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8fce9a09cd
Apply parts of pyupgrade to torch (starting with the safest changes). This PR only does two things: removes the need to inherit from object and removes unused future imports. Pull Request resolved: https://github.com/pytorch/pytorch/pull/94308 Approved by: https://github.com/ezyang, https://github.com/albanD
44 lines
1.9 KiB
Python
44 lines
1.9 KiB
Python
import torch
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class WrapperModule:
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""" Wraps the instance of wrapped_type.
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For graph_mode traces the instance of wrapped_type.
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Randomaly initializes num_params tensors with single float element.
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Args:
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wrapped_type:
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- Object type to be wrapped.
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Expects the wrapped_type to:
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- be constructed with pt_fn specified in module_config.
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- provide forward method that takes module_config.num_params args.
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module_config:
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- Specified pt_fn to construct wrapped_type with, whether graph_mode
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is enabled, and number of parameters wrapped_type's forward method
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takes.
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debug:
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- Whether debug mode is enabled.
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save:
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- In graph mode, whether graph is to be saved.
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"""
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def __init__(self, wrapped_type, module_config, debug, save=False):
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pt_fn = module_config.pt_fn
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self.module = wrapped_type(pt_fn)
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self.tensor_inputs = []
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self.module_name = wrapped_type.__name__
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for _ in range(module_config.num_params):
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self.tensor_inputs.append(torch.randn(1))
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if module_config.graph_mode:
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self.module = torch.jit.trace(self.module, self.tensor_inputs)
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if save:
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file_name = self.module_name + "_" + pt_fn.__name__ + ".pt"
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torch.jit.save(self.module, file_name)
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print("Generated graph is saved in {}".format(file_name))
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print("Benchmarking module {} with fn {}: Graph mode:{}".format(self.module_name, pt_fn.__name__, module_config.graph_mode))
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if (debug and isinstance(self.module, torch.jit.ScriptModule)):
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print(self.module.graph)
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print(self.module.code)
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def forward(self, niters):
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with torch.no_grad():
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for _ in range(niters):
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self.module.forward(*self.tensor_inputs)
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