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75 lines
3.0 KiB
75 lines
3.0 KiB
#!/usr/bin/env python
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# -*- encoding: utf-8 -*-
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from functools import partial
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import colossalai
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import pytest
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import torch
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import torch.multiprocessing as mp
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from colossalai.logging import get_dist_logger
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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.gemini.memory_tracer.model_data_memtracer import \
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colo_model_mem_usage
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from colossalai.utils.memory import colo_device_memory_used
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from colossalai.zero.init_ctx import ZeroInitContext
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from colossalai.zero.shard_utils import (BucketTensorShardStrategy, TensorShardStrategy)
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from tests.components_to_test.registry import non_distributed_component_funcs
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from common import CONFIG
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@parameterize("init_device_type", ['cpu', 'cuda'])
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@parameterize("shard_strategy_class", [TensorShardStrategy, BucketTensorShardStrategy])
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def run_model_test(init_device_type, shard_strategy_class):
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logger = get_dist_logger("test_zero_init")
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for get_components_func in non_distributed_component_funcs:
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model_builder, _, _, _, _ = get_components_func()
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if init_device_type == 'cuda':
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init_device = get_current_device()
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elif init_device_type == 'cpu':
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init_device = torch.device("cpu")
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else:
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continue
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model_numel_tensor = torch.zeros(1, dtype=torch.int)
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with ZeroInitContext(target_device=init_device,
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shard_strategy=shard_strategy_class(),
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shard_param=True,
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model_numel_tensor=model_numel_tensor):
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model = model_builder(checkpoint=True)
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for param in model.parameters():
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assert hasattr(param, 'colo_attr')
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assert param.colo_attr.sharded_data_tensor.dtype == torch.half
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assert param.colo_attr.sharded_data_tensor.is_sharded
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assert param.colo_attr.data_payload.device.type == init_device.type, \
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f'{param.colo_attr.data_payload.device.type} vs. {init_device.type}'
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cuda_mem_use, _ = colo_model_mem_usage(model)
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model_data_cuda_mem_MB = cuda_mem_use / 1e6
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logger.info(f"Existing ZeRO Context.\nModel Data CUDA Memory {model_data_cuda_mem_MB} MB", ranks=[0])
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sys_cuda_mem_MB = colo_device_memory_used(get_current_device()) / 1e6
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logger.info(f"System CUDA Memory Usage {sys_cuda_mem_MB} MB", ranks=[0])
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logger.info(f"Model Number Parameter {model_numel_tensor.numpy()[0]/1e6} M", ranks=[0])
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def run_dist(rank, world_size, port):
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colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
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run_model_test()
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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_init_context(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_zero_init_context(4)
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