ColossalAI/tests/test_zero/test_gemini/test_search.py

import pytest
import torch

import colossalai
from colossalai.tensor import ComputePattern, ComputeSpec, ProcessGroup, ShardSpec
from colossalai.testing import rerun_if_address_is_in_use, spawn
from colossalai.utils import get_current_device
from colossalai.zero import ColoInitContext
from colossalai.zero.gemini.chunk import init_chunk_manager, search_chunk_configuration
from tests.components_to_test.registry import non_distributed_component_funcs


def init_1d_row_spec(model, pg: ProcessGroup):
    tensor_spec = (ShardSpec([0], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
    for n, p in model.named_parameters():
        if 'weight' in n and 'ln' not in n:
            p.set_process_group(pg)
            p.set_tensor_spec(*tensor_spec)


def exam_search_chunk_size():
    world_size = torch.distributed.get_world_size()
    pg_tp = ProcessGroup(tp_degree=world_size)

    get_components_func = non_distributed_component_funcs.get_callable('gpt2')
    model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()

    # make sure torch_model and model has the same parameter values
    with ColoInitContext(device=get_current_device()):
        model = model_builder()
    init_1d_row_spec(model, pg_tp)
    config_dict, *_ = search_chunk_configuration(model,
                                                 search_range_m=1,
                                                 search_interval=16,
                                                 min_chunk_size_m=0,
                                                 filter_exlarge_params=True)

    for key in config_dict:
        chunk_size = config_dict[key]['chunk_size']
        if world_size == 1:
            assert chunk_size == 31616
        else:
            assert chunk_size == 1024


def exam_search_strict_ddp():
    world_size = torch.distributed.get_world_size()
    default_shard_pg = ProcessGroup(tp_degree=world_size)
    default_shard_spec = ShardSpec([-1], [world_size])

    get_components_func = non_distributed_component_funcs.get_callable('gpt2')
    model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
    # get the chunk configuration over replicated models
    with ColoInitContext(device=get_current_device()):
        ddp_model = model_builder()
    re_dict, re_total, re_wasted = search_chunk_configuration(ddp_model,
                                                              search_range_m=1,
                                                              search_interval=16,
                                                              min_chunk_size_m=0,
                                                              filter_exlarge_params=True,
                                                              strict_ddp_flag=False)
    # get the chunk configuration over sharded ddp models
    with ColoInitContext(device=get_current_device(), default_pg=default_shard_pg,
                         default_dist_spec=default_shard_spec):
        sharded_ddp_model = model_builder()
    sh_dict, sh_total, sh_wasted = search_chunk_configuration(sharded_ddp_model,
                                                              search_range_m=1,
                                                              search_interval=16,
                                                              min_chunk_size_m=0,
                                                              filter_exlarge_params=True,
                                                              strict_ddp_flag=True)
    assert re_dict == sh_dict
    for key in re_dict:
        assert re_dict[key] == sh_dict[key]

    assert re_total == sh_total
    assert re_wasted == sh_wasted


def exam_chunk_manager():
    world_size = torch.distributed.get_world_size()
    default_shard_pg = ProcessGroup(tp_degree=world_size)
    default_shard_spec = ShardSpec([-1], [world_size])

    get_components_func = non_distributed_component_funcs.get_callable('gpt2')
    model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()

    with ColoInitContext(device=get_current_device(), default_pg=default_shard_pg,
                         default_dist_spec=default_shard_spec):
        sharded_ddp_model = model_builder()
    chunk_manager = init_chunk_manager(sharded_ddp_model,
                                       get_current_device(),
                                       hidden_dim=16,
                                       search_range_m=1,
                                       min_chunk_size_m=0,
                                       filter_exlarge_params=True,
                                       strict_ddp_flag=True)
    config_dict = chunk_manager.dp_degree_chunk_size_dict
    assert len(config_dict) == 1
    assert config_dict[world_size] == 31616


def run_dist(rank, world_size, port):
    colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
    exam_search_chunk_size()
    exam_search_strict_ddp()
    exam_chunk_manager()


@pytest.mark.dist
@pytest.mark.parametrize('world_size', [1, 4])
@rerun_if_address_is_in_use()
def test_search(world_size):
    spawn(run_dist, world_size)


if __name__ == '__main__':
    test_search(4)