mirror of https://github.com/hpcaitech/ColossalAI
119 lines
5.2 KiB
Python
119 lines
5.2 KiB
Python
import pytest
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import torch
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import colossalai
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from colossalai.tensor import ComputePattern, ComputeSpec, ProcessGroup, ShardSpec
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from colossalai.testing import 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 import ColoInitContext
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from colossalai.zero.gemini.chunk import init_chunk_manager, search_chunk_configuration
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from tests.components_to_test.registry import non_distributed_component_funcs
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def init_1d_row_spec(model, pg: ProcessGroup):
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tensor_spec = (ShardSpec([0], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
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for n, p in model.named_parameters():
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if 'weight' in n and 'ln' not in n:
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p.set_process_group(pg)
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p.set_tensor_spec(*tensor_spec)
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def exam_search_chunk_size():
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world_size = torch.distributed.get_world_size()
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pg_tp = ProcessGroup(tp_degree=world_size)
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get_components_func = non_distributed_component_funcs.get_callable('gpt2')
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model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
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# make sure torch_model and model has the same parameter values
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with ColoInitContext(device=get_current_device()):
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model = model_builder()
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init_1d_row_spec(model, pg_tp)
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config_dict, *_ = search_chunk_configuration(model,
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search_range_m=1,
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search_interval=16,
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min_chunk_size_m=0,
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filter_exlarge_params=True)
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for key in config_dict:
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chunk_size = config_dict[key]['chunk_size']
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if world_size == 1:
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assert chunk_size == 31616
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else:
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assert chunk_size == 1024
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def exam_search_strict_ddp():
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world_size = torch.distributed.get_world_size()
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default_shard_pg = ProcessGroup(tp_degree=world_size)
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default_shard_spec = ShardSpec([-1], [world_size])
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get_components_func = non_distributed_component_funcs.get_callable('gpt2')
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model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
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# get the chunk configuration over replicated models
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with ColoInitContext(device=get_current_device()):
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ddp_model = model_builder()
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re_dict, re_total, re_wasted = search_chunk_configuration(ddp_model,
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search_range_m=1,
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search_interval=16,
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min_chunk_size_m=0,
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filter_exlarge_params=True,
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strict_ddp_flag=False)
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# get the chunk configuration over sharded ddp models
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with ColoInitContext(device=get_current_device(), default_pg=default_shard_pg,
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default_dist_spec=default_shard_spec):
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sharded_ddp_model = model_builder()
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sh_dict, sh_total, sh_wasted = search_chunk_configuration(sharded_ddp_model,
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search_range_m=1,
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search_interval=16,
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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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assert re_dict == sh_dict
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for key in re_dict:
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assert re_dict[key] == sh_dict[key]
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assert re_total == sh_total
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assert re_wasted == sh_wasted
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def exam_chunk_manager():
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world_size = torch.distributed.get_world_size()
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default_shard_pg = ProcessGroup(tp_degree=world_size)
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default_shard_spec = ShardSpec([-1], [world_size])
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get_components_func = non_distributed_component_funcs.get_callable('gpt2')
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model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
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with ColoInitContext(device=get_current_device(), default_pg=default_shard_pg,
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default_dist_spec=default_shard_spec):
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sharded_ddp_model = model_builder()
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chunk_manager = init_chunk_manager(sharded_ddp_model,
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get_current_device(),
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hidden_dim=16,
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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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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] == 31616
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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_search_strict_ddp()
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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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