mirror of https://github.com/hpcaitech/ColossalAI
100 lines
3.6 KiB
Python
100 lines
3.6 KiB
Python
import pytest
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import colossalai
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import torch
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import torch.multiprocessing as mp
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from colossalai.testing import 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.model.colo_init_context import ColoInitContext
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from colossalai.gemini import ChunkManager
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from functools import partial
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from tests.components_to_test.registry import non_distributed_component_funcs
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from colossalai.nn.parallel import ZeroDDP
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from colossalai.nn.optimizer import HybridAdam
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from colossalai.zero import ZeroOptimizer
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from colossalai.testing import parameterize
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from colossalai.gemini.gemini_mgr import GeminiManager
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from colossalai.tensor import ProcessGroup
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def check_state(s1, s2):
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for v1, v2 in zip(s1.values(), s2.values()):
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if isinstance(v1, torch.Tensor):
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v1 = v1.to(v2.device)
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assert torch.equal(v1, v2), f'{torch.sum((v1-v2).abs())}'
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else:
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assert v1 == v2
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def check_load_state_dict(optim, torch_optim):
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for group, torch_group in zip(optim.optim.param_groups, torch_optim.param_groups):
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for p, torch_p in zip(group['params'], torch_group['params']):
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state = optim.optim.state[p]
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torch_state = torch_optim.state[torch_p]
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if p.storage().size() == 0:
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assert len(state) == 0
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check_state(state, torch_state)
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def check_state_dict(state_dict, torch_state_dict):
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for (k1, s1), (k2, s2) in zip(state_dict['state'].items(), torch_state_dict['state'].items()):
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assert k1 == k2
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check_state(s1, s2)
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@parameterize('use_chunk', [False, True])
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@parameterize('use_zero', [False, True])
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@parameterize('placement_policy', ['cuda', 'cpu', 'auto'])
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def run_zero_optim_state_dict(use_chunk, use_zero, placement_policy):
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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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with ColoInitContext(device=get_current_device()):
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model = model_builder()
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model = model.cuda()
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torch_model = model_builder().cuda()
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pg = ProcessGroup()
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chunk_size = ChunkManager.search_chunk_size(model, 8192, 8) if use_chunk else None
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chunk_manager = ChunkManager(chunk_size,
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pg,
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enable_distributed_storage=use_zero,
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init_device=GeminiManager.get_default_device(placement_policy))
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gemini_manager = GeminiManager(placement_policy, chunk_manager)
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model = ZeroDDP(model, gemini_manager)
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optim = HybridAdam(model.parameters(), lr=1e-3)
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optim = ZeroOptimizer(optim, model, initial_scale=1)
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torch_optim = torch.optim.Adam(torch_model.parameters(), lr=1e-3)
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for p in torch_model.parameters():
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p.grad = torch.rand_like(p)
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torch_optim.step()
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torch_state_dict = torch_optim.state_dict()
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optim.load_state_dict(torch_state_dict)
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check_load_state_dict(optim, torch_optim)
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state_dict = optim.state_dict()
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if pg.rank() == 0:
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check_state_dict(state_dict, torch_state_dict)
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def run_dist(rank, world_size, port):
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config = {}
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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_zero_optim_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_zero_optim_state_dict(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_zero_optim_state_dict(2)
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