mirror of https://github.com/hpcaitech/ColossalAI
106 lines
4.1 KiB
Python
106 lines
4.1 KiB
Python
import pytest
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import colossalai
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import torch
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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 functools import partial
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from tests.test_tensor.common_utils import tensor_equal, set_seed, tensor_shard_equal
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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.gemini.chunk import search_chunk_configuration, ChunkManager
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from colossalai.nn.parallel import ZeroDDP
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from colossalai.testing import parameterize
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from colossalai.amp import convert_to_apex_amp
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from colossalai.gemini.gemini_mgr import GeminiManager
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from colossalai.tensor import ProcessGroup
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from tests.test_tensor.common_utils import debug_print
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def check_grad(model: ZeroDDP, torch_model: torch.nn.Module):
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chunk_manager = model.chunk_manager
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param_list = [p for p in model.parameters()]
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chunk_list = chunk_manager.get_chunks(param_list)
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for chunk in chunk_list:
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chunk_manager.access_chunk(chunk)
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for (p0, p1) in zip(model.parameters(), torch_model.parameters()):
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assert torch.allclose(p0, p1.grad, atol=1e-3, rtol=1e-5), "{}".format(torch.max(torch.abs(p0 - p1.grad)).item())
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def run_fwd_bwd(model, criterion, optimizer, input_ids, attn_mask):
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optimizer.zero_grad()
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logits = model(input_ids, attn_mask)
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logits = logits.float()
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loss = criterion(logits, input_ids)
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optimizer.backward(loss)
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return logits
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@parameterize('placement_policy', ['cuda', 'cpu', 'auto', 'const'])
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def exam_gpt_fwd_bwd(placement_policy):
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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()
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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.data)
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world_size = torch.distributed.get_world_size()
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config_dict = search_chunk_configuration(model, search_range_mb=1, search_interval_byte=100)
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config_dict[world_size]['chunk_size'] = 5000
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config_dict[world_size]['keep_gathered'] = False
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chunk_manager = ChunkManager(config_dict)
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gemini_manager = GeminiManager(placement_policy, chunk_manager)
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model = ZeroDDP(model, gemini_manager, pin_memory=True)
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pg = ProcessGroup()
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amp_config = dict(opt_level='O2', keep_batchnorm_fp32=False, loss_scale=1)
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torch_optim = torch.optim.Adam(torch_model.parameters(), lr=1e-3)
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torch_model, torch_optim = convert_to_apex_amp(torch_model, torch_optim, amp_config)
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torch_model = DDP(torch_model, device_ids=[pg.rank()], process_group=pg.dp_process_group())
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model.eval()
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torch_model.eval()
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set_seed(pg.dp_local_rank())
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for i, (input_ids, attn_mask) in enumerate(train_dataloader):
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if i > 0:
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break
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logits = model(input_ids, attn_mask)
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logits = logits.float()
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loss = criterion(logits, input_ids)
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model.backward(loss)
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torch_logits = run_fwd_bwd(torch_model, criterion, torch_optim, input_ids, attn_mask)
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assert torch.allclose(logits, torch_logits, rtol=0), "{} {} {}".format(
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torch.max(torch.abs(logits - torch_logits)).item(), logits, torch_logits)
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check_grad(model, torch_model)
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def run_dist(rank, world_size, port):
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config = {}
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colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
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exam_gpt_fwd_bwd()
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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(1)
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