mirror of https://github.com/hpcaitech/ColossalAI
141 lines
5.0 KiB
Python
141 lines
5.0 KiB
Python
import copy
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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, ColoDDP
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from colossalai.gemini.gemini_mgr import GeminiManager
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from collections import OrderedDict
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from colossalai.tensor import ProcessGroup, ColoParameter
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from colossalai.testing import parameterize
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def check_state_dict_equal(state_dict: OrderedDict, other_state_dict: OrderedDict):
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for (k1, t1), (k2, t2) in zip(state_dict.items(), other_state_dict.items()):
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assert k1 == k2
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if t1.device != t2.device:
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temp_t2 = t2.to(t1.device)
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else:
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temp_t2 = t2
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assert torch.equal(t1, temp_t2), "\t{}\n\t{}".format(t1, temp_t2)
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def check_model_equal(model_a, model_b, allow_empty: bool = False, same_dtype: bool = True):
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for (na, pa), (nb, pb) in zip(model_a.named_parameters(), model_b.named_parameters()):
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assert na == nb
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if not allow_empty:
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assert pa.storage().size() > 0
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assert pb.storage().size() > 0
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else:
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if pa.storage().size() == 0 or pb.storage().size() == 0:
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continue
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if same_dtype:
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assert pa.dtype == pb.dtype
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temp_pb = pb
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else:
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temp_pb = pb.to(pa.dtype)
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assert torch.equal(pa, temp_pb), "Parameter '{}' is not equal.\n {} {}".format(na, pa, pb)
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def init_ddp(module: torch.nn.Module) -> ColoDDP:
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pg = ProcessGroup()
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return ColoDDP(module, process_group=pg)
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def init_ddpv2(module: torch.nn.Module,
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use_chunk: bool = False,
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use_zero: bool = False,
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placement_policy: str = 'cuda') -> ZeroDDP:
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pg = ProcessGroup()
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chunk_size = ChunkManager.search_chunk_size(module, 64, 4) if use_chunk else None
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chunk_manager = ChunkManager(chunk_size, pg, enable_distributed_storage=use_zero)
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gemini_manager = GeminiManager(placement_policy, chunk_manager)
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return ZeroDDP(module, gemini_manager)
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def run_ddp_state_dict():
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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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torch_model = model_builder().cuda()
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with ColoInitContext(device=get_current_device()):
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model = model_builder()
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model = init_ddp(model)
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torch_state_dict = torch_model.state_dict()
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for param in model.parameters():
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if isinstance(param, ColoParameter):
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assert param.get_process_group() is not None
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model.load_state_dict(torch_state_dict)
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for param in model.parameters():
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if isinstance(param, ColoParameter):
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assert param.get_process_group() is not None
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state_dict = model.state_dict()
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check_state_dict_equal(torch_state_dict, state_dict)
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@parameterize('use_chunk', [False, True])
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@parameterize('placement_policy', ['cuda', 'cpu'])
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@parameterize('use_zero', [False, True])
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@parameterize('only_rank_0', [False, True])
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def run_zero_state_dict(use_chunk, placement_policy, use_zero, only_rank_0):
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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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torch_model = model_builder().cuda()
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org_torch_model = copy.deepcopy(torch_model)
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torch_state_dict = torch_model.state_dict()
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with ColoInitContext(device=get_current_device()):
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model = model_builder()
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model = init_ddpv2(model, use_chunk, use_zero, placement_policy)
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for param in model.parameters():
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if isinstance(param, ColoParameter):
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assert param.get_process_group() is not None
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model.load_state_dict(torch_state_dict, strict=False)
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check_model_equal(model, torch_model, allow_empty=True, same_dtype=False)
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for param in model.parameters():
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if isinstance(param, ColoParameter):
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assert param.get_process_group() is not None
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pg = ProcessGroup()
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state_dict = model.state_dict(only_rank_0=only_rank_0)
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if not only_rank_0 or pg.dp_local_rank() == 0:
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torch_model.load_state_dict(state_dict, strict=False)
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check_model_equal(torch_model, org_torch_model, allow_empty=False, same_dtype=True)
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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_ddp_state_dict()
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run_zero_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_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_state_dict(2)
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