mirror of https://github.com/hpcaitech/ColossalAI
111 lines
4.4 KiB
Python
111 lines
4.4 KiB
Python
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.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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@parameterize('model_name', ['gpt2', 'bert'])
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def exam_state_dict(placement_policy, keep_gathered, model_name: str):
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set_seed(431)
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get_components_func = non_distributed_component_funcs.get_callable(model_name)
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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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torch_model = model_builder()
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for torch_p, p in zip(torch_model.parameters(), model.parameters()):
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torch_p.data.copy_(p.data)
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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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chunk_manager = ChunkManager(config_dict)
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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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model.train()
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zero_dict = model.state_dict(only_rank_0=False)
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torch_dict = torch_model.state_dict()
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for key, value in torch_dict.items():
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if key == 'model.lm_head.weight':
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continue
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assert key in zero_dict, "{} not in ZeRO dictionary.".format(key)
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temp_zero_value = zero_dict[key].to(device=value.device, dtype=value.dtype)
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assert torch.equal(value, temp_zero_value), "parameter '{}' has problem.".format(key)
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@parameterize('placement_policy', ['cuda', 'cpu', 'auto'])
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@parameterize('keep_gathered', [True, False])
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@parameterize('model_name', ['gpt2', 'bert'])
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def exam_load_state_dict(placement_policy, keep_gathered, model_name: str):
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set_seed(431)
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get_components_func = non_distributed_component_funcs.get_callable(model_name)
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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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torch_dict = torch_model.state_dict()
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model.load_state_dict(torch_dict, strict=False)
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zero_dict = model.state_dict(only_rank_0=False)
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for key, value in torch_dict.items():
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if key == 'model.lm_head.weight':
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continue
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assert key in zero_dict, "{} not in ZeRO dictionary.".format(key)
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temp_zero_value = zero_dict[key].to(device=value.device, dtype=value.dtype)
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assert torch.equal(value, temp_zero_value), "parameter '{}' has problem.".format(key)
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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_state_dict()
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exam_load_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_ddp(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_ddp(1)
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