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aibig-modeldata-parallelismdeep-learningdistributed-computingfoundation-modelsheterogeneous-traininghpcinferencelarge-scalemodel-parallelismpipeline-parallelism
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41 lines
1.4 KiB
41 lines
1.4 KiB
import torch |
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import colossalai |
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from colossalai.legacy.zero.gemini.tensor_utils import colo_model_data_tensor_move, colo_model_data_tensor_move_inline |
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from colossalai.legacy.zero.sharded_param import ShardedTensor |
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from colossalai.testing import rerun_if_address_is_in_use, spawn |
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def run_tensor_move(rank, world_size, port): |
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colossalai.legacy.launch(rank=0, world_size=world_size, host="localhost", port=port, backend="nccl") |
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src_t = torch.ones(2, 3).cuda() |
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tgt_t = torch.zeros(2, 3) |
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colo_model_data_tensor_move(src_t, tgt_t) |
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assert torch.sum(tgt_t) == 6.0, f"{torch.sum(tgt_t.payload)} vs. 6.0" |
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src_t = torch.ones(2, 3) |
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tgt_t = torch.zeros(2, 3).cuda().half() |
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colo_model_data_tensor_move(src_t, tgt_t) |
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# the src_t has been removed |
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assert src_t.numel() == 0 |
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assert torch.sum(tgt_t) == 6.0, f"{torch.sum(tgt_t.payload)} vs. 6.0" |
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src_t = ShardedTensor(torch.ones(2, 3)) |
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tgt_t = ShardedTensor(torch.zeros(2, 3).cuda().half()) |
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colo_model_data_tensor_move(src_t, tgt_t) |
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assert torch.sum(tgt_t.payload) == 6.0, f"{torch.sum(tgt_t.payload)} vs. 6.0" |
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assert tgt_t.device.type == "cuda" |
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colo_model_data_tensor_move_inline(tgt_t, torch.device("cpu")) |
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assert tgt_t.device.type == "cpu" |
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@rerun_if_address_is_in_use() |
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def test_tensor_move(): |
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spawn(run_tensor_move, 1) |
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if __name__ == "__main__": |
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test_tensor_move()
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