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107 lines
3.6 KiB
107 lines
3.6 KiB
import pytest
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import torch
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import torch.nn as nn
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import colossalai
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from colossalai.context import MOE_CONTEXT
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from colossalai.logging import get_dist_logger
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from colossalai.nn import CheckpointModule
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from colossalai.nn.layer import MoeModule
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from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
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from colossalai.utils import get_current_device
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from colossalai.zero.legacy.init_ctx import ZeroInitContext
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from colossalai.zero.legacy.shard_utils import BucketTensorShardStrategy, TensorShardStrategy
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from tests.test_zero.test_legacy.common import CONFIG
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class MoeModel(nn.Module):
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def __init__(self, checkpoint: bool = False):
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class TestSubModule(CheckpointModule):
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def __init__(self):
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super().__init__(checkpoint)
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expert_cls = nn.Linear
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expert_args_dict = dict(in_features=16, out_features=16)
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self.moe = MoeModule(
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dim_model=16, num_experts=8, use_residual=True, expert_cls=expert_cls, **expert_args_dict
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)
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self.proj = nn.Linear(16, 4)
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def _forward(self, x):
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x, y = self.moe(x)
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x = self.proj(x)
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return x, y
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super().__init__()
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self.test_embed = nn.Linear(4, 16)
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self.test_transform = TestSubModule()
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def forward(self, x):
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MOE_CONTEXT.reset_loss()
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x = self.test_embed(x)
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x, y = self.test_transform(x)
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MOE_CONTEXT.add_loss(y)
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return x
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@parameterize("init_device_type", ["cpu", "cuda"])
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@parameterize("shard_strategy_class", [TensorShardStrategy, BucketTensorShardStrategy])
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def run_moe_zero_init(init_device_type, shard_strategy_class):
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get_dist_logger("test_moe_zero_init")
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if init_device_type == "cuda":
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init_device = get_current_device()
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elif init_device_type == "cpu":
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init_device = torch.device("cpu")
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else:
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raise NotImplementedError("Unknown device found.")
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model_numel_tensor = torch.zeros(1, dtype=torch.int)
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with ZeroInitContext(
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target_device=init_device,
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shard_strategy=shard_strategy_class(),
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shard_param=True,
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model_numel_tensor=model_numel_tensor,
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):
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model = MoeModel(checkpoint=True)
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for name, param in model.named_parameters():
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assert hasattr(param, "colo_attr")
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# the parameters in moe experts and its gate should not be sharded
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if ("experts" in name) or ("gate" in name) or ("residual_combine" in name):
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assert not param.colo_attr.sharded_data_tensor.is_sharded, "`{}` parameter has problem".format(name)
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else:
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assert param.colo_attr.sharded_data_tensor.is_sharded
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# the parameters in moe experts is not replicated
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if "experts" in name:
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assert not param.colo_attr.is_replicated
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else:
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assert param.colo_attr.is_replicated
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if param.colo_attr.param_is_sharded:
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assert (
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param.colo_attr.data_payload.device.type == init_device.type
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), f"{param.colo_attr.data_payload.device.type} vs. {init_device.type}"
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else:
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assert param.colo_attr.data_payload.device.type == "cuda"
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def _run_dist(rank, world_size, port):
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colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
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MOE_CONTEXT.setup(seed=42)
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run_moe_zero_init()
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@pytest.mark.dist
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@pytest.mark.parametrize("world_size", [2, 4])
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@rerun_if_address_is_in_use()
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def test_moe_zero_init(world_size):
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spawn(_run_dist, world_size)
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if __name__ == "__main__":
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test_moe_zero_init(world_size=2)
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