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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 <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>
#include <c10/cuda/CUDAGuard.h>
#include "bias_act.h"
//------------------------------------------------------------------------
static bool has_same_layout(torch::Tensor x, torch::Tensor y)
{
if (x.dim() != y.dim())
return false;
for (int64_t i = 0; i < x.dim(); i++)
{
if (x.size(i) != y.size(i))
return false;
if (x.size(i) >= 2 && x.stride(i) != y.stride(i))
return false;
}
return true;
}
//------------------------------------------------------------------------
static torch::Tensor bias_act(torch::Tensor x, torch::Tensor b, torch::Tensor xref, torch::Tensor yref, torch::Tensor dy, int grad, int dim, int act, float alpha, float gain, float clamp)
{
// Validate arguments.
TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device");
TORCH_CHECK(b.numel() == 0 || (b.dtype() == x.dtype() && b.device() == x.device()), "b must have the same dtype and device as x");
TORCH_CHECK(xref.numel() == 0 || (xref.sizes() == x.sizes() && xref.dtype() == x.dtype() && xref.device() == x.device()), "xref must have the same shape, dtype, and device as x");
TORCH_CHECK(yref.numel() == 0 || (yref.sizes() == x.sizes() && yref.dtype() == x.dtype() && yref.device() == x.device()), "yref must have the same shape, dtype, and device as x");
TORCH_CHECK(dy.numel() == 0 || (dy.sizes() == x.sizes() && dy.dtype() == x.dtype() && dy.device() == x.device()), "dy must have the same dtype and device as x");
TORCH_CHECK(x.numel() <= INT_MAX, "x is too large");
TORCH_CHECK(b.dim() == 1, "b must have rank 1");
TORCH_CHECK(b.numel() == 0 || (dim >= 0 && dim < x.dim()), "dim is out of bounds");
TORCH_CHECK(b.numel() == 0 || b.numel() == x.size(dim), "b has wrong number of elements");
TORCH_CHECK(grad >= 0, "grad must be non-negative");
// Validate layout.
TORCH_CHECK(x.is_non_overlapping_and_dense(), "x must be non-overlapping and dense");
TORCH_CHECK(b.is_contiguous(), "b must be contiguous");
TORCH_CHECK(xref.numel() == 0 || has_same_layout(xref, x), "xref must have the same layout as x");
TORCH_CHECK(yref.numel() == 0 || has_same_layout(yref, x), "yref must have the same layout as x");
TORCH_CHECK(dy.numel() == 0 || has_same_layout(dy, x), "dy must have the same layout as x");
// Create output tensor.
const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
torch::Tensor y = torch::empty_like(x);
TORCH_CHECK(has_same_layout(y, x), "y must have the same layout as x");
// Initialize CUDA kernel parameters.
bias_act_kernel_params p;
p.x = x.data_ptr();
p.b = (b.numel()) ? b.data_ptr() : NULL;
p.xref = (xref.numel()) ? xref.data_ptr() : NULL;
p.yref = (yref.numel()) ? yref.data_ptr() : NULL;
p.dy = (dy.numel()) ? dy.data_ptr() : NULL;
p.y = y.data_ptr();
p.grad = grad;
p.act = act;
p.alpha = alpha;
p.gain = gain;
p.clamp = clamp;
p.sizeX = (int)x.numel();
p.sizeB = (int)b.numel();
p.stepB = (b.numel()) ? (int)x.stride(dim) : 1;
// Choose CUDA kernel.
void* kernel;
AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&]
{
kernel = choose_bias_act_kernel<scalar_t>(p);
});
TORCH_CHECK(kernel, "no CUDA kernel found for the specified activation func");
// Launch CUDA kernel.
p.loopX = 4;
int blockSize = 4 * 32;
int gridSize = (p.sizeX - 1) / (p.loopX * blockSize) + 1;
void* args[] = {&p};
AT_CUDA_CHECK(cudaLaunchKernel(kernel, gridSize, blockSize, args, 0, at::cuda::getCurrentCUDAStream()));
return y;
}
//------------------------------------------------------------------------
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
{
m.def("bias_act", &bias_act);
}
//------------------------------------------------------------------------
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