mirror of https://github.com/hpcaitech/ColossalAI
140 lines
5.1 KiB
Python
140 lines
5.1 KiB
Python
import pytest
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import torch
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from torch.testing import assert_close
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import colossalai
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from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
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from colossalai.zero import GeminiDDP
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from colossalai.zero.gemini.chunk import search_chunk_configuration
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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 ignore_the_first_parameter(model: torch.nn.Module):
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for name, param in model.named_parameters():
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print(f"parameter `{name}` is set ignored")
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GeminiDDP.set_params_to_ignore([param])
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return
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@parameterize('placement_config', PLACEMENT_CONFIGS)
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@parameterize('keep_gathered', [True, False])
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@parameterize('model_name', ['gpt2', 'bert'])
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def exam_state_dict(placement_config, keep_gathered, model_name: str):
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set_seed(431)
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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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model = model_builder()
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torch_model = model_builder()
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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_gathered
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model = GeminiDDP(model, config_dict, **placement_config, pin_memory=True)
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model.train()
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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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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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assert_close(value, temp_zero_value, rtol=1e-3, atol=1e-5)
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@parameterize('placement_config', PLACEMENT_CONFIGS)
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@parameterize('keep_gathered', [True, False])
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@parameterize('model_name', ['gpt2', 'bert'])
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def exam_load_state_dict(placement_config, keep_gathered, model_name: str):
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set_seed(431)
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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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model = model_builder()
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set_seed(451)
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torch_model = model_builder() # get a different model
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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_gathered
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model = GeminiDDP(model, config_dict, **placement_config, pin_memory=True)
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torch_dict = torch_model.state_dict()
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model.load_state_dict(torch_dict, strict=False)
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zero_dict = model.state_dict(only_rank_0=False)
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for key, value in torch_dict.items():
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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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assert_close(value, temp_zero_value, rtol=1e-3, atol=1e-5)
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@parameterize('placement_config', PLACEMENT_CONFIGS)
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@parameterize('model_name', ['gpt2', 'bert'])
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def exam_state_dict_shard(placement_config, model_name: str):
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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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model = model_builder()
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model_size = sum(p.numel() * p.element_size() for p in model.parameters()) / 1024**2
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config_dict, *_ = search_chunk_configuration(model, search_range_m=1, search_interval=100)
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model = GeminiDDP(model, config_dict, **placement_config)
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model.train()
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zero_dict = model.state_dict(only_rank_0=False)
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accumulated_keys = set()
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# ensure number of shards > 1
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for shard, _ in model.state_dict_shard(max_shard_size=(model_size / 3), only_rank_0=False):
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for key, value in shard.items():
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assert key not in accumulated_keys, f"key `{key}` is duplicated."
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accumulated_keys.add(key)
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assert key in zero_dict, f"{key} not in ZeRO dictionary."
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assert torch.equal(value, zero_dict[key]), f"{key} not equal."
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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_state_dict()
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exam_load_state_dict()
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exam_state_dict_shard()
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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_zero_ddp(world_size):
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spawn(run_dist, world_size)
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if __name__ == '__main__':
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test_zero_ddp(1)
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