mirror of https://github.com/hpcaitech/ColossalAI
99 lines
3.7 KiB
Python
99 lines
3.7 KiB
Python
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import pytest
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import colossalai
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import torch
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from colossalai.context.parallel_mode import ParallelMode
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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.tensor import ChunkManager
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from colossalai.core import global_context as gpc
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from functools import partial
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from tests.test_tensor._utils import set_seed
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from tests.components_to_test.registry import non_distributed_component_funcs
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from colossalai.nn.parallel.data_parallel import ZeroDDP
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from colossalai.gemini import GeminiManager
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from colossalai.testing import parameterize
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from colossalai.nn.optimizer import HybridAdam
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from colossalai.zero import ZeroOptimizer
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def init_zero(model, use_chunk, use_zero, placement_policy):
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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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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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return ZeroDDP(model, gemini_manager)
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def run_step(model, optim, criterion, data, label):
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optim.zero_grad()
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logits = model(data)
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loss = criterion(logits, label)
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optim.backward(loss)
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optim.step()
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def check_state_dict_eq(state_dict, other):
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for p, state in state_dict['state'].items():
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other_state = other['state'][p]
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for k, v in state.items():
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if isinstance(v, torch.Tensor):
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assert torch.allclose(v, other_state[k], atol=1e-3), f'{v} vs {other_state[k]}'
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else:
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assert v == other_state[k]
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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'])
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def run_nested_model(use_chunk, use_zero, placement_policy):
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get_components_func = non_distributed_component_funcs.get_callable('nested_model')
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model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
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set_seed(42)
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with ColoInitContext(device=get_current_device()):
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model = model_builder()
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set_seed(42)
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with ColoInitContext(device=get_current_device()):
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model_copy = model_builder()
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model = init_zero(model, use_chunk, use_zero, placement_policy)
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model_copy = init_zero(model_copy, use_chunk, use_zero, placement_policy)
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optim = HybridAdam(model.parameters(), lr=1e-3)
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optim = ZeroOptimizer(optim, model, initial_scale=32)
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optim_copy = HybridAdam(model_copy.parameters(), lr=1e-3)
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optim_copy = ZeroOptimizer(optim_copy, model_copy, initial_scale=32)
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model.train()
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model_copy.train()
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set_seed(gpc.get_local_rank(ParallelMode.DATA))
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data_iter = iter(train_dataloader)
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data, label = map(lambda x: x.cuda(), next(data_iter))
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run_step(model, optim, criterion, data, label)
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optim_copy.load_state_dict(optim.state_dict())
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check_state_dict_eq(optim.state_dict(), optim_copy.state_dict())
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data, label = map(lambda x: x.cuda(), next(data_iter))
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run_step(model_copy, optim_copy, criterion, data, label)
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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_nested_model()
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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_dist(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_dist(2)
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