mirror of https://github.com/hpcaitech/ColossalAI
65 lines
2.5 KiB
Python
65 lines
2.5 KiB
Python
#!/usr/bin/env python
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# -*- encoding: utf-8 -*-
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import pytest
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import torch
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from common import CONFIG, check_grads_padding, run_fwd_bwd
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from torch.nn.parallel import DistributedDataParallel as DDP
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import colossalai
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from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
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from colossalai.zero.legacy.init_ctx import ZeroInitContext
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from colossalai.zero.legacy.shard_utils import BucketTensorShardStrategy
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from colossalai.zero.legacy.sharded_model import ShardedModelV2
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from colossalai.zero.legacy.sharded_model._utils import cast_tensor_to_fp16
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from colossalai.zero.legacy.sharded_model.utils import col_model_deepcopy
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from tests.components_to_test.registry import non_distributed_component_funcs
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@parameterize("enable_autocast", [True])
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@parameterize("shard_strategy_class", [BucketTensorShardStrategy])
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def run_model_test(enable_autocast, shard_strategy_class):
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test_models = ['repeated_computed_layers', 'resnet18', 'bert', 'hanging_param_model']
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shard_strategy = shard_strategy_class()
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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, _, _, criterion = get_components_func()
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with ZeroInitContext(target_device=torch.device('cuda', torch.cuda.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(zero_model, shard_strategy)
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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()
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model = DDP(model, device_ids=[torch.cuda.current_device()])
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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 = cast_tensor_to_fp16(data).cuda(), label.cuda()
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run_fwd_bwd(model, data, label, criterion, enable_autocast)
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run_fwd_bwd(zero_model, data, label, criterion, enable_autocast)
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check_grads_padding(model, zero_model, loose=True)
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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_model_test()
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@pytest.mark.dist
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@pytest.mark.parametrize("world_size", [1, 2])
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@rerun_if_address_is_in_use()
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def test_shard_model_v2(world_size):
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spawn(run_dist, world_size)
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if __name__ == '__main__':
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test_shard_model_v2(world_size=2)
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