mirror of https://github.com/hpcaitech/ColossalAI
63 lines
1.6 KiB
Python
63 lines
1.6 KiB
Python
import torch
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from torch.fx import GraphModule
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from torch.utils.checkpoint import checkpoint
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from colossalai.fx import ColoTracer
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from colossalai.testing import clear_cache_before_run
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class MLP(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.linear1 = torch.nn.Linear(4, 4)
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self.linear2 = torch.nn.Linear(4, 4)
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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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return x
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# Simple module for demonstration
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class MyModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.mlp_1 = MLP()
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self.mlp_2 = MLP()
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self.output = torch.nn.Linear(4, 4)
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def forward(self, x):
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x = checkpoint(self.mlp_1, x)
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x = checkpoint(self.mlp_2, x)
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x = self.output(x)
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return x
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@clear_cache_before_run()
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def test_activation_checkpoint_annotation():
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module = MyModule()
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# test tracing with activation checkpoint
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tracer = ColoTracer(trace_act_ckpt=True)
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graph = tracer.trace(module)
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gm = GraphModule(module, graph)
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for node in gm.graph.nodes:
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if node.name in ["mlp_1_linear1", "mlp_1_linear2"]:
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assert node.meta.get("activation_checkpoint", -1) == 0
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for node in gm.graph.nodes:
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if node.name in ["mlp_2_linear1", "mlp_2_linear2"]:
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assert node.meta.get("activation_checkpoint", -1) == 1
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tracer = ColoTracer(trace_act_ckpt=False)
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graph = tracer.trace(module)
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gm = GraphModule(module, graph)
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for node in gm.graph.nodes:
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assert not hasattr(node, "activation_checkpoint")
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if __name__ == "__main__":
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test_activation_checkpoint_annotation()
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