls = nn.Linear
                expert_args_dict = dict(in_features=16, out_features=16)
                self.moe = MoeModule(dim_model=16,
                                     num_experts=8,
                                     use_residual=True,
                                     expert_cls=expert_cls,
                                     **expert_args_dict)
                self.proj = nn.Linear(16, 4)

            def _forward(self, x):
                x, y = self.moe(x)
                x = self.proj(x)
                return x, y

        super().__init__()
        self.test_embed = nn.Linear(4, 16)
        self.test_transform = TestSubModule()

    def forward(self, x):
        MOE_CONTEXT.reset_loss()

        x = self.test_embed(x)
        x, y = self.test_transform(x)

        MOE_CONTEXT.add_loss(y)
        return x


@parameterize("init_device_type", ['cpu', 'cuda'])
@parameterize("shard_strategy_class", [TensorShardStrategy, BucketTensorShardStrategy])
def run_moe_zero_init(init_device_type, shard_strategy_class):
    logger = get_dist_logger("test_moe_zero_init")

    if init_device_type == 'cuda':
        init_device = get_current_device()
    elif init_device_type == 'cpu':
        init_device = torch.device("cpu")
    else:
        raise NotImplementedError("Unknown device found.")

    model_numel_tensor = torch.zeros(1, dtype=torch.int)
    with ZeroInitContext(target_device=init_device,
                         shard_strategy=shard_strategy_class(),
                         shard_param=True,
                         model_numel_tensor=model_numel_tensor):
        model = MoeModel(checkpoint=True)

    for name, param in model.named_parameters():
        assert hasattr(param, 'colo_attr')

        # the parameters in moe experts and its gate should not be sharded
        if ('experts' in name) or ('gate' in name) or ('residual_combine' in name):
            assert not param.colo_attr.sharded_data_tensor.is_sharded, "`{}` parameter has problem".format(name)
        else:
            assert param.colo_attr.sharded_data_tensor.is_sharded

        # the parameters in moe experts is not replicated
        if 'experts' in name:
            assert not param.colo_attr.is_replicated
        else:
            assert param.colo_attr.is_replicated

        if param.colo_attr.param_is_sharded:
            assert param.colo_attr.data_payload.device.type == init_device.type, \
                f'{param.colo_attr.data_payload.device.type} vs. {init_device.type}'
        else:
            assert param.colo_attr.data_payload.device.type == 'cuda'


def _run_dist(rank, world_size, port):
    colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
    MOE_CONTEXT.setup(seed=42)
    run_moe_zero_init()


@pytest.mark.dist
@pytest.mark.parametrize("world_size", [2, 4])
@rerun_if_address_is_in_use()
def test_moe_zero_init(world_size):
    run_func = partial(_run_dist, world_size=world_size, port=free_port())
    mp.spawn(run_func, nprocs=world_size)


if __name__ == '__main__':
    test_moe_zero_init(world_size=2)