mirror of https://github.com/hpcaitech/ColossalAI
aibig-modeldata-parallelismdeep-learningdistributed-computingfoundation-modelsheterogeneous-traininghpcinferencelarge-scalemodel-parallelismpipeline-parallelism
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108 lines
3.8 KiB
108 lines
3.8 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(dim_model=16, |
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num_experts=8, |
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use_residual=True, |
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expert_cls=expert_cls, |
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**expert_args_dict) |
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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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logger = 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(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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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 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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