mirror of https://github.com/hpcaitech/ColossalAI
81 lines
3.1 KiB
Python
81 lines
3.1 KiB
Python
import pytest
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import colossalai
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from colossalai.context.parallel_mode import ParallelMode
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import torch.multiprocessing as mp
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from colossalai.testing import rerun_if_address_is_in_use
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from colossalai.utils.cuda import get_current_device
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from colossalai.utils import free_port
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from colossalai.utils.model.colo_init_context import ColoInitContext
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from colossalai.tensor import ChunkManager
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from colossalai.core import global_context as gpc
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from functools import partial
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from _utils import tensor_equal, set_seed
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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 colossalai.nn.parallel import ColoDDPV2
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from colossalai.testing import parameterize
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def check_param_equal(model, torch_model):
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for p, torch_p in zip(model.parameters(), torch_model.parameters()):
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if p.storage().size() > 0:
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assert tensor_equal(torch_p, p.float()), f'{torch_p} vs {p}'
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def check_grad_equal(model, torch_model):
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for p, torch_p in zip(model.parameters(), torch_model.parameters()):
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if p.grad is not None:
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assert tensor_equal(torch_p.grad, p.grad.float())
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@parameterize('use_chunk', [False, True])
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@parameterize('use_zero', [False, True])
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def run_gpt(use_chunk, use_zero):
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set_seed(42)
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get_components_func = non_distributed_component_funcs.get_callable('gpt2')
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model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
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with ColoInitContext(device=get_current_device()):
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model = model_builder(checkpoint=True)
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model = model.cuda()
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torch_model = model_builder().cuda()
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for torch_p, p in zip(torch_model.parameters(), model.parameters()):
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torch_p.data.copy_(p)
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model = model.half()
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chunk_size = 38 * 1024**2 if use_chunk else None
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chunk_manager = ChunkManager(chunk_size, enable_distributed_storage=use_zero)
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model = ColoDDPV2(model, chunk_manager)
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torch_model = DDP(torch_model, device_ids=[gpc.get_global_rank()], process_group=gpc.get_group(ParallelMode.DATA))
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print(chunk_manager)
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check_param_equal(model, torch_model)
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model.train()
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torch_model.train()
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set_seed(gpc.get_local_rank(ParallelMode.DATA))
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for i, (input_ids, attn_mask) in enumerate(train_dataloader):
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logits = model(input_ids, attn_mask)
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torch_logits = torch_model(input_ids, attn_mask)
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assert tensor_equal(torch_logits, logits.float())
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loss = criterion(logits, input_ids)
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torch_loss = criterion(torch_logits, input_ids)
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model.backward(loss)
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torch_loss.backward()
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check_grad_equal(model, torch_model)
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break
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def run_dist(rank, world_size, port):
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colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
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run_gpt()
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@pytest.mark.dist
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@pytest.mark.parametrize('world_size', [1, 4])
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@rerun_if_address_is_in_use()
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def test_gpt(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_gpt(4)
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