Create vtoonify/model/stylegan/op_gpu/upfirdn2d_kernel.cu

vtoonify/model/stylegan/op_gpu/upfirdn2d_kernel.cu

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+// 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
+
+#include <torch/types.h>
+
+#include <ATen/ATen.h>
+#include <ATen/AccumulateType.h>
+#include <ATen/cuda/CUDAApplyUtils.cuh>
+#include <ATen/cuda/CUDAContext.h>
+
+#include <cuda.h>
+#include <cuda_runtime.h>
+
+static __host__ __device__ __forceinline__ int floor_div(int a, int b) {
+  int c = a / b;
+
+  if (c * b > a) {
+    c--;
+  }
+
+  return c;
+}
+
+struct UpFirDn2DKernelParams {
+  int up_x;
+  int up_y;
+  int down_x;
+  int down_y;
+  int pad_x0;
+  int pad_x1;
+  int pad_y0;
+  int pad_y1;
+
+  int major_dim;
+  int in_h;
+  int in_w;
+  int minor_dim;
+  int kernel_h;
+  int kernel_w;
+  int out_h;
+  int out_w;
+  int loop_major;
+  int loop_x;
+};
+
+template <typename scalar_t>
+__global__ void upfirdn2d_kernel_large(scalar_t *out, const scalar_t *input,
+                                       const scalar_t *kernel,
+                                       const UpFirDn2DKernelParams p) {
+  int minor_idx = blockIdx.x * blockDim.x + threadIdx.x;
+  int out_y = minor_idx / p.minor_dim;
+  minor_idx -= out_y * p.minor_dim;
+  int out_x_base = blockIdx.y * p.loop_x * blockDim.y + threadIdx.y;
+  int major_idx_base = blockIdx.z * p.loop_major;
+
+  if (out_x_base >= p.out_w || out_y >= p.out_h ||
+      major_idx_base >= p.major_dim) {
+    return;
+  }
+
+  int mid_y = out_y * p.down_y + p.up_y - 1 - p.pad_y0;
+  int in_y = min(max(floor_div(mid_y, p.up_y), 0), p.in_h);
+  int h = min(max(floor_div(mid_y + p.kernel_h, p.up_y), 0), p.in_h) - in_y;
+  int kernel_y = mid_y + p.kernel_h - (in_y + 1) * p.up_y;
+
+  for (int loop_major = 0, major_idx = major_idx_base;
+       loop_major < p.loop_major && major_idx < p.major_dim;
+       loop_major++, major_idx++) {
+    for (int loop_x = 0, out_x = out_x_base;
+         loop_x < p.loop_x && out_x < p.out_w; loop_x++, out_x += blockDim.y) {
+      int mid_x = out_x * p.down_x + p.up_x - 1 - p.pad_x0;
+      int in_x = min(max(floor_div(mid_x, p.up_x), 0), p.in_w);
+      int w = min(max(floor_div(mid_x + p.kernel_w, p.up_x), 0), p.in_w) - in_x;
+      int kernel_x = mid_x + p.kernel_w - (in_x + 1) * p.up_x;
+
+      const scalar_t *x_p =
+          &input[((major_idx * p.in_h + in_y) * p.in_w + in_x) * p.minor_dim +
+                 minor_idx];
+      const scalar_t *k_p = &kernel[kernel_y * p.kernel_w + kernel_x];
+      int x_px = p.minor_dim;
+      int k_px = -p.up_x;
+      int x_py = p.in_w * p.minor_dim;
+      int k_py = -p.up_y * p.kernel_w;
+
+      scalar_t v = 0.0f;
+
+      for (int y = 0; y < h; y++) {
+        for (int x = 0; x < w; x++) {
+          v += static_cast<scalar_t>(*x_p) * static_cast<scalar_t>(*k_p);
+          x_p += x_px;
+          k_p += k_px;
+        }
+
+        x_p += x_py - w * x_px;
+        k_p += k_py - w * k_px;
+      }
+
