mirror of https://github.com/hpcaitech/ColossalAI
85 lines
2.9 KiB
Python
85 lines
2.9 KiB
Python
#!/usr/bin/env python
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# -*- encoding: utf-8 -*-
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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.tensor_shard_strategy import \
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TensorShardStrategy
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from colossalai.zero.sharded_model import ShardedModelV2
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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_grads, check_grads_padding
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def run_fwd_bwd(model, data, label, criterion, enable_autocast=False):
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model.train()
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with torch.cuda.amp.autocast(enabled=enable_autocast):
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y = model(data)
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loss = criterion(y, label)
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loss = loss.float()
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if isinstance(model, ShardedModelV2):
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model.backward(loss)
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else:
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loss.backward()
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def run_bert_fwd_bwd(model, data, label, enable_autocast=False):
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model.train()
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with torch.cuda.amp.autocast(enabled=enable_autocast):
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output = model(input_ids=data, labels=label)
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loss = output[0]
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if isinstance(model, ShardedModelV2):
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model.backward(loss)
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else:
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loss.backward()
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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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test_models = ['repeated_computed_layers', 'resnet18', 'bert']
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shard_strategy = TensorShardStrategy()
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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, test_dataloader, optimizer, criterion = get_components_func()
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model = model(checkpoint=True).half().cuda()
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zero_model = ShardedModelV2(copy.deepcopy(model), shard_strategy)
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if dist.get_world_size() > 1:
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model = DDP(model)
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for i, (data, label) in enumerate(train_dataloader):
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if i > 2:
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break
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if model_name == 'bert':
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data, label = data.cuda(), label.cuda()
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run_bert_fwd_bwd(model, data, label, False)
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run_bert_fwd_bwd(zero_model, data, label, False)
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else:
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data, label = data.half().cuda(), label.cuda()
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run_fwd_bwd(model, data, label, criterion, False)
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run_fwd_bwd(zero_model, data, label, criterion, False)
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if dist.get_world_size() > 1:
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check_grads_padding(model, zero_model, loose=True)
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else:
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check_grads(model, zero_model, loose=True)
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@pytest.mark.dist
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@pytest.mark.parametrize("world_size", [1, 2, 4])
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def test_shard_model_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_shard_model_v2(world_size=2)
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