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// Copyright (c) SenseTime Research. All rights reserved.
// Copyright (c) 2019, NVIDIA Corporation. All rights reserved.
//
// This work is made available under the Nvidia Source Code License-NC.
// To view a copy of this license, visit
// https://nvlabs.github.io/stylegan2/license.html
#define EIGEN_USE_GPU
#define __CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS__
#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/shape_inference.h"
#include <stdio.h>
using namespace tensorflow;
using namespace tensorflow::shape_inference;
#define OP_CHECK_CUDA_ERROR(CTX, CUDA_CALL) do { cudaError_t err = CUDA_CALL; OP_REQUIRES(CTX, err == cudaSuccess, errors::Internal(cudaGetErrorName(err))); } while (false)
//------------------------------------------------------------------------
// CUDA kernel.
template <class T>
struct FusedBiasActKernelParams
{
const T* x; // [sizeX]
const T* b; // [sizeB] or NULL
const T* ref; // [sizeX] or NULL
T* y; // [sizeX]
int grad;
int axis;
int act;
float alpha;
float gain;
int sizeX;
int sizeB;
int stepB;
int loopX;
};
template <class T>
static __global__ void FusedBiasActKernel(const FusedBiasActKernelParams<T> p)
{
const float expRange = 80.0f;
const float halfExpRange = 40.0f;
const float seluScale = 1.0507009873554804934193349852946f;
const float seluAlpha = 1.6732632423543772848170429916717f;
// 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 and apply bias.
float x = (float)p.x[xi];
if (p.b)
x += (float)p.b[(xi / p.stepB) % p.sizeB];
float ref = (p.ref) ? (float)p.ref[xi] : 0.0f;
if (p.gain != 0.0f & p.act != 9)
ref /= p.gain;
// Evaluate activation func.
float y;
switch (p.act * 10 + p.grad)
{
// linear
default:
case 10: y = x; break;
case 11: y = x; break;
case 12: y = 0.0f; break;
// relu
case 20: y = (x > 0.0f) ? x : 0.0f; break;
case 21: y = (ref > 0.0f) ? x : 0.0f; break;
case 22: y = 0.0f; break;
// lrelu
case 30: y = (x > 0.0f) ? x : x * p.alpha; break;
case 31: y = (ref > 0.0f) ? x : x * p.alpha; break;
case 32: y = 0.0f; break;
// tanh
case 40: { float c = expf(x); float d = 1.0f / c; y = (x < -expRange) ? -1.0f : (x > expRange) ? 1.0f : (c - d) / (c + d); } break;
case 41: y = x * (1.0f - ref * ref); break;
case 42: y = x * (1.0f - ref * ref) * (-2.0f * ref); break;
// sigmoid
case 50: y = (x < -expRange) ? 0.0f : 1.0f / (expf(-x) + 1.0f); break;
case 51: y = x * ref * (1.0f - ref); break;
case 52: y = x * ref * (1.0f - ref) * (1.0f - 2.0f * ref); break;
// elu
case 60: y = (x >= 0.0f) ? x : expf(x) - 1.0f; break;
case 61: y = (ref >= 0.0f) ? x : x * (ref + 1.0f); break;
case 62: y = (ref >= 0.0f) ? 0.0f : x * (ref + 1.0f); break;
// selu
case 70: y = (x >= 0.0f) ? seluScale * x : (seluScale * seluAlpha) * (expf(x) - 1.0f); break;
case 71: y = (ref >= 0.0f) ? x * seluScale : x * (ref + seluScale * seluAlpha); break;
case 72: y = (ref >= 0.0f) ? 0.0f : x * (ref + seluScale * seluAlpha); break;
// softplus
case 80: y = (x > expRange) ? x : logf(expf(x) + 1.0f); break;
case 81: y = x * (1.0f - expf(-ref)); break;
case 82: { float c = expf(-ref); y = x * c * (1.0f - c); } break;
// swish
case 90: y = (x < -expRange) ? 0.0f : x / (expf(-x) + 1.0f); break;
case 91: { float c = expf(ref); float d = c + 1.0f; y = (ref > halfExpRange) ? x : x * c * (ref + d) / (d * d); } break;
case 92: { float c = expf(ref); float d = c + 1.0f; y = (ref > halfExpRange) ? 0.0f : x * c * (ref * (2.0f - d) + 2.0f * d) / (d * d * d); } break;
}
// Apply gain and store.
