mirror of https://github.com/hpcaitech/ColossalAI
aibig-modeldata-parallelismdeep-learningdistributed-computingfoundation-modelsheterogeneous-traininghpcinferencelarge-scalemodel-parallelismpipeline-parallelism
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53 lines
1.9 KiB
53 lines
1.9 KiB
import torch |
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from colossalai.tensor import distspec, ColoParameter |
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from torch.nn import functional as F |
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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.testing import rerun_if_address_is_in_use |
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from colossalai.utils import free_port |
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from colossalai.tensor import TensorSpec, ComputePattern, ComputeSpec, DistSpecManager, ProcessGroup |
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from _utils import tensor_equal, tensor_shard_equal |
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def init_1d_col(weight, pg: ProcessGroup): |
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spec = TensorSpec(distspec.shard(pg, [-1], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D)) |
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with DistSpecManager.no_grad(): |
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weight.set_tensor_spec(spec) |
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def run_with_spec(spec_init_func): |
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pg = ProcessGroup(tp_degree=torch.distributed.get_world_size()) |
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model = torch.nn.EmbeddingBag(10, 4).cuda() |
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weight = ColoParameter(model.weight.clone()) |
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spec_init_func(weight, pg) |
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inputs = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9]).cuda() |
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offsets = torch.tensor([0, 4]).cuda() |
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out = model(inputs, offsets=offsets) |
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colo_out = F.embedding_bag(inputs, weight, offsets=offsets) |
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assert tensor_equal(out, colo_out) |
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grad = torch.rand_like(out) |
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out.backward(grad) |
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colo_out.backward(grad) |
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assert tensor_shard_equal(model.weight.grad, weight.grad, pg.tp_local_rank(), pg.tp_world_size()) |
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def run_dist(rank, world_size, port): |
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config = dict(parallel=dict(tensor=dict(mode="1d", size=world_size),)) |
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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_with_spec(init_1d_col) |
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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_embedding_bag_1d(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_embedding_bag_1d(4)
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