mirror of https://github.com/hpcaitech/ColossalAI
121 lines
4.1 KiB
Python
121 lines
4.1 KiB
Python
import pytest
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import torch
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import torch.distributed as dist
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from torch.nn.parallel import DistributedDataParallel as DDP
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from torch.testing import assert_close
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import colossalai
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from colossalai.amp import convert_to_apex_amp
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from colossalai.nn.optimizer import HybridAdam
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from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
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from colossalai.utils.cuda import get_current_device
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from colossalai.zero import GeminiDDP, GeminiOptimizer
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from colossalai.zero.gemini.chunk import search_chunk_configuration
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from tests.components_to_test import run_fwd_bwd
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from tests.components_to_test.registry import non_distributed_component_funcs
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from tests.test_tensor.common_utils import set_seed
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PLACEMENT_CONFIGS = [
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{
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'placement_policy': 'static',
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'shard_param_frac': 0.0
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}, # zero2
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{
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'placement_policy': 'static',
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'shard_param_frac': 1.0
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}, # zero3
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{
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'placement_policy': 'static',
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'shard_param_frac': 0.5
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}, # zero3-half
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{
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'placement_policy': 'auto'
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}
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]
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def check_grad(model: GeminiDDP, 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_close(p0, p1.grad, rtol=1e-3, atol=5e-5)
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@parameterize('placement_config', PLACEMENT_CONFIGS)
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@parameterize('keep_gather', [False, True])
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@parameterize('model_name', ['gpt2', 'bert', 'albert'])
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@parameterize('use_grad_checkpoint', [False, True])
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def exam_gpt_fwd_bwd(
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placement_config,
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keep_gather,
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model_name: str,
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use_grad_checkpoint: bool = False,
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):
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init_device = get_current_device()
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get_components_func = non_distributed_component_funcs.get_callable(model_name)
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model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
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set_seed(42)
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model = model_builder(use_grad_checkpoint)
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set_seed(42)
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torch_model = model_builder(use_grad_checkpoint).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_m=1, search_interval=100)
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config_dict[world_size]['chunk_size'] = 5000
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config_dict[world_size]['keep_gathered'] = keep_gather
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model = GeminiDDP(model, config_dict, init_device, pin_memory=True, **placement_config)
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optimizer = HybridAdam(model.parameters(), lr=1e-3)
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zero_optim = GeminiOptimizer(optimizer, model, initial_scale=1)
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rank = dist.get_rank()
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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=[rank])
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set_seed(rank)
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for i, (input_ids, label) in enumerate(train_dataloader):
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# you can only test a single fwd + bwd.
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# after bwd param is grad for Gemini, due to the chunk reuse optimization.
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if i > 0:
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break
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input_ids, label = input_ids.cuda(), label.cuda()
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torch_optim.zero_grad()
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zero_optim.zero_grad()
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# set random seed is same as torch_model.eval()
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set_seed(42)
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torch_loss = run_fwd_bwd(torch_model, input_ids, label, criterion, torch_optim)
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set_seed(42)
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loss = run_fwd_bwd(model, input_ids, label, criterion, zero_optim)
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assert torch.equal(torch_loss, loss)
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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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spawn(run_dist, world_size)
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if __name__ == '__main__':
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test_gpt(4)
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