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46 lines
1.3 KiB
46 lines
1.3 KiB
import torch
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###### BIAS GELU FUSION/ NO AUTOGRAD ################
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# 1/sqrt(2*pi)-> 0.3989423
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# 1/sqrt(2) -> 0.70710678
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# sqrt(2/pi) -> 0.79788456
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# this function is tanh approximation of gelu
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# actual gelu is:
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# x * 0.5 * (1.0 + torch.erf(x * 0.70710678))
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@torch.jit.script
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def bias_gelu(bias, y):
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x = bias + y
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return x * 0.5 * (1.0 + torch.tanh(0.79788456 * x * (1 + 0.044715 * x * x)))
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# gradient of tanh approximation of gelu
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# gradient of actual gelu is:
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# 0.5 * (1. + torch.erf(x * 0.70710678)) + 0.3989423 * x * torch.exp(-0.5 * x * x)
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@torch.jit.script
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def bias_gelu_back(g, bias, y):
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x = bias + y
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tanh_out = torch.tanh(0.79788456 * x * (1 + 0.044715 * x * x))
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# sqrt(2/pi) * 3 * 0.044715 -> 0.1070322243
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ff = 0.5 * x * ((1 - tanh_out * tanh_out) * (0.79788456 + 0.1070322243 * x * x)) + 0.5 * (1 + tanh_out)
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return ff * g
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class GeLUFunction(torch.autograd.Function):
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@staticmethod
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# bias is an optional argument
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def forward(ctx, input, bias):
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ctx.save_for_backward(input, bias)
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return bias_gelu(bias, input)
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@staticmethod
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def backward(ctx, grad_output):
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input, bias = ctx.saved_tensors
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tmp = bias_gelu_back(grad_output, bias, input)
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return tmp, tmp
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bias_gelu_impl = GeLUFunction.apply
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