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Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/48340 This changes the context managed classes from using a decorator to define them to using inheritance. Inheritance allows the python static type checking to work correctly. ``` context.define_context() class Bar(object): ... context.define_context(allow_default=True) class Foo(object): ... ``` becomes ``` class Foo(context.Managed): ... class Bar(context.DefaultManaged): ... ``` Behavior differences: * arg_name has been removed since it's not used anywhere * classes need to call `super()` in `__enter__/__exit__` methods if they override (none do) This also defines a context.pyi file to add types for python3. python2 support should not be affected Test Plan: ci buck test //caffe2/caffe2/python:context_test //caffe2/caffe2/python:checkpoint_test Reviewed By: dongyuzheng Differential Revision: D25133469 fbshipit-source-id: 16368bf723eeb6ce3308d6827f5ac5e955b4e29a
54 lines
1.4 KiB
Python
54 lines
1.4 KiB
Python
## @package optimizer_context
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# Module caffe2.python.optimizer_context
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from caffe2.python import context
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from caffe2.python.modifier_context import (
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ModifierContext, UseModifierBase)
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DEFAULT_OPTIM = 'DEFAULT'
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class OptimizerContext(ModifierContext, context.DefaultManaged):
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"""
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provide context to allow param_info to have different optimizers
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"""
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def has_optimizer(self, name):
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return self._has_modifier(name)
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def get_optimizer(self, name):
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assert self.has_optimizer(name), (
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"{} optimizer is not provided!".format(name))
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return self._get_modifier(name)
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class UseOptimizer(UseModifierBase):
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'''
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context class to allow setting the current context.
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Example usage with brew:
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- with UseOptimizer(optim):
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brew.func
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- with UseOptimizer({'WEIGHT': weight_optim}):
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brew.func
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- with UseOptimizer({'DEFAULT': optim, 'BIAS': bias_optim,
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'WEIGHT': weight_optim}):
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brew.func
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- with UseOptimizer(optim1):
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brew.func
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with UseOptimizer(optim2):
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brew.func
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Example usage with layer:
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optimizers = {'optim1': optim1, 'optim2': optim2}
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with Optimizers(optimizers):
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optim = OptimizerContext.current().get_optimizer('optim1')
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layer(optim=optim)
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'''
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def _context_class(self):
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return OptimizerContext
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