p.y[xi] = (T)(y * p.gain);
}
}
//------------------------------------------------------------------------
// TensorFlow op.
template <class T>
struct FusedBiasActOp : public OpKernel
{
FusedBiasActKernelParams<T> m_attribs;
FusedBiasActOp(OpKernelConstruction* ctx) : OpKernel(ctx)
{
memset(&m_attribs, 0, sizeof(m_attribs));
OP_REQUIRES_OK(ctx, ctx->GetAttr("grad", &m_attribs.grad));
OP_REQUIRES_OK(ctx, ctx->GetAttr("axis", &m_attribs.axis));
OP_REQUIRES_OK(ctx, ctx->GetAttr("act", &m_attribs.act));
OP_REQUIRES_OK(ctx, ctx->GetAttr("alpha", &m_attribs.alpha));
OP_REQUIRES_OK(ctx, ctx->GetAttr("gain", &m_attribs.gain));
OP_REQUIRES(ctx, m_attribs.grad >= 0, errors::InvalidArgument("grad must be non-negative"));
OP_REQUIRES(ctx, m_attribs.axis >= 0, errors::InvalidArgument("axis must be non-negative"));
OP_REQUIRES(ctx, m_attribs.act >= 0, errors::InvalidArgument("act must be non-negative"));
}
void Compute(OpKernelContext* ctx)
{
FusedBiasActKernelParams<T> p = m_attribs;
cudaStream_t stream = ctx->eigen_device<Eigen::GpuDevice>().stream();
const Tensor& x = ctx->input(0); // [...]
const Tensor& b = ctx->input(1); // [sizeB] or [0]
const Tensor& ref = ctx->input(2); // x.shape or [0]
p.x = x.flat<T>().data();
p.b = (b.NumElements()) ? b.flat<T>().data() : NULL;
p.ref = (ref.NumElements()) ? ref.flat<T>().data() : NULL;
OP_REQUIRES(ctx, b.NumElements() == 0 || m_attribs.axis < x.dims(), errors::InvalidArgument("axis out of bounds"));
OP_REQUIRES(ctx, b.dims() == 1, errors::InvalidArgument("b must have rank 1"));
OP_REQUIRES(ctx, b.NumElements() == 0 || b.NumElements() == x.dim_size(m_attribs.axis), errors::InvalidArgument("b has wrong number of elements"));
OP_REQUIRES(ctx, ref.NumElements() == ((p.grad == 0) ? 0 : x.NumElements()), errors::InvalidArgument("ref has wrong number of elements"));
OP_REQUIRES(ctx, x.NumElements() <= kint32max, errors::InvalidArgument("x is too large"));
p.sizeX = (int)x.NumElements();
p.sizeB = (int)b.NumElements();
p.stepB = 1;
for (int i = m_attribs.axis + 1; i < x.dims(); i++)
p.stepB *= (int)x.dim_size(i);
Tensor* y = NULL; // x.shape
OP_REQUIRES_OK(ctx, ctx->allocate_output(0, x.shape(), &y));
p.y = y->flat<T>().data();
p.loopX = 4;
int blockSize = 4 * 32;
int gridSize = (p.sizeX - 1) / (p.loopX * blockSize) + 1;
void* args[] = {&p};
OP_CHECK_CUDA_ERROR(ctx, cudaLaunchKernel((void*)FusedBiasActKernel<T>, gridSize, blockSize, args, 0, stream));
}
};
REGISTER_OP("FusedBiasAct")
.Input ("x: T")
.Input ("b: T")
.Input ("ref: T")
.Output ("y: T")
.Attr ("T: {float, half}")
.Attr ("grad: int = 0")
.Attr ("axis: int = 1")
.Attr ("act: int = 0")
.Attr ("alpha: float = 0.0")
.Attr ("gain: float = 1.0");
REGISTER_KERNEL_BUILDER(Name("FusedBiasAct").Device(DEVICE_GPU).TypeConstraint<float>("T"), FusedBiasActOp<float>);
REGISTER_KERNEL_BUILDER(Name("FusedBiasAct").Device(DEVICE_GPU).TypeConstraint<Eigen::half>("T"), FusedBiasActOp<Eigen::half>);
//------------------------------------------------------------------------
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