+      out[((major_idx * p.out_h + out_y) * p.out_w + out_x) * p.minor_dim +
+          minor_idx] = v;
+    }
+  }
+}
+
+template <typename scalar_t, int up_x, int up_y, int down_x, int down_y,
+          int kernel_h, int kernel_w, int tile_out_h, int tile_out_w>
+__global__ void upfirdn2d_kernel(scalar_t *out, const scalar_t *input,
+                                 const scalar_t *kernel,
+                                 const UpFirDn2DKernelParams p) {
+  const int tile_in_h = ((tile_out_h - 1) * down_y + kernel_h - 1) / up_y + 1;
+  const int tile_in_w = ((tile_out_w - 1) * down_x + kernel_w - 1) / up_x + 1;
+
+  __shared__ volatile float sk[kernel_h][kernel_w];
+  __shared__ volatile float sx[tile_in_h][tile_in_w];
+
+  int minor_idx = blockIdx.x;
+  int tile_out_y = minor_idx / p.minor_dim;
+  minor_idx -= tile_out_y * p.minor_dim;
+  tile_out_y *= tile_out_h;
+  int tile_out_x_base = blockIdx.y * p.loop_x * tile_out_w;
+  int major_idx_base = blockIdx.z * p.loop_major;
+
+  if (tile_out_x_base >= p.out_w | tile_out_y >= p.out_h |
+      major_idx_base >= p.major_dim) {
+    return;
+  }
+
+  for (int tap_idx = threadIdx.x; tap_idx < kernel_h * kernel_w;
+       tap_idx += blockDim.x) {
+    int ky = tap_idx / kernel_w;
+    int kx = tap_idx - ky * kernel_w;
+    scalar_t v = 0.0;
+
+    if (kx < p.kernel_w & ky < p.kernel_h) {
+      v = kernel[(p.kernel_h - 1 - ky) * p.kernel_w + (p.kernel_w - 1 - kx)];
+    }
+
+    sk[ky][kx] = v;
+  }
+
+  for (int loop_major = 0, major_idx = major_idx_base;
+       loop_major < p.loop_major & major_idx < p.major_dim;
+       loop_major++, major_idx++) {
+    for (int loop_x = 0, tile_out_x = tile_out_x_base;
+         loop_x < p.loop_x & tile_out_x < p.out_w;
+         loop_x++, tile_out_x += tile_out_w) {
+      int tile_mid_x = tile_out_x * down_x + up_x - 1 - p.pad_x0;
+      int tile_mid_y = tile_out_y * down_y + up_y - 1 - p.pad_y0;
+      int tile_in_x = floor_div(tile_mid_x, up_x);
+      int tile_in_y = floor_div(tile_mid_y, up_y);
+
+      __syncthreads();
+
+      for (int in_idx = threadIdx.x; in_idx < tile_in_h * tile_in_w;
+           in_idx += blockDim.x) {
+        int rel_in_y = in_idx / tile_in_w;
+        int rel_in_x = in_idx - rel_in_y * tile_in_w;
+        int in_x = rel_in_x + tile_in_x;
+        int in_y = rel_in_y + tile_in_y;
+
+        scalar_t v = 0.0;
+
+        if (in_x >= 0 & in_y >= 0 & in_x < p.in_w & in_y < p.in_h) {
+          v = input[((major_idx * p.in_h + in_y) * p.in_w + in_x) *
+                        p.minor_dim +
+                    minor_idx];
+        }
+
+        sx[rel_in_y][rel_in_x] = v;
+      }
+
+      __syncthreads();
+      for (int out_idx = threadIdx.x; out_idx < tile_out_h * tile_out_w;
+           out_idx += blockDim.x) {
+        int rel_out_y = out_idx / tile_out_w;
+        int rel_out_x = out_idx - rel_out_y * tile_out_w;
+        int out_x = rel_out_x + tile_out_x;
+        int out_y = rel_out_y + tile_out_y;
+
+        int mid_x = tile_mid_x + rel_out_x * down_x;
