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60 lines
1.7 KiB
60 lines
1.7 KiB
import pytest
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import torch
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from colossalai.pipeline.pipelinable import PipelinableContext
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from colossalai.testing import rerun_if_address_is_in_use, rerun_on_exception, spawn
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NUM_CHUNKS = 1
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PIPELINE_SIZE = 2
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class MLP(torch.nn.Module):
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def __init__(self, dim: int = 256):
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super().__init__()
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intermediate_dim = dim * 4
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self.dense_1 = torch.nn.Linear(dim, intermediate_dim)
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self.activation = torch.nn.GELU()
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self.dense_2 = torch.nn.Linear(intermediate_dim, dim)
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self.dropout = torch.nn.Dropout(0.1)
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def forward(self, x):
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x = self.dense_1(x)
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x = self.activation(x)
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x = self.dense_2(x)
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x = self.dropout(x)
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return x
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def run_pipelinable(rank, world_size, port):
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pipelinable = PipelinableContext()
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with pipelinable:
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model = MLP()
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assert pipelinable.policy == "balanced"
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pipelinable.policy = "uniform"
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assert pipelinable.policy == "uniform"
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pipelinable.to_layer_list()
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assert pipelinable.layers_count == len(list(model.children()))
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pipeline_model_part_0 = pipelinable.partition(NUM_CHUNKS, PIPELINE_SIZE, 0)
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assert isinstance(pipeline_model_part_0, torch.nn.Module)
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pipeline_model_part_1 = pipelinable.partition(NUM_CHUNKS, PIPELINE_SIZE, 1)
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assert isinstance(pipeline_model_part_1, torch.nn.Module)
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layers_count_in_part_0 = len(list(pipeline_model_part_0._module_list))
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layers_count_in_part_1 = len(list(pipeline_model_part_1._module_list))
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assert layers_count_in_part_0 + layers_count_in_part_1 == pipelinable.layers_count
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@pytest.mark.skip(reason="this is useless")
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@rerun_if_address_is_in_use()
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def test_pipelinable():
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spawn(run_pipelinable, 1)
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if __name__ == '__main__':
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test_pipelinable()
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