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https://github.com/pytorch/pytorch.git
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758a0a88a2
This PR removes unnecessary `pass` statement. This is semanticly safe because the bytecode for the Python code does not change. Note that if there is a docstring in the function, a empty function does not need a `pass` statement as placeholder. Pull Request resolved: https://github.com/pytorch/pytorch/pull/133200 Approved by: https://github.com/malfet, https://github.com/eqy, https://github.com/kit1980
119 lines
2.5 KiB
Python
119 lines
2.5 KiB
Python
import os
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import sys
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import torch
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class Setup:
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def setup(self):
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raise NotImplementedError
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def shutdown(self):
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raise NotImplementedError
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class FileSetup:
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path = None
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def shutdown(self):
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if os.path.exists(self.path):
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os.remove(self.path)
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class EvalModeForLoadedModule(FileSetup):
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path = "dropout_model.pt"
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def setup(self):
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class Model(torch.jit.ScriptModule):
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def __init__(self) -> None:
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super().__init__()
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self.dropout = torch.nn.Dropout(0.1)
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@torch.jit.script_method
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def forward(self, x):
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x = self.dropout(x)
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return x
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model = Model()
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model = model.train()
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model.save(self.path)
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class SerializationInterop(FileSetup):
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path = "ivalue.pt"
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def setup(self):
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ones = torch.ones(2, 2)
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twos = torch.ones(3, 5) * 2
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value = (ones, twos)
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torch.save(value, self.path, _use_new_zipfile_serialization=True)
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# See testTorchSaveError in test/cpp/jit/tests.h for usage
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class TorchSaveError(FileSetup):
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path = "eager_value.pt"
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def setup(self):
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ones = torch.ones(2, 2)
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twos = torch.ones(3, 5) * 2
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value = (ones, twos)
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torch.save(value, self.path, _use_new_zipfile_serialization=False)
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class TorchSaveJitStream_CUDA(FileSetup):
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path = "saved_stream_model.pt"
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def setup(self):
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if not torch.cuda.is_available():
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return
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class Model(torch.nn.Module):
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def forward(self):
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s = torch.cuda.Stream()
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a = torch.rand(3, 4, device="cuda")
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b = torch.rand(3, 4, device="cuda")
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with torch.cuda.stream(s):
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is_stream_s = (
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torch.cuda.current_stream(s.device_index()).id() == s.id()
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)
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c = torch.cat((a, b), 0).to("cuda")
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s.synchronize()
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return is_stream_s, a, b, c
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model = Model()
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# Script the model and save
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script_model = torch.jit.script(model)
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torch.jit.save(script_model, self.path)
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tests = [
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EvalModeForLoadedModule(),
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SerializationInterop(),
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TorchSaveError(),
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TorchSaveJitStream_CUDA(),
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]
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def setup():
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for test in tests:
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test.setup()
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def shutdown():
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for test in tests:
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test.shutdown()
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if __name__ == "__main__":
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command = sys.argv[1]
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if command == "setup":
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setup()
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elif command == "shutdown":
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shutdown()
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