mirror of
https://github.com/hpcaitech/ColossalAI.git
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119 lines
4.8 KiB
Python
119 lines
4.8 KiB
Python
from functools import partial
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import colossalai
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from colossalai.utils.cuda import get_current_device
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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.amp import convert_to_apex_amp
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from colossalai.nn.optimizer import CPUAdam
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from colossalai.testing import parameterize, rerun_on_exception
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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_model_params
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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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@parameterize("gpu_margin_mem_ratio", [0.0, 0.7])
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def _run_test_sharded_optim_v2(cpu_offload, shard_strategy_class, use_cpuadam, gpu_margin_mem_ratio):
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test_models = ['repeated_computed_layers', 'resnet18', 'bert', 'no_leaf_module']
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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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if gpu_margin_mem_ratio > 0.0 and not (cpu_offload and use_cpuadam):
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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(
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target_device=torch.device(f'cpu:0') if cpu_offload else torch.device(f'cuda:{get_current_device()}'),
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shard_strategy=shard_strategy,
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shard_param=True):
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zero_model = model_builder(checkpoint=True)
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zero_model = ShardedModelV2(
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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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use_memory_tracer=gpu_margin_mem_ratio > 0.0,
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reuse_fp16_shard=use_cpuadam,
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)
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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 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,
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sharded_optim,
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cpu_offload=cpu_offload,
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initial_scale=2**5,
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gpu_margin_mem_ratio=gpu_margin_mem_ratio)
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amp_config = dict(opt_level='O2', keep_batchnorm_fp32=False)
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apex_model, apex_optimizer = convert_to_apex_amp(model, optim, amp_config)
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if dist.get_world_size() > 1:
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apex_model = DDP(apex_model)
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for i, (data, label) in enumerate(train_dataloader):
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if i > 5:
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break
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data, label = data.cuda(), label.cuda()
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_run_step(apex_model, apex_optimizer, data, label, criterion, False)
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_run_step(zero_model, sharded_optim, data, label, criterion, False)
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check_sharded_model_params(model, zero_model, loose=True, reuse_fp16_shard=use_cpuadam)
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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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@rerun_on_exception(exception_type=mp.ProcessRaisedException, pattern=".*Address already in use.*")
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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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