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131 lines
5.3 KiB
131 lines
5.3 KiB
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.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_if_address_is_in_use
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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 colossalai.utils import get_current_device
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from tests.components_to_test.registry import non_distributed_component_funcs
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from colossalai.engine.gradient_handler import MoeGradientHandler
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from colossalai.context import MOE_CONTEXT
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from colossalai.testing import assert_equal_in_group
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from tests.test_zero.common import CONFIG, check_sharded_model_params
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from tests.test_moe.test_moe_zero_init import MoeModel
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def _run_step(model, optimizer, data, label, criterion, grad_handler):
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model.train()
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optimizer.zero_grad()
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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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if grad_handler is not None:
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grad_handler.handle_gradient()
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optimizer.step()
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@parameterize("cpu_offload", [True])
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@parameterize("use_cpuadam", [True]) # We do not use Hybrid Adam right now, since it has a little bug
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@parameterize("reuse_fp16_shard", [True, False])
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@parameterize("shard_strategy_class", [TensorShardStrategy, BucketTensorShardStrategy])
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def _run_test_sharded_optim_v2(cpu_offload,
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shard_strategy_class,
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use_cpuadam,
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reuse_fp16_shard,
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gpu_margin_mem_ratio=0.0):
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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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MOE_CONTEXT.reset_loss()
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get_components_func = non_distributed_component_funcs.get_callable('no_leaf_module')
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_, train_dataloader, _, optimizer_class, criterion = get_components_func()
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with ZeroInitContext(target_device=torch.device('cpu') if cpu_offload else get_current_device(),
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shard_strategy=shard_strategy,
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shard_param=True):
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zero_model = MoeModel(checkpoint=True)
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zero_model = ShardedModelV2(zero_model,
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shard_strategy,
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tensor_placement_policy='cpu' if cpu_offload else 'cuda',
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reuse_fp16_shard=reuse_fp16_shard)
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# check whether parameters are identical in ddp
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for name, p in zero_model.named_parameters():
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if not p.colo_attr.param_is_sharded and p.colo_attr.is_replicated:
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assert_equal_in_group(p.colo_attr.data_payload.to(get_current_device()))
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model = MoeModel(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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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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apex_grad_handler = MoeGradientHandler(model)
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# Since MOE is not compatible with apex_amp now, we need to convert gate weight to fp32
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for (n, p), zp in zip(apex_model.named_parameters(), zero_model.parameters()):
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if 'gate' in n:
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p.data = p.float()
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p.data.copy_(zp.colo_attr.data_payload)
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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, apex_grad_handler)
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_run_step(zero_model, sharded_optim, data, label, criterion, None)
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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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MOE_CONTEXT.setup(seed=42)
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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", [2])
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@rerun_if_address_is_in_use()
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def test_moe_zero_optim(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_moe_zero_optim(world_size=4)
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