mirror of https://github.com/hpcaitech/ColossalAI
112 lines
4.6 KiB
Python
112 lines
4.6 KiB
Python
import copy
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from functools import partial
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import colossalai
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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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import torch.multiprocessing as mp
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from colossalai.utils import free_port
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from colossalai.zero.shard_utils import (BucketTensorShardStrategy, TensorShardStrategy)
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from colossalai.zero.sharded_model import ShardedModelV2
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from colossalai.zero.sharded_optim import ShardedOptimizerV2
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from tests.components_to_test.registry import non_distributed_component_funcs
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from torch.nn.parallel import DistributedDataParallel as DDP
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from colossalai.nn.optimizer import CPUAdam
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from common import CONFIG, check_sharded_params_padding
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def _run_step(model, optimizer, data, label, criterion, enable_autocast=False):
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model.train()
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optimizer.zero_grad()
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with torch.cuda.amp.autocast(enabled=enable_autocast):
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if criterion:
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y = model(data)
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loss = criterion(y, label)
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else:
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loss = model(data, label)
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loss = loss.float()
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if isinstance(model, ShardedModelV2):
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optimizer.backward(loss)
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else:
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loss.backward()
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optimizer.step()
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def _run_dist(rank, world_size, port, cpu_offload, shard_strategy, use_cpuadam):
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colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
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test_models = ['repeated_computed_layers', 'resnet18', 'bert']
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shard_strategy = shard_strategy()
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if use_cpuadam and cpu_offload is False:
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return
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for model_name in test_models:
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get_components_func = non_distributed_component_funcs.get_callable(model_name)
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model, train_dataloader, _, optimizer_class, criterion = get_components_func()
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model = model(checkpoint=True).cuda()
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zero_model = ShardedModelV2(copy.deepcopy(model),
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shard_strategy,
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offload_config=dict(device='cpu') if cpu_offload else None)
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if dist.get_world_size() > 1:
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model = DDP(model)
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lr = 1e-3
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if use_cpuadam:
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optim = torch.optim.Adam(model.parameters(), lr=lr)
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sharded_optim = ShardedOptimizerV2(zero_model, CPUAdam, cpu_offload=cpu_offload, initial_scale=2**5, lr=lr)
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else:
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optim = optimizer_class(model.parameters(), lr=lr)
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sharded_optim = ShardedOptimizerV2(zero_model,
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optimizer_class,
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cpu_offload=cpu_offload,
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initial_scale=2**5,
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lr=lr)
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for i, (data, label) in enumerate(train_dataloader):
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#FIXME() if i > 5, the unittest will fail
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if i > 3:
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break
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data, label = data.cuda(), label.cuda()
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_run_step(model, optim, data, label, criterion, False)
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_run_step(zero_model, sharded_optim, data, label, criterion, False)
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check_sharded_params_padding(model, zero_model, loose=True)
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# use_cpuadam = True can be used with cpu_offload = False
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@pytest.mark.dist
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@pytest.mark.parametrize("world_size", [1, 2])
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@pytest.mark.parametrize("cpu_offload", [False])
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@pytest.mark.parametrize("use_cpuadam", [False])
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@pytest.mark.parametrize("shard_strategy", [TensorShardStrategy, BucketTensorShardStrategy])
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def test_sharded_optim_v2(world_size, cpu_offload, shard_strategy, use_cpuadam):
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run_func = partial(_run_dist,
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world_size=world_size,
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port=free_port(),
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cpu_offload=cpu_offload,
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shard_strategy=shard_strategy,
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use_cpuadam=use_cpuadam)
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mp.spawn(run_func, nprocs=world_size)
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@pytest.mark.dist
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@pytest.mark.parametrize("world_size", [1, 2])
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@pytest.mark.parametrize("cpu_offload", [True])
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@pytest.mark.parametrize("shard_strategy", [TensorShardStrategy, BucketTensorShardStrategy])
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@pytest.mark.parametrize("use_cpuadam", [True, False])
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def test_sharded_optim_v2_cpu_adam(world_size, cpu_offload, shard_strategy, use_cpuadam):
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run_func = partial(_run_dist,
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world_size=world_size,
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port=free_port(),
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cpu_offload=cpu_offload,
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shard_strategy=shard_strategy,
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use_cpuadam=use_cpuadam)
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mp.spawn(run_func, nprocs=world_size)
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if __name__ == '__main__':
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test_sharded_optim_v2_cpu_adam(world_size=2,
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cpu_offload=False,
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shard_strategy=TensorShardStrategy,
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use_cpuadam=True)
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