mirror of https://github.com/hpcaitech/ColossalAI
aibig-modeldata-parallelismdeep-learningdistributed-computingfoundation-modelsheterogeneous-traininghpcinferencelarge-scalemodel-parallelismpipeline-parallelism
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67 lines
2.1 KiB
67 lines
2.1 KiB
from collections import OrderedDict |
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import pytest |
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import torch |
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import colossalai |
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from colossalai.nn.parallel import ColoDDP |
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from colossalai.tensor import ColoParameter, ProcessGroup |
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from colossalai.testing import rerun_if_address_is_in_use, spawn |
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from colossalai.utils.cuda import get_current_device |
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from colossalai.zero import ColoInitContext |
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from tests.components_to_test.registry import non_distributed_component_funcs |
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def check_state_dict_equal(state_dict: OrderedDict, other_state_dict: OrderedDict): |
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for (k1, t1), (k2, t2) in zip(state_dict.items(), other_state_dict.items()): |
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assert k1 == k2 |
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if t1.device != t2.device: |
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temp_t2 = t2.to(t1.device) |
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else: |
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temp_t2 = t2 |
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assert torch.equal(t1, temp_t2), "\t{}\n\t{}".format(t1, temp_t2) |
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def init_ddp(module: torch.nn.Module) -> ColoDDP: |
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pg = ProcessGroup() |
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return ColoDDP(module, process_group=pg) |
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def run_ddp_state_dict(): |
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get_components_func = non_distributed_component_funcs.get_callable('gpt2') |
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model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func() |
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torch_model = model_builder().cuda() |
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with ColoInitContext(device=get_current_device()): |
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model = model_builder() |
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model = init_ddp(model) |
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torch_state_dict = torch_model.state_dict() |
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for param in model.parameters(): |
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if isinstance(param, ColoParameter): |
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assert param.get_process_group() is not None |
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model.load_state_dict(torch_state_dict) |
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for param in model.parameters(): |
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if isinstance(param, ColoParameter): |
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assert param.get_process_group() is not None |
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state_dict = model.state_dict() |
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check_state_dict_equal(torch_state_dict, state_dict) |
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def run_dist(rank, world_size, port): |
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colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') |
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run_ddp_state_dict() |
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@pytest.mark.dist |
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@pytest.mark.parametrize('world_size', [1, 2]) |
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@rerun_if_address_is_in_use() |
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def test_state_dict(world_size): |
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spawn(run_dist, world_size) |
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if __name__ == '__main__': |
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test_state_dict(2)
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