ColossalAI/tests/test_moe/test_moe_zero_init.py

import pytest
import torch
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, spawn
from colossalai.utils import get_current_device
from colossalai.zero.legacy.init_ctx import ZeroInitContext
from colossalai.zero.legacy.shard_utils import BucketTensorShardStrategy, TensorShardStrategy
from tests.test_zero.test_legacy.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):
    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):
    spawn(_run_dist, world_size)


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