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// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
//
// NVIDIA CORPORATION and its licensors retain all intellectual property
// and proprietary rights in and to this software, related documentation
// and any modifications thereto. Any use, reproduction, disclosure or
// distribution of this software and related documentation without an express
// license agreement from NVIDIA CORPORATION is strictly prohibited.
#include <c10/util/Half.h>
#include "bias_act.h"
//------------------------------------------------------------------------
// Helpers.
template <class T> struct InternalType;
template <> struct InternalType<double> { typedef double scalar_t; };
template <> struct InternalType<float> { typedef float scalar_t; };
template <> struct InternalType<c10::Half> { typedef float scalar_t; };
//------------------------------------------------------------------------
// CUDA kernel.
template <class T, int A>
__global__ void bias_act_kernel(bias_act_kernel_params p)
{
typedef typename InternalType<T>::scalar_t scalar_t;
int G = p.grad;
scalar_t alpha = (scalar_t)p.alpha;
scalar_t gain = (scalar_t)p.gain;
scalar_t clamp = (scalar_t)p.clamp;
scalar_t one = (scalar_t)1;
scalar_t two = (scalar_t)2;
scalar_t expRange = (scalar_t)80;
scalar_t halfExpRange = (scalar_t)40;
scalar_t seluScale = (scalar_t)1.0507009873554804934193349852946;
scalar_t seluAlpha = (scalar_t)1.6732632423543772848170429916717;
// Loop over elements.
int xi = blockIdx.x * p.loopX * blockDim.x + threadIdx.x;
for (int loopIdx = 0; loopIdx < p.loopX && xi < p.sizeX; loopIdx++, xi += blockDim.x)
{
// Load.
scalar_t x = (scalar_t)((const T*)p.x)[xi];
scalar_t b = (p.b) ? (scalar_t)((const T*)p.b)[(xi / p.stepB) % p.sizeB] : 0;
scalar_t xref = (p.xref) ? (scalar_t)((const T*)p.xref)[xi] : 0;
scalar_t yref = (p.yref) ? (scalar_t)((const T*)p.yref)[xi] : 0;
scalar_t dy = (p.dy) ? (scalar_t)((const T*)p.dy)[xi] : one;
scalar_t yy = (gain != 0) ? yref / gain : 0;
scalar_t y = 0;
// Apply bias.
((G == 0) ? x : xref) += b;
// linear
if (A == 1)
{
if (G == 0) y = x;
if (G == 1) y = x;
}
// relu
if (A == 2)
{
if (G == 0) y = (x > 0) ? x : 0;
if (G == 1) y = (yy > 0) ? x : 0;
}
// lrelu
if (A == 3)
{
if (G == 0) y = (x > 0) ? x : x * alpha;
if (G == 1) y = (yy > 0) ? x : x * alpha;
}
// tanh
if (A == 4)
{
if (G == 0) { scalar_t c = exp(x); scalar_t d = one / c; y = (x < -expRange) ? -one : (x > expRange) ? one : (c - d) / (c + d); }
if (G == 1) y = x * (one - yy * yy);
if (G == 2) y = x * (one - yy * yy) * (-two * yy);
}
// sigmoid
if (A == 5)
{
if (G == 0) y = (x < -expRange) ? 0 : one / (exp(-x) + one);
if (G == 1) y = x * yy * (one - yy);
if (G == 2) y = x * yy * (one - yy) * (one - two * yy);
}
// elu
if (A == 6)
{
if (G == 0) y = (x >= 0) ? x : exp(x) - one;
if (G == 1) y = (yy >= 0) ? x : x * (yy + one);
if (G == 2) y = (yy >= 0) ? 0 : x * (yy + one);
}
// selu
if (A == 7)
{
if (G == 0) y = (x >= 0) ? seluScale * x : (seluScale * seluAlpha) * (exp(x) - one);
if (G == 1) y = (yy >= 0) ? x * seluScale : x * (yy + seluScale * seluAlpha);
if (G == 2) y = (yy >= 0) ? 0 : x * (yy + seluScale * seluAlpha);
}
// softplus
if (A == 8)
{
if (G == 0) y = (x > expRange) ? x : log(exp(x) + one);
if (G == 1) y = x * (one - exp(-yy));
if (G == 2) { scalar_t c = exp(-yy); y = x * c * (one - c); }
}
// swish
if (A == 9)
{
if (G == 0)
y = (x < -expRange) ? 0 : x / (exp(-x) + one);
else
{
scalar_t c = exp(xref);
scalar_t d = c + one;
if (G == 1)
y = (xref > halfExpRange) ? x : x * c * (xref + d) / (d * d);
else
y = (xref > halfExpRange) ? 0 : x * c * (xref * (two - d) + two * d) / (d * d * d);
yref = (xref < -expRange) ? 0 : xref / (exp(-xref) + one) * gain;
}
}
// Apply gain.
y *= gain * dy;
// Clamp.
if (clamp >= 0)
{
if (G == 0)
y = (y > -clamp & y < clamp) ? y : (y >= 0) ? clamp : -clamp;
else
y = (yref > -clamp & yref < clamp) ? y : 0;
}
// Store.
((T*)p.y)[xi] = (T)y;
}
}
//------------------------------------------------------------------------
// CUDA kernel selection.
template <class T> void* choose_bias_act_kernel(const bias_act_kernel_params& p)
{
if (p.act == 1) return (void*)bias_act_kernel<T, 1>;
if (p.act == 2) return (void*)bias_act_kernel<T, 2>;
if (p.act == 3) return (void*)bias_act_kernel<T, 3>;
if (p.act == 4) return (void*)bias_act_kernel<T, 4>;
if (p.act == 5) return (void*)bias_act_kernel<T, 5>;
if (p.act == 6) return (void*)bias_act_kernel<T, 6>;
if (p.act == 7) return (void*)bias_act_kernel<T, 7>;
if (p.act == 8) return (void*)bias_act_kernel<T, 8>;
if (p.act == 9) return (void*)bias_act_kernel<T, 9>;
return NULL;
}
//------------------------------------------------------------------------
// Template specializations.
template void* choose_bias_act_kernel<double> (const bias_act_kernel_params& p);
template void* choose_bias_act_kernel<float> (const bias_act_kernel_params& p);
template void* choose_bias_act_kernel<c10::Half> (const bias_act_kernel_params& p);
//------------------------------------------------------------------------
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