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aibig-modeldata-parallelismdeep-learningdistributed-computingfoundation-modelsheterogeneous-traininghpcinferencelarge-scalemodel-parallelismpipeline-parallelism
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56 lines
1.8 KiB
56 lines
1.8 KiB
import pytest |
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import torch |
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import torch.distributed as dist |
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import colossalai |
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from colossalai.context import MOE_CONTEXT |
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from colossalai.tensor import ColoParameter |
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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 import ColoInitContext |
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from tests.test_moe.test_moe_zero_init import MoeModel |
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from tests.test_tensor.common_utils import debug_print |
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from tests.test_zero.test_legacy.common import CONFIG |
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@parameterize("init_device_type", ['cpu', 'cuda']) |
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def exam_moe_colo_init(init_device_type): |
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world_size = dist.get_world_size() |
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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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with ColoInitContext(device=init_device): |
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model = MoeModel(checkpoint=True) |
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for name, param in model.named_parameters(): |
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assert isinstance(param, ColoParameter), "parameter `{}` has an init problem".format(name) |
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if hasattr(param, "moe_info"): |
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param.set_process_group(param.moe_info.pg) |
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if hasattr(param, "moe_info"): |
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assert param.process_group.dp_world_size() == param.moe_info.dp_size |
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else: |
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assert param.process_group.dp_world_size() == world_size |
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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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exam_moe_colo_init() |
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@pytest.mark.dist |
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@pytest.mark.parametrize("world_size", [4]) |
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@rerun_if_address_is_in_use() |
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def test_moe_colo_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_colo_init(world_size=4)
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