mirror of https://github.com/hpcaitech/ColossalAI
115 lines
2.9 KiB
Python
115 lines
2.9 KiB
Python
#!/usr/bin/env python
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# -*- encoding: utf-8 -*-
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from functools import partial
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from pathlib import Path
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import pytest
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import torch
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import torch.multiprocessing as mp
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from colossalai.context.parallel_mode import ParallelMode
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from colossalai.core import global_context as gpc
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from colossalai.initialize import launch
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CONFIG_PATH = Path(__file__).parent.joinpath('configs/parallel_3d_init.py').absolute()
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def check_data_parallel_rank(rank):
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dp_rank = gpc.get_local_rank(ParallelMode.DATA)
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if rank in list(range(16)):
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assert dp_rank == 0
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elif rank in list(range(16, 32)):
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assert dp_rank == 1
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def check_pipeline_parallel_rank(rank):
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ppr = gpc.get_local_rank(ParallelMode.PIPELINE)
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if rank in list(range(8)):
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assert ppr == 0
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elif rank in list(range(8, 16)):
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assert ppr == 1
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elif rank in list(range(16, 24)):
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assert ppr == 0
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elif rank in list(range(24, 32)):
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assert ppr == 1
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def check_tensor_parallel_rank(rank):
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tp_rank = gpc.get_local_rank(ParallelMode.TENSOR)
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for i in range(8):
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ranks = list(range(i, 32, 8))
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if rank in ranks:
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assert tp_rank == i
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def check_3d_parallel_rank(rank):
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ip_rank = gpc.get_local_rank(ParallelMode.PARALLEL_3D_INPUT)
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wp_rank = gpc.get_local_rank(ParallelMode.PARALLEL_3D_WEIGHT)
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op_rank = gpc.get_local_rank(ParallelMode.PARALLEL_3D_OUTPUT)
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# check for input parallel group
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for i in range(2):
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_ranks = list(range(i * 2, 32, 4))
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_ranks_plus_one = [val + 1 for val in _ranks]
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input_ranks = _ranks + _ranks_plus_one
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if rank in input_ranks:
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assert ip_rank == i
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# check for weight parallel group
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for i in range(2):
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ranks = list(range(i, 32, 2))
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if rank in ranks:
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assert wp_rank == i
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# check for output parallel group
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for i in range(2):
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ranks = []
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for j in range(i * 4, 32, 8):
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ranks.extend([j + k for k in range(4)])
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if rank in ranks:
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assert op_rank == i
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def init_3d(rank, world_size, backend, port, host):
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dist_args = dict(
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config=CONFIG_PATH,
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rank=rank,
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world_size=world_size,
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backend=backend,
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port=port,
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host=host,
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verbose=True
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)
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launch(**dist_args)
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check_tensor_parallel_rank(rank)
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check_3d_parallel_rank(rank)
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check_data_parallel_rank(rank)
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check_pipeline_parallel_rank(rank)
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gpc.destroy()
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torch.cuda.empty_cache()
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@pytest.mark.cpu
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def test_3d_init():
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"""
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As no computation or communication is done, we can run this test on CPU.
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"""
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world_size = 32
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test_fn = partial(init_3d,
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world_size=world_size,
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backend='gloo',
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port='29902',
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host='localhost'
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)
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mp.spawn(test_fn, nprocs=world_size)
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if __name__ == '__main__':
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test_3d_init()
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