mirror of https://github.com/hpcaitech/ColossalAI
85 lines
1.8 KiB
Python
85 lines
1.8 KiB
Python
import pytest
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from torch import nn
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import torch
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from colossalai.tensor import ColoTensor
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from colossalai.tensor.graph import GraphContext
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import gc
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class SimpleNet(nn.Module):
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def __init__(self) -> None:
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super().__init__()
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self.proj1 = nn.Linear(4, 8)
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self.proj2 = nn.Linear(8, 4)
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self.proj3 = nn.Linear(4, 4)
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self.proj4 = nn.Linear(4, 4)
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def forward(self, x):
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x = self.proj1(x)
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x = self.proj2(x)
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x = self.proj3(x)
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x = self.proj4(x)
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return x
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def _visit_graph(start_node):
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if start_node is None:
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return
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start_node.print()
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post_node_list = start_node.post_nodes
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for node in post_node_list:
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_visit_graph(node)
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def _get_tensors():
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for obj in gc.get_objects():
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try:
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if torch.is_tensor(obj):
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yield obj
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except Exception as e:
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print('A trivial exception occured: {}'.format(e))
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def _count_tensors():
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cnt = 0
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for t in _get_tensors():
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cnt += 1
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return cnt
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def count_tensors(use_colossal):
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model = SimpleNet()
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model.eval()
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with torch.no_grad():
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if use_colossal:
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colo_input = ColoTensor.from_torch_tensor(torch.randn(4))
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graph_ctx = GraphContext()
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with graph_ctx:
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output = model(colo_input)
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output = model(colo_input)
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ret = _count_tensors()
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_visit_graph(graph_ctx.graph_nodes[0])
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del graph_ctx
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return ret
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else:
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input_t = torch.randn(4)
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output = model(input_t)
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output = model(input_t)
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return _count_tensors()
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@pytest.mark.skip
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# FIXME(ver217)
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def test_check_activation_tensors():
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assert count_tensors(False) == count_tensors(True)
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if __name__ == "__main__":
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count_tensors(True)
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