mirror of https://github.com/hpcaitech/ColossalAI
104 lines
4.0 KiB
Python
104 lines
4.0 KiB
Python
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.nn.optimizer import CPUAdam
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from colossalai.testing import parameterize
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from colossalai.utils import free_port
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from colossalai.zero.init_ctx import ZeroInitContext
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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_model.utils import col_model_deepcopy
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from colossalai.zero.sharded_optim import ShardedOptimizerV2
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from colossalai.zero.sharded_optim._utils import has_inf_or_nan
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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 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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@parameterize("cpu_offload", [True, False])
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@parameterize("use_cpuadam", [True, False])
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@parameterize("shard_strategy_class", [TensorShardStrategy, BucketTensorShardStrategy])
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def _run_test_sharded_optim_v2(cpu_offload, shard_strategy_class, use_cpuadam):
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test_models = ['repeated_computed_layers', 'resnet18', 'bert']
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shard_strategy = shard_strategy_class()
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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_builder, train_dataloader, _, optimizer_class, criterion = get_components_func()
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with ZeroInitContext(convert_fp16=True,
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target_device=torch.device(f'cpu:0'),
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shard_strategy=shard_strategy,
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shard_param=True,
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rm_torch_payload_on_the_fly=False):
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zero_model = model_builder(checkpoint=True)
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zero_model = ShardedModelV2(zero_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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model = model_builder(checkpoint=True).half()
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col_model_deepcopy(zero_model, model)
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model = model.cuda().float()
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if dist.get_world_size() > 1:
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model = DDP(model)
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if use_cpuadam:
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optimizer_class = CPUAdam
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optim = optimizer_class(model.parameters(), lr=1e-3)
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sharded_optim = optimizer_class(zero_model.parameters(), lr=1e-3)
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sharded_optim = ShardedOptimizerV2(zero_model, sharded_optim, cpu_offload=cpu_offload, initial_scale=2**5)
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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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for param in model.parameters():
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assert not has_inf_or_nan(param)
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def _run_dist(rank, world_size, port):
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colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
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_run_test_sharded_optim_v2()
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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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def test_sharded_optim_v2(world_size):
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run_func = partial(_run_dist, world_size=world_size, port=free_port())
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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(world_size=2)
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