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64 lines
1.9 KiB
64 lines
1.9 KiB
import pytest
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import torch
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import colossalai
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from colossalai.accelerator import get_accelerator
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from colossalai.testing import rerun_if_address_is_in_use, spawn
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from colossalai.zero.gemini.chunk import init_chunk_manager, search_chunk_configuration
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from tests.kit.model_zoo import model_zoo
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def exam_search_chunk_size():
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model_builder, data_gen_fn, output_transform_fn, *_ = next(
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iter(model_zoo.get_sub_registry("transformers_gpt_lm").values())
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)
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# make sure torch_model and model has the same parameter values
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model = model_builder()
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config_dict, *_ = search_chunk_configuration(
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model, search_range_m=1, search_interval=128, min_chunk_size_m=0, filter_exlarge_params=True
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)
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for key in config_dict:
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chunk_size = config_dict[key]["chunk_size"]
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assert chunk_size == 527872
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def exam_chunk_manager():
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world_size = torch.distributed.get_world_size()
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model_builder, data_gen_fn, output_transform_fn, *_ = next(
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iter(model_zoo.get_sub_registry("transformers_gpt_lm").values())
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)
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sharded_ddp_model = model_builder()
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chunk_manager = init_chunk_manager(
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sharded_ddp_model,
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get_accelerator().get_current_device(),
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hidden_dim=128,
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search_range_m=1,
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min_chunk_size_m=0,
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filter_exlarge_params=True,
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strict_ddp_flag=True,
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)
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config_dict = chunk_manager.dp_degree_chunk_size_dict
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assert len(config_dict) == 1
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assert config_dict[world_size] == 527872
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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_search_chunk_size()
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exam_chunk_manager()
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@pytest.mark.dist
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@pytest.mark.parametrize("world_size", [1, 4])
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@rerun_if_address_is_in_use()
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def test_search(world_size):
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spawn(run_dist, world_size)
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if __name__ == "__main__":
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test_search(4)
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