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@ -152,6 +152,13 @@ class HardSwishJitAutoFn(torch.autograd.Function):
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x = ctx.saved_tensors[0]
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return hard_swish_jit_bwd(x, grad_output)
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@staticmethod
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def symbolic(g, self):
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input = g.op("Add", self, g.op('Constant', value_t=torch.tensor(3, dtype=torch.float)))
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hardtanh_ = g.op("Clip", input, g.op('Constant', value_t=torch.tensor(0, dtype=torch.float)), g.op('Constant', value_t=torch.tensor(6, dtype=torch.float)))
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hardtanh_ = g.op("Div", hardtanh_, g.op('Constant', value_t=torch.tensor(6, dtype=torch.float)))
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return g.op("Mul", self, hardtanh_)
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def hard_swish_me(x, inplace=False):
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return HardSwishJitAutoFn.apply(x)
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