51 lines
1.5 KiB
Python
51 lines
1.5 KiB
Python
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import torch
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from bytecode import Bytecode, Instr
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class get_local(object):
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cache = {}
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is_activate = False
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def __init__(self, varname):
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self.varname = varname
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def __call__(self, func):
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if not type(self).is_activate:
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return func
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type(self).cache[func.__qualname__] = []
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c = Bytecode.from_code(func.__code__)
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extra_code = [
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Instr('STORE_FAST', '_res'),
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Instr('LOAD_FAST', self.varname),
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Instr('STORE_FAST', '_value'),
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Instr('LOAD_FAST', '_res'),
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Instr('LOAD_FAST', '_value'),
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Instr('BUILD_TUPLE', 2),
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Instr('STORE_FAST', '_result_tuple'),
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Instr('LOAD_FAST', '_result_tuple'),
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]
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c[-1:-1] = extra_code
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func.__code__ = c.to_code()
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def wrapper(*args, **kwargs):
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res, values = func(*args, **kwargs)
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if isinstance(values, torch.Tensor):
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type(self).cache[func.__qualname__].append(values.detach().cpu().numpy())
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elif isinstance(values, list): # list of Tensor
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type(self).cache[func.__qualname__].append([value.detach().cpu().numpy() for value in values])
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else:
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raise NotImplementedError
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return res
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return wrapper
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@classmethod
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def clear(cls):
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for key in cls.cache.keys():
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cls.cache[key] = []
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@classmethod
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def activate(cls):
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cls.is_activate = True
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