mirror of https://github.com/hpcaitech/ColossalAI
aibig-modeldata-parallelismdeep-learningdistributed-computingfoundation-modelsheterogeneous-traininghpcinferencelarge-scalemodel-parallelismpipeline-parallelism
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75 lines
3.0 KiB
75 lines
3.0 KiB
from functools import partial |
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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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import colossalai |
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from colossalai.context import MOE_CONTEXT |
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from colossalai.engine.gradient_handler import MoeGradientHandler |
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from colossalai.nn import MoeLoss |
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from colossalai.testing import assert_equal_in_group, parameterize, rerun_if_address_is_in_use |
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from colossalai.utils import free_port |
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from colossalai.zero.legacy.init_ctx import ZeroInitContext |
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from colossalai.zero.legacy.shard_utils import BucketTensorShardStrategy, TensorShardStrategy |
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from colossalai.zero.legacy.sharded_model import ShardedModelV2 |
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from colossalai.zero.legacy.sharded_model._utils import cast_tensor_to_fp16 |
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from colossalai.zero.legacy.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 tests.test_moe.test_moe_zero_init import MoeModel |
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from tests.test_zero.common import CONFIG, check_grads_padding, run_fwd_bwd |
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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('hanging_param_model') |
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_, train_dataloader, _, optimizer_class, _ = get_components_func() |
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criterion = MoeLoss(aux_weight=0.01, loss_fn=torch.nn.CrossEntropyLoss) |
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with ZeroInitContext(target_device=torch.device('cuda', torch.cuda.current_device()), |
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shard_strategy=shard_strategy, |
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shard_param=True): |
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zero_model = MoeModel(checkpoint=True) |
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zero_model = ShardedModelV2(zero_model, shard_strategy) |
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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.colo_attr.is_replicated: |
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assert_equal_in_group(p.colo_attr.data_payload) |
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model = MoeModel(checkpoint=True).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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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_if_address_is_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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