mirror of https://github.com/hpcaitech/ColossalAI
121 lines
3.6 KiB
Python
121 lines
3.6 KiB
Python
import torch
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import pytest
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from colossalai.tensor import ColoTensor
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from numpy import allclose
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import colossalai
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from colossalai.utils import free_port
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from colossalai.tensor import distspec, ColoTensorSpec
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from colossalai.core import global_context as gpc
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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 import free_port
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from colossalai.tensor import distspec, ColoTensor, ProcessGroup
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from functools import partial
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def _run_tensor_indexing():
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pg = ProcessGroup()
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torch_t = torch.randn(2, 3)
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colo_t = ColoTensor(torch_t, ColoTensorSpec(pg))
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assert allclose(torch_t[:, 1], colo_t[:, 1])
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def _run_wrapped_tensor_func():
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pg = ProcessGroup()
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t_ref = torch.randn(4, 5)
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t = ColoTensor.from_torch_tensor(t_ref.clone(), ColoTensorSpec(pg))
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# non-func attr
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assert t.is_cuda == t_ref.is_cuda
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# return 1 torch.Tensor
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t_abs = t.abs()
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assert isinstance(t_abs, ColoTensor) and torch.equal(t_abs, t_ref.abs())
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# return 1 non-torch.Tensor
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assert t.dim() == t_ref.dim()
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# return >1 torch.Tensor
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assert isinstance(t, ColoTensor)
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t_split1, t_split2 = t.split(2)
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assert isinstance(t_split1, ColoTensor) and isinstance(t_split2, ColoTensor), f"{type(t_split1)} {type(t_split2)}"
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def _run_operand():
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pg = ProcessGroup()
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t_ref = torch.randn(4, 5)
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t = ColoTensor.from_torch_tensor(t_ref.clone(), ColoTensorSpec(pg))
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t_ref_res = t_ref + t_ref
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t_res = t + t
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assert torch.allclose(t_ref_res, t_res)
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#### Test Distributed init a Colotensor
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def _run_view(world_size):
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t_ref = torch.randn(4, 5)
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rank = gpc.get_global_rank()
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pg = ProcessGroup(rank, list(range(world_size)), tp_degree=world_size)
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t = ColoTensor.from_torch_tensor(
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t_ref, ColoTensorSpec(pg, dist_attr=distspec.shard(dims=[0], num_partitions=[pg.tp_world_size()])))
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assert t.size_global()[0] == 4 * world_size
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assert t.size_global(1) == 5
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assert t.size_global() == torch.Size([4 * world_size, 5])
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t = t.view_global(4 * 5 * world_size)
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assert t.shape == torch.Size([4 * 5 * world_size])
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def _run_tensor_shard_init(world_size):
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t_ref = torch.randn(4, 5)
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pg = ProcessGroup(tp_degree=world_size)
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shard_attr = distspec.shard(dims=[0], num_partitions=[pg.tp_world_size()])
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tensor_spec = ColoTensorSpec(pg, dist_attr=shard_attr)
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t = ColoTensor.from_torch_tensor(t_ref.clone(), tensor_spec)
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t.set_dist_spec(distspec.replicate())
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assert t.shape == torch.Size((4 * world_size, 5)), f"{t.shape} vs ({4 * world_size, 5})"
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def _run_tensor_replicated_init(world_size):
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t_ref = torch.randn(4 * world_size, 5)
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pg = ProcessGroup()
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spec = ColoTensorSpec(pg)
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t = ColoTensor.from_torch_tensor(t_ref.clone(), spec)
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assert t.shape == torch.Size((4 * world_size, 5)), f"{t.shape}"
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def _run_process_group(world_size):
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pg1 = ProcessGroup()
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pg2 = ProcessGroup()
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assert pg1 == pg2
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def run_dist_tests(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_tensor_shard_init(world_size)
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_run_tensor_replicated_init(world_size)
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_run_view(world_size)
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_run_process_group(world_size)
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_run_tensor_indexing()
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_run_operand()
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# TODO not passed
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# _run_wrapped_tensor_func()
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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_dist_cases(world_size):
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run_func = partial(run_dist_tests, 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_dist_cases(2)
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