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96 lines
3.4 KiB
96 lines
3.4 KiB
from functools import partial
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import pytest
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import torch
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import torch.distributed as dist
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import torch.multiprocessing as mp
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import colossalai
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from colossalai.gemini.chunk import ChunkManager, search_chunk_configuration
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from colossalai.gemini.gemini_mgr import GeminiManager
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from colossalai.nn.optimizer import HybridAdam
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from colossalai.nn.optimizer.zero_optimizer import ZeroOptimizer
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from colossalai.nn.parallel import ZeroDDP
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from colossalai.testing import parameterize, rerun_if_address_is_in_use
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from colossalai.utils import free_port
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from colossalai.utils.cuda import get_current_device
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from colossalai.utils.model.colo_init_context import ColoInitContext
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from tests.components_to_test.registry import non_distributed_component_funcs
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from tests.test_tensor.common_utils import debug_print, set_seed
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@parameterize('placement_policy', ['cuda', 'cpu', 'auto'])
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@parameterize('keep_gathered', [True, False])
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def exam_zero_optim_state_dict(placement_policy, keep_gathered):
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set_seed(431)
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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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set_seed(451)
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torch_model = model_builder() # get a different model
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world_size = torch.distributed.get_world_size()
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config_dict, _ = search_chunk_configuration(model, search_range_mb=1, search_interval_byte=100)
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config_dict[world_size]['chunk_size'] = 5000
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config_dict[world_size]['keep_gathered'] = keep_gathered
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if placement_policy != 'cuda':
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init_device = torch.device('cpu')
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else:
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init_device = None
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chunk_manager = ChunkManager(config_dict, init_device=init_device)
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gemini_manager = GeminiManager(placement_policy, chunk_manager)
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model = ZeroDDP(model, gemini_manager, pin_memory=True)
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optimizer = HybridAdam(model.parameters())
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optim = ZeroOptimizer(optimizer, model, initial_scale=32) # initialize the link between chunk16 and chunk32
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set_seed(dist.get_rank() * 3 + 128)
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model.train()
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for i, (input_ids, attn_mask) in enumerate(train_dataloader):
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if i > 0:
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break
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optim.zero_grad()
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logits = model(input_ids, attn_mask)
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logits = logits.float()
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loss = criterion(logits, input_ids)
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optim.backward(loss)
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optim.step()
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optim_state_dict = optim.state_dict()
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optim.load_state_dict(optim_state_dict)
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new_state = optim.state_dict()['state']
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org_state = optim_state_dict['state']
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for k, v in org_state.items():
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w = new_state[k]
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for n, m in v.items():
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if isinstance(m, torch.Tensor):
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o = w[n]
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if m.device != o.device:
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o = o.to(m.device)
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assert torch.equal(m, o)
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else:
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assert m == w[n]
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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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exam_zero_optim_state_dict()
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@pytest.mark.dist
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@pytest.mark.parametrize('world_size', [1, 4])
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@rerun_if_address_is_in_use()
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def test_zero_optim(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(1)
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