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https://github.com/hpcaitech/ColossalAI.git
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4f59693207
* [fx] compute memory stat and flop count for MetaInfoProp. * [fx] modify node attribute. * [fx] modify ckpt_chen. * [fx] fix compatibility. * [fx] fix import error. * [fx] skip test for MetaInfoProp. * [fx] skip test for MetaInfoProp. * [fx] skip test for MetaInfoProp. * [fx] skip test for MetaInfoProp. * [fx] skip if torch 1.11.0. * [fx] recover MetaInfoProp support for PyTorch 1.11. * [fx] provide a stable but not accurate enough version of profiler. * [fx] provide a stable but not accurate enough version of profiler. * [fx] fix compatibility in tests. * [fx] fix compatibility in tests. * [fx] fix compatibility in tests. * [fx] fix compatibility in tests. * [fx] fix compatibility in tests. * [fx] fix compatibility in tests. * [fx] fix compatibility in tests. * [fx] fix compatibility in tests. * [fx] fix compatibility in tests. * [fx] fix compatibility in tests. * [fx] fix import error.
50 lines
1.6 KiB
Python
50 lines
1.6 KiB
Python
import torch
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import torch.nn as nn
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import colossalai
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import colossalai.nn as col_nn
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from torch.fx import symbolic_trace
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from colossalai.fx.passes.meta_info_prop import MetaInfoProp
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from colossalai.fx.passes.adding_split_node_pass import split_with_split_nodes_pass, uniform_split_pass
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from colossalai.fx.passes.utils import get_comm_size
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from colossalai import META_COMPATIBILITY
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import pytest
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MODEL_DIM = 16
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BATCH_SIZE = 8
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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):
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super().__init__()
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self.linear1 = torch.nn.Linear(dim, dim)
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self.linear2 = torch.nn.Linear(dim, dim)
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self.linear3 = torch.nn.Linear(dim, dim)
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self.linear4 = torch.nn.Linear(dim, dim)
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def forward(self, x):
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x = self.linear1(x)
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x = self.linear2(x)
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x = self.linear3(x)
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x = self.linear4(x)
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return x
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@pytest.mark.skipif(not META_COMPATIBILITY, reason='torch version is lower than 1.12.0')
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def test_comm_size_compute():
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model = MLP(MODEL_DIM)
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input_sample = torch.rand(BATCH_SIZE, MODEL_DIM, device='meta')
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gm = symbolic_trace(model)
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MetaInfoProp(gm).run(input_sample)
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annotated_model = uniform_split_pass(gm, PIPELINE_SIZE)
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split_model, split_submodules = split_with_split_nodes_pass(annotated_model)
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submodule_list = list(split_model.children())
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comm_size = get_comm_size(submodule_list[0], submodule_list[1])
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# the shape of tensor send from partition 0 to partition 1 is (8, 16)
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assert comm_size == 128
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if __name__ == '__main__':
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test_comm_size_compute()
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