mirror of https://github.com/hpcaitech/ColossalAI
170 lines
6.6 KiB
Python
170 lines
6.6 KiB
Python
from functools import partial
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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 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.gemini.chunk import ChunkManager, init_chunk_manager, search_chunk_configuration
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from colossalai.gemini.gemini_mgr import GeminiManager
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from colossalai.nn.optimizer import HybridAdam
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from colossalai.nn.optimizer.zero_optimizer import ZeroOptimizer
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from colossalai.nn.parallel import ZeroDDP
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from colossalai.tensor import ColoParameter, ColoTensor
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from colossalai.testing import parameterize, rerun_if_address_is_in_use
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from colossalai.utils import free_port
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from colossalai.utils.cuda import get_current_device
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from colossalai.utils.model.colo_init_context import ColoInitContext, post_process_colo_init_ctx
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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 debug_print, set_seed
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# this model is large enough to slice to chunks
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TEST_MODELS = ['gpt2']
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# these models are too small, all parameters in these models are compacted into one chunk
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EXAMPLE_MODELS = ['albert', 'beit', 'bert', 'hanging_param_model', 'nested_model', 'repeated_computed_layers']
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def check_param(model: ZeroDDP, torch_model: torch.nn.Module):
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zero_dict = model.state_dict(only_rank_0=False)
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torch_dict = torch_model.state_dict()
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for key, value in torch_dict.items():
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# key is 'module.model.PARAMETER', so we truncate it
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key = key[7:]
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if key == 'model.lm_head.weight':
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continue
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assert key in zero_dict, "{} not in ZeRO dictionary.".format(key)
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temp_zero_value = zero_dict[key].to(device=value.device, dtype=value.dtype)
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# debug_print([0], "max range: ", key, torch.max(torch.abs(value - temp_zero_value)))
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assert_close(value, temp_zero_value, rtol=1e-3, atol=4e-3)
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@parameterize('placement_policy', ['cuda', 'cpu', 'auto', 'const'])
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@parameterize('model_name', TEST_MODELS)
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def exam_model_step(placement_policy, model_name: str):
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set_seed(42)
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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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torch_model = model_builder().cuda()
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amp_config = dict(opt_level='O2', keep_batchnorm_fp32=False, loss_scale=128)
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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=[dist.get_rank()])
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init_dev = get_current_device()
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with ColoInitContext(device=init_dev):
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model = model_builder()
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for torch_p, p in zip(torch_model.parameters(), model.parameters()):
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p.data.copy_(torch_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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if placement_policy != 'cuda':
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init_device = torch.device('cpu')
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else:
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init_device = None
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chunk_manager = ChunkManager(config_dict, init_device=init_device)
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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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optimizer = HybridAdam(model.parameters(), lr=1e-3)
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zero_optim = ZeroOptimizer(optimizer, model, initial_scale=128)
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model.eval()
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torch_model.eval()
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set_seed(dist.get_rank() * 3 + 128)
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for i, (input_ids, label) in enumerate(train_dataloader):
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if i > 2:
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break
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input_ids, label = input_ids.cuda(), label.cuda()
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zero_optim.zero_grad()
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torch_optim.zero_grad()
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torch_loss = run_fwd_bwd(torch_model, input_ids, label, criterion, torch_optim)
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loss = run_fwd_bwd(model, input_ids, label, criterion, zero_optim)
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assert_close(torch_loss, loss)
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zero_optim.step()
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torch_optim.step()
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check_param(model, torch_model)
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@parameterize('placement_policy', ['cuda', 'cpu', 'auto', 'const'])
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@parameterize('model_name', EXAMPLE_MODELS)
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def exam_tiny_example(placement_policy, model_name: str):
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set_seed(2008)
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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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torch_model = model_builder().cuda()
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amp_config = dict(opt_level='O2', keep_batchnorm_fp32=False, loss_scale=2)
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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=[dist.get_rank()])
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init_dev = get_current_device()
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with ColoInitContext(device=init_dev):
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model = model_builder()
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for torch_p, p in zip(torch_model.parameters(), model.parameters()):
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p.data.copy_(torch_p.data)
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chunk_manager = init_chunk_manager(model=model, init_device=get_current_device(), search_range_mb=1)
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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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optimizer = HybridAdam(model.parameters(), lr=1e-3)
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zero_optim = ZeroOptimizer(optimizer, model, initial_scale=2)
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model.eval()
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torch_model.eval()
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set_seed(dist.get_rank() * 3 + 128)
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for i, (input_ids, label) in enumerate(train_dataloader):
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if i > 2:
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break
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input_ids = input_ids.cuda()
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label = label.cuda()
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zero_optim.zero_grad()
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torch_optim.zero_grad()
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torch_loss = run_fwd_bwd(torch_model, input_ids, label, criterion, torch_optim)
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loss = run_fwd_bwd(model, input_ids, label, criterion, zero_optim)
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assert_close(torch_loss, loss, rtol=1.5e-6, atol=2e-5) # atol should be 2e-5 for torch lower than 1.12
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zero_optim.step()
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torch_optim.step()
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check_param(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_model_step()
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exam_tiny_example()
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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_optim(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_optim(1)
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