mirror of https://github.com/hpcaitech/ColossalAI
78 lines
2.5 KiB
Python
78 lines
2.5 KiB
Python
from tests.components_to_test.registry import non_distributed_component_funcs
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import colossalai
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import pytest
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import torch.multiprocessing as mp
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from colossalai.testing import parameterize, rerun_if_address_is_in_use
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from colossalai.utils.cuda import get_current_device
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from colossalai.utils import free_port
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from colossalai.utils import ColoInitContext
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from colossalai.tensor import named_params_with_colotensor, TensorSpec, ComputePattern, ParallelAction, ColoTensor
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from colossalai.context import ParallelMode
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from functools import partial
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def run_simple_net():
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# A simple net with two stacked nn.Linear
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get_components_func = non_distributed_component_funcs.get_callable('simple_net')
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model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
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with ColoInitContext(device=get_current_device()):
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model = model_builder(checkpoint=True)
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parallel_action_list = [
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ParallelAction(priority=1, compute_pattern=ComputePattern.TP1DRow, parallel_mode=ParallelMode.PARALLEL_1D)
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]
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spec = TensorSpec(parallel_action_list)
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# A naive way to set spec for all weights in Linear
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for name, p in named_params_with_colotensor(model):
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if not isinstance(p, ColoTensor):
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continue
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if 'weight' in name and 'LayerNorm' not in name and 'ln' not in name and 'embed' not in name:
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p.set_spec(spec)
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model.cuda()
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for param in named_params_with_colotensor(model):
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print(param)
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for i, (data, label) in enumerate(train_dataloader):
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data = data.to(get_current_device())
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label = label.to(get_current_device())
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if criterion:
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output = model(data)
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loss = criterion(output, label)
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else:
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output = model(data, label)
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loss = output
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print(loss.torch_tensor())
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loss.backward()
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if i > 5:
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break
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# TODO(jzy) check the results with col.nn.Linear?
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def run_dist(rank, world_size, port):
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config = dict(parallel=dict(tensor=dict(mode="1d", size=world_size),))
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colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
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run_simple_net()
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@pytest.mark.skip
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@pytest.mark.dist
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@parameterize('world_size', [1, 4])
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@rerun_if_address_is_in_use()
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def test_simple_net(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_simple_net()
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