mirror of https://github.com/hpcaitech/ColossalAI
122 lines
4.1 KiB
Python
122 lines
4.1 KiB
Python
import pytest
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import torch
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import torch.distributed as dist
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from torch.distributed.distributed_c10d import _get_default_group
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import colossalai
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from colossalai.tensor import ColoParameter
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from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
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from colossalai.utils import get_current_device
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from colossalai.zero.gemini import TensorState
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from colossalai.zero.gemini.chunk import Chunk
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def dist_sum(x):
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temp = torch.tensor([x], device=get_current_device())
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dist.all_reduce(temp)
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return temp.item()
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def add_param(param_list, param_cp_list, *args, **kwargs):
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param = ColoParameter(torch.randn(*args, **kwargs))
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param_list.append(param)
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param_cp_list.append(param.clone())
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def check_equal(param, param_cp):
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if param.device != param_cp.device:
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temp = param.data.to(param_cp.device)
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else:
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temp = param.data
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return torch.equal(temp, param_cp.data)
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@parameterize('init_device', [None, torch.device('cpu')])
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@parameterize('keep_gathered', [True, False])
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@parameterize('pin_memory', [True, False])
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def exam_chunk_basic(init_device, keep_gathered, pin_memory):
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world_size = torch.distributed.get_world_size()
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pg = _get_default_group()
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my_chunk = Chunk(chunk_size=1024,
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process_group=pg,
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dtype=torch.float32,
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init_device=init_device,
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cpu_shard_init=True,
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keep_gathered=keep_gathered,
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pin_memory=pin_memory)
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param_list = []
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param_cp_list = []
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add_param(param_list, param_cp_list, 8, 8, 8, device='cuda')
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add_param(param_list, param_cp_list, 4, 4)
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add_param(param_list, param_cp_list, 4, 8, 2, device='cuda')
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add_param(param_list, param_cp_list, 1, 1, 5)
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for param in param_list:
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my_chunk.append_tensor(param)
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assert my_chunk.utilized_size == 597
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for param, param_cp in zip(param_list, param_cp_list):
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check_equal(param, param_cp)
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my_chunk.close_chunk()
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if keep_gathered is False:
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assert my_chunk.cpu_shard.size(0) == 1024 // world_size
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assert my_chunk.device_type == 'cpu'
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assert my_chunk.can_move
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my_chunk.shard_move(get_current_device())
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else:
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assert my_chunk.cuda_global_chunk.size(0) == 1024
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assert my_chunk.device_type == 'cuda'
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assert not my_chunk.can_move
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assert dist_sum(my_chunk.valid_end) == my_chunk.utilized_size
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flag = my_chunk.has_inf_or_nan
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assert not flag, "has_inf_or_nan is {}".format(flag)
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my_chunk.access_chunk()
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assert my_chunk.device_type == 'cuda'
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for param, param_cp in zip(param_list, param_cp_list):
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check_equal(param, param_cp)
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assert my_chunk.tensor_state_cnter[TensorState.HOLD] == 4
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my_chunk.tensor_trans_state(param_list[0], TensorState.COMPUTE)
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assert my_chunk.tensor_state_cnter[TensorState.HOLD] == 3
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assert my_chunk.tensor_state_cnter[TensorState.COMPUTE] == 1
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assert not my_chunk.can_release
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for param in param_list:
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my_chunk.tensor_trans_state(param, TensorState.COMPUTE)
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my_chunk.tensor_trans_state(param, TensorState.HOLD_AFTER_BWD)
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my_chunk.tensor_trans_state(param, TensorState.READY_FOR_REDUCE)
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assert my_chunk.tensor_state_cnter[TensorState.READY_FOR_REDUCE] == 4
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assert my_chunk.can_reduce
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my_chunk.reduce()
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assert my_chunk.tensor_state_cnter[TensorState.HOLD] == 4
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if keep_gathered is False:
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assert my_chunk.cuda_shard.size(0) == 1024 // world_size
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assert my_chunk.device_type == 'cuda'
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assert my_chunk.can_move
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else:
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assert my_chunk.cuda_global_chunk.size(0) == 1024
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assert my_chunk.device_type == 'cuda'
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assert not my_chunk.can_move
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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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exam_chunk_basic()
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@pytest.mark.dist
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@pytest.mark.parametrize('world_size', [1, 2, 4])
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@rerun_if_address_is_in_use()
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def test_chunk_function(world_size):
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spawn(run_dist, world_size)
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if __name__ == '__main__':
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test_chunk_function(4)
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