+        int mid_y = tile_mid_y + rel_out_y * down_y;
+        int in_x = floor_div(mid_x, up_x);
+        int in_y = floor_div(mid_y, up_y);
+        int rel_in_x = in_x - tile_in_x;
+        int rel_in_y = in_y - tile_in_y;
+        int kernel_x = (in_x + 1) * up_x - mid_x - 1;
+        int kernel_y = (in_y + 1) * up_y - mid_y - 1;
+
+        scalar_t v = 0.0;
+
+#pragma unroll
+        for (int y = 0; y < kernel_h / up_y; y++)
+#pragma unroll
+          for (int x = 0; x < kernel_w / up_x; x++)
+            v += sx[rel_in_y + y][rel_in_x + x] *
+                 sk[kernel_y + y * up_y][kernel_x + x * up_x];
+
+        if (out_x < p.out_w & out_y < p.out_h) {
+          out[((major_idx * p.out_h + out_y) * p.out_w + out_x) * p.minor_dim +
+              minor_idx] = v;
+        }
+      }
+    }
+  }
+}
+
+torch::Tensor upfirdn2d_op(const torch::Tensor &input,
+                           const torch::Tensor &kernel, int up_x, int up_y,
+                           int down_x, int down_y, int pad_x0, int pad_x1,
+                           int pad_y0, int pad_y1) {
+  int curDevice = -1;
+  cudaGetDevice(&curDevice);
+  cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+
+  UpFirDn2DKernelParams p;
+
+  auto x = input.contiguous();
+  auto k = kernel.contiguous();
+
+  p.major_dim = x.size(0);
+  p.in_h = x.size(1);
+  p.in_w = x.size(2);
+  p.minor_dim = x.size(3);
+  p.kernel_h = k.size(0);
+  p.kernel_w = k.size(1);
+  p.up_x = up_x;
+  p.up_y = up_y;
+  p.down_x = down_x;
+  p.down_y = down_y;
+  p.pad_x0 = pad_x0;
+  p.pad_x1 = pad_x1;
+  p.pad_y0 = pad_y0;
+  p.pad_y1 = pad_y1;
+
+  p.out_h = (p.in_h * p.up_y + p.pad_y0 + p.pad_y1 - p.kernel_h + p.down_y) /
+            p.down_y;
+  p.out_w = (p.in_w * p.up_x + p.pad_x0 + p.pad_x1 - p.kernel_w + p.down_x) /
+            p.down_x;
+
+  auto out =
+      at::empty({p.major_dim, p.out_h, p.out_w, p.minor_dim}, x.options());
+
+  int mode = -1;
+
+  int tile_out_h = -1;
+  int tile_out_w = -1;
+
+  if (p.up_x == 1 && p.up_y == 1 && p.down_x == 1 && p.down_y == 1 &&
+      p.kernel_h <= 4 && p.kernel_w <= 4) {
+    mode = 1;
+    tile_out_h = 16;
+    tile_out_w = 64;
+  }
+
+  if (p.up_x == 1 && p.up_y == 1 && p.down_x == 1 && p.down_y == 1 &&
+      p.kernel_h <= 3 && p.kernel_w <= 3) {
+    mode = 2;
+    tile_out_h = 16;
+    tile_out_w = 64;
+  }
+
+  if (p.up_x == 2 && p.up_y == 2 && p.down_x == 1 && p.down_y == 1 &&
+      p.kernel_h <= 4 && p.kernel_w <= 4) {
+    mode = 3;
+    tile_out_h = 16;
+    tile_out_w = 64;
+  }
+
+  if (p.up_x == 2 && p.up_y == 2 && p.down_x == 1 && p.down_y == 1 &&
+      p.kernel_h <= 2 && p.kernel_w <= 2) {
+    mode = 4;
+    tile_out_h = 16;
+    tile_out_w = 64;
+  }
+
+  if (p.up_x == 1 && p.up_y == 1 && p.down_x == 2 && p.down_y == 2 &&
+      p.kernel_h <= 4 && p.kernel_w <= 4) {
+    mode = 5;
+    tile_out_h = 8;
+    tile_out_w = 32;
+  }
+
+  if (p.up_x == 1 && p.up_y == 1 && p.down_x == 2 && p.down_y == 2 &&
+      p.kernel_h <= 2 && p.kernel_w <= 2) {
