mirror of https://github.com/hpcaitech/ColossalAI
68 lines
2.1 KiB
Python
68 lines
2.1 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.context.parallel_mode import ParallelMode
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from colossalai.core import global_context as gpc
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from colossalai.utils import free_port
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from colossalai.zero.sharded_model import ShardedModelV2
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from colossalai.zero.sharded_optim import ShardedOptimizerV2
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from torch.optim import Adam
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from common import (CONFIG, Net, check_grads, check_grads_padding, check_params, check_sharded_params_padding)
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def run_step(model, optimizer, x, 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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y = model(x)
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loss = y.sum()
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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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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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model = Net(checkpoint=True).cuda()
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zero_model = copy.deepcopy(model)
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zero_model = ShardedModelV2(zero_model, process_group=gpc.get_group(ParallelMode.DATA))
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for n, p in zero_model.named_parameters():
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p._name = n
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optim = Adam(model.parameters(), lr=1e-3)
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sharded_optim = ShardedOptimizerV2(Adam(zero_model.parameters(), lr=1e-3), zero_model)
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for _ in range(2):
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x = torch.rand(2, 5).cuda()
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run_step(zero_model, sharded_optim, x, False)
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run_step(model, optim, x, False)
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if dist.get_world_size() > 1:
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check_grads_padding(model, zero_model)
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check_sharded_params_padding(model, zero_model)
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else:
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check_grads(model, zero_model)
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check_params(model, zero_model)
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@pytest.mark.dist
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def test_sharded_optim_v2():
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world_size = 2
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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()
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