[Retiarii] refactor based on the new launch approach #3185
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| @@ -52,7 +52,7 @@ def __init__(self, experiment_id: str): | |||
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| def connect(self) -> BufferedIOBase: | |||
| conn, _ = self._socket.accept() | |||
| self.file = conn.makefile('w+b') | |||
| self.file = conn.makefile('rwb') | |||
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#3183 @liuzhe-lz can answer this question
| while True: | ||
| time.sleep(10) | ||
| status = self.get_status() | ||
| if status in ['ERROR', 'STOPPED', 'NO_MORE_TRIAL']: |
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What if the experiment is done?
| if _records is not None: | ||
| assert name not in _records, '{} already in _records'.format(name) | ||
| _records[name] = value | ||
| __all__ = [ |
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Is this nn.__all__ plus a few others?
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yes, ours, for example placeholder
| BatchNorm2d = wrap_module(nn.BatchNorm2d) | ||
| ReLU = wrap_module(nn.ReLU) | ||
| Dropout = wrap_module(nn.Dropout) | ||
| Linear = wrap_module(nn.Linear) | ||
| MaxPool2d = wrap_module(nn.MaxPool2d) | ||
| AvgPool2d = wrap_module(nn.AvgPool2d) | ||
| Identity = wrap_module(nn.Identity) | ||
| AdaptiveAvgPool2d = wrap_module(nn.AdaptiveAvgPool2d)''' | ||
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| Identity = wrap_module(nn.Identity) |
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Can we refactor it into a for loop?
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i think this way is more clear and easy to maintain
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because not all the modules are included, for example, loss modules. and different versions of pytorch have different members in nn.module. I will handle different versions later
nni/retiarii/converter/graph_gen.py
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| @@ -463,6 +466,9 @@ def convert_module(script_module, module, module_name, ir_model): | |||
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| ir_graph._register() | |||
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| if id(module) not in modules_arg: | |||
| raise RuntimeError(f'{original_type_name} arguments are not recorded, \ | |||
| you may forget to decorate this class with @register_module()') | |||
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you might have forgotten
withstatement