mirror of https://github.com/hpcaitech/ColossalAI
78 lines
2.5 KiB
Python
78 lines
2.5 KiB
Python
import copy
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import os
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from functools import partial
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import pytest
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import torch
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import torch.distributed as dist
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from torch.nn.parallel import DistributedDataParallel as DDP
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from torch.testing import assert_close
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from colossalai.elixir.cuda import gpu_device
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from colossalai.elixir.search import simple_search
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from colossalai.elixir.utils import init_distributed, seed_all
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from colossalai.elixir.wrapper import ElixirModule, ElixirOptimizer
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from colossalai.nn.optimizer import HybridAdam
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from colossalai.testing import run_on_environment_flag
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from tests.test_elixir.utils import TEST_MODELS, allclose, assert_dict_values, to_cuda
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def exam_optimizer_one_model(model_fn, data_fn, nproc, group, exam_seed=2261):
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ddp_model = model_fn().cuda()
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test_model = copy.deepcopy(ddp_model)
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ddp_model = DDP(ddp_model)
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ddp_optim = HybridAdam(ddp_model.parameters(), lr=1e-1, weight_decay=0)
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test_optim = HybridAdam(test_model.parameters(), lr=1e-1, weight_decay=0)
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sr = simple_search(test_model, nproc, shard_device=gpu_device())
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test_model = ElixirModule(test_model, sr, group)
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test_optim = ElixirOptimizer(test_model, test_optim)
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# get different data
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seed_all(exam_seed + dist.get_rank(group))
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data = to_cuda(data_fn())
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seed_all(exam_seed, cuda_deterministic=True)
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ddp_optim.zero_grad()
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ddp_loss = ddp_model(**data)
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ddp_loss.backward()
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ddp_optim.step()
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test_optim.zero_grad()
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test_loss = test_model(**data)
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test_optim.backward(test_loss)
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test_optim.step()
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assert_close(ddp_loss, test_loss)
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torch_st = ddp_model.module.state_dict()
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test_st = test_model.state_dict()
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assert_dict_values(torch_st, test_st, fn=partial(allclose, rtol=2e-6, atol=2e-5))
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def exam_optimizer_in_models(nproc, group):
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model_fn, data_fn = TEST_MODELS.get('resnet')
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exam_optimizer_one_model(model_fn, data_fn, nproc, group)
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def run_dist(rank, world_size):
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os.environ['RANK'] = str(rank)
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os.environ['LOCAL_RANK'] = str(rank)
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os.environ['WORLD_SIZE'] = str(world_size)
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os.environ['MASTER_ADDR'] = '127.0.0.1'
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os.environ['MASTER_PORT'] = str(29512)
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init_distributed()
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exam_optimizer_in_models(nproc=world_size, group=dist.GroupMember.WORLD)
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@pytest.mark.dist
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@pytest.mark.parametrize('world_size', [1, 2, 4])
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@run_on_environment_flag('ELX')
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def test_elixir_optimizer(world_size):
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run_func = partial(run_dist, world_size=world_size)
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torch.multiprocessing.spawn(run_func, nprocs=world_size)
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if __name__ == '__main__':
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test_elixir_optimizer(world_size=4)
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