mirror of https://github.com/hpcaitech/ColossalAI
79 lines
3.1 KiB
Python
79 lines
3.1 KiB
Python
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.testing import parameterize, rerun_on_exception
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from colossalai.utils import free_port
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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 colossalai.zero.sharded_model import ShardedModelV2
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from colossalai.zero.sharded_model._utils import cast_tensor_to_fp16
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from colossalai.zero.sharded_model.utils import col_model_deepcopy
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from tests.components_to_test.registry import non_distributed_component_funcs
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from colossalai.engine.gradient_handler import MoeGradientHandler
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from colossalai.context import MOE_CONTEXT
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from colossalai.testing import assert_equal_in_group
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from tests.test_zero_data_parallel.common import CONFIG, check_grads_padding, run_fwd_bwd
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from tests.test_moe.test_moe_zero_init import MoeModel
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@parameterize("enable_autocast", [False])
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@parameterize("shard_strategy_class", [TensorShardStrategy, BucketTensorShardStrategy])
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def run_model_test(enable_autocast, shard_strategy_class):
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shard_strategy = shard_strategy_class()
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get_components_func = non_distributed_component_funcs.get_callable('no_leaf_module')
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_, train_dataloader, _, _, criterion = get_components_func()
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rm_torch_payload_on_the_fly = False
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with ZeroInitContext(target_device=torch.cuda.current_device(),
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shard_strategy=shard_strategy,
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shard_param=True,
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rm_torch_payload_on_the_fly=rm_torch_payload_on_the_fly):
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zero_model = MoeModel()
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zero_model = ShardedModelV2(zero_model, shard_strategy, use_memory_tracer=True)
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# check whether parameters are identical in ddp
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for name, p in zero_model.named_parameters():
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if not p.colo_attr.param_is_sharded and p.is_replicated:
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assert_equal_in_group(p.data)
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model = MoeModel().half()
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col_model_deepcopy(zero_model, model)
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model = model.cuda()
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grad_handler = MoeGradientHandler(model)
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for i, (data, label) in enumerate(train_dataloader):
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if i > 5:
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break
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data, label = cast_tensor_to_fp16(data).cuda(), label.cuda()
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run_fwd_bwd(model, data, label, criterion, enable_autocast)
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run_fwd_bwd(zero_model, data, label, criterion, enable_autocast)
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grad_handler.handle_gradient()
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check_grads_padding(model, zero_model, loose=True)
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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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MOE_CONTEXT.setup(seed=42)
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MOE_CONTEXT.reset_loss()
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run_model_test()
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@pytest.mark.dist
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@pytest.mark.parametrize("world_size", [2])
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@rerun_on_exception(exception_type=mp.ProcessRaisedException, pattern=".*Address already in use.*")
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def test_moe_zero_model(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_moe_zero_model(world_size=2)
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