+    mode = 6;
+    tile_out_h = 8;
+    tile_out_w = 32;
+  }
+
+  dim3 block_size;
+  dim3 grid_size;
+
+  if (tile_out_h > 0 && tile_out_w > 0) {
+    p.loop_major = (p.major_dim - 1) / 16384 + 1;
+    p.loop_x = 1;
+    block_size = dim3(32 * 8, 1, 1);
+    grid_size = dim3(((p.out_h - 1) / tile_out_h + 1) * p.minor_dim,
+                     (p.out_w - 1) / (p.loop_x * tile_out_w) + 1,
+                     (p.major_dim - 1) / p.loop_major + 1);
+  } else {
+    p.loop_major = (p.major_dim - 1) / 16384 + 1;
+    p.loop_x = 4;
+    block_size = dim3(4, 32, 1);
+    grid_size = dim3((p.out_h * p.minor_dim - 1) / block_size.x + 1,
+                     (p.out_w - 1) / (p.loop_x * block_size.y) + 1,
+                     (p.major_dim - 1) / p.loop_major + 1);
+  }
+
+  AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&] {
+    switch (mode) {
+    case 1:
+      upfirdn2d_kernel<scalar_t, 1, 1, 1, 1, 4, 4, 16, 64>
+          <<<grid_size, block_size, 0, stream>>>(out.data_ptr<scalar_t>(),
+                                                 x.data_ptr<scalar_t>(),
+                                                 k.data_ptr<scalar_t>(), p);
+
+      break;
+
+    case 2:
+      upfirdn2d_kernel<scalar_t, 1, 1, 1, 1, 3, 3, 16, 64>
+          <<<grid_size, block_size, 0, stream>>>(out.data_ptr<scalar_t>(),
+                                                 x.data_ptr<scalar_t>(),
+                                                 k.data_ptr<scalar_t>(), p);
+
+      break;
+
+    case 3:
+      upfirdn2d_kernel<scalar_t, 2, 2, 1, 1, 4, 4, 16, 64>
+          <<<grid_size, block_size, 0, stream>>>(out.data_ptr<scalar_t>(),
+                                                 x.data_ptr<scalar_t>(),
+                                                 k.data_ptr<scalar_t>(), p);
+
+      break;
+
+    case 4:
+      upfirdn2d_kernel<scalar_t, 2, 2, 1, 1, 2, 2, 16, 64>
+          <<<grid_size, block_size, 0, stream>>>(out.data_ptr<scalar_t>(),
+                                                 x.data_ptr<scalar_t>(),
+                                                 k.data_ptr<scalar_t>(), p);
+
+      break;
+
+    case 5:
+      upfirdn2d_kernel<scalar_t, 1, 1, 2, 2, 4, 4, 8, 32>
+          <<<grid_size, block_size, 0, stream>>>(out.data_ptr<scalar_t>(),
+                                                 x.data_ptr<scalar_t>(),
+                                                 k.data_ptr<scalar_t>(), p);
+
+      break;
+
+    case 6:
+      upfirdn2d_kernel<scalar_t, 1, 1, 2, 2, 4, 4, 8, 32>
+          <<<grid_size, block_size, 0, stream>>>(out.data_ptr<scalar_t>(),
+                                                 x.data_ptr<scalar_t>(),
+                                                 k.data_ptr<scalar_t>(), p);
+
+      break;
+
+    default:
+      upfirdn2d_kernel_large<scalar_t><<<grid_size, block_size, 0, stream>>>(
+          out.data_ptr<scalar_t>(), x.data_ptr<scalar_t>(),
+          k.data_ptr<scalar_t>(), p);
+    }
+  });
+
+  return out;
+}