mirror of https://github.com/hpcaitech/ColossalAI
103 lines
3.9 KiB
Python
103 lines
3.9 KiB
Python
from functools import partial
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import colossalai
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import pytest
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import torch
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import torch.multiprocessing as mp
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import torch.nn as nn
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from colossalai.logging import get_dist_logger
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from colossalai.testing import parameterize
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from colossalai.utils import free_port
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from colossalai.context import MOE_CONTEXT
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from colossalai.nn.layer import MoeModule
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from colossalai.zero.init_ctx import ZeroInitContext
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from colossalai.zero.shard_utils import (BucketTensorShardStrategy, TensorShardStrategy)
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from colossalai.testing import rerun_on_exception
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from colossalai.utils import get_current_device
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from tests.test_zero_data_parallel.common import CONFIG
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class MoeModel(nn.Module):
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def __init__(self):
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super().__init__()
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self.proj1 = nn.Linear(4, 16)
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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(dim_model=16, num_experts=8, use_residual=True, expert_cls=expert_cls, **expert_args_dict)
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self.proj2 = nn.Linear(16, 4)
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def forward(self, x):
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x = self.proj1(x)
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x = self.moe(x)
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x = self.proj2(x)
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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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logger = get_dist_logger("test_moe_zero_init")
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if init_device_type == 'cuda':
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init_device = torch.device(f"cuda:{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(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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rm_torch_payload_on_the_fly=False):
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model = MoeModel()
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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 weights in the gate should be fp32
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if 'gate' in name:
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assert param.colo_attr.sharded_data_tensor.dtype == torch.float32
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else:
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assert param.colo_attr.sharded_data_tensor.dtype == torch.half
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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
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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.is_replicated
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else:
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assert param.is_replicated
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if param.colo_attr.param_is_sharded:
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assert param.colo_attr.sharded_data_tensor.payload.device.type == init_device.type, \
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f'{param.colo_attr.sharded_data_tensor.payload.device.type} vs. {init_device.type}'
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else:
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assert param.colo_attr.sharded_data_tensor.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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@pytest.mark.skip("Under development")
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@rerun_on_exception(exception_type=mp.ProcessRaisedException, pattern=".*Address already in use.*")
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def test_moe_zero_init(world_size):
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run_func = partial(_run_dist, world_size=world_size, port=free_port())
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mp.spawn(run_func, nprocs=world_size)
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if __name__ == '__main__':
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test_moe_zero_init(world_size=2)
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