chore: import upstream snapshot with attribution

paddle/phi/kernels/impl/deformable_conv_kernel_impl.h

@@ -0,0 +1,187 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include "paddle/common/hostdevice.h"
+#include "paddle/phi/core/dense_tensor.h"
+#include "paddle/phi/kernels/empty_kernel.h"
+#include "paddle/phi/kernels/full_kernel.h"
+#include "paddle/phi/kernels/funcs/blas/blas.h"
+#include "paddle/phi/kernels/funcs/deformable_conv_functor.h"
+#include "paddle/phi/kernels/transpose_kernel.h"
+#include "paddle/utils/optional.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void DeformableConvKernel(const Context& dev_ctx,
+                          const DenseTensor& x,
+                          const DenseTensor& offset,
+                          const DenseTensor& filter,
+                          const optional<DenseTensor>& mask,
+                          const std::vector<int>& strides,
+                          const std::vector<int>& paddings,
+                          const std::vector<int>& dilations,
+                          int deformable_groups,
+                          int groups,
+                          int im2col_step,
+                          DenseTensor* out) {
+  if (x.numel() == 0 || filter.numel() == 0) {
+    Full<T, Context>(dev_ctx, out->dims(), 0, out);
+    return;
+  }
+
+  const int64_t batch_size = static_cast<int64_t>(x.dims()[0]);
+
+  int64_t temp_step = std::min<int64_t>(64, batch_size);
+  if (batch_size % temp_step == 0) {
+    im2col_step = temp_step;
+  }
+
+  std::vector<int64_t> filter_shape_vec(vectorize(filter.dims()));
+  std::vector<int64_t> output_shape_vec(vectorize(out->dims()));
+
+  // col_shape_vec: {c_i * k_h * k_w, im2col_step, o_h, o_w}
+  std::vector<int64_t> col_buffer_shape_vec(filter_shape_vec.size());
+  col_buffer_shape_vec[0] = x.dims()[1] * filter.dims()[2] * filter.dims()[3];
+  col_buffer_shape_vec[1] = im2col_step;
+  for (size_t j = 0; j < filter_shape_vec.size() - 2; ++j) {
+    col_buffer_shape_vec[j + 2] = output_shape_vec[j + 2];
+  }
+
+  std::vector<int64_t> output_buffer_shape_vec(1);
+  output_buffer_shape_vec[0] = batch_size * output_shape_vec[1] *
+                               output_shape_vec[2] * output_shape_vec[3];
+
+  DenseTensor col_buffer = Empty<T>(dev_ctx, col_buffer_shape_vec);
+  DenseTensor output_buffer = Empty<T>(dev_ctx, output_buffer_shape_vec);
+
+  int64_t M = output_shape_vec[1] / groups;
+  int64_t N = im2col_step * output_shape_vec[2] * output_shape_vec[3];
+  int64_t K = x.dims()[1] * filter_shape_vec[2] * filter_shape_vec[3] / groups;
+
+  DenseTensor weight_3d;
+  weight_3d.ShareDataWith(filter).Resize({groups, M, K});
+
+  DenseTensor col_buffer_3d;
+  col_buffer_3d.ShareDataWith(col_buffer).Resize({groups, K, N});
+
+  DenseTensor output_4d;
+  output_4d.ShareDataWith(output_buffer)
+      .Resize({batch_size / im2col_step, groups, M, N});
+
+  DDim input_shape = slice_ddim(x.dims(), 1, x.dims().size());
+  std::vector<int64_t> input_shape_vec = vectorize(input_shape);
+
+  int64_t input_dim = x.numel() / x.dims()[0];
+  int64_t input_offset_dim = offset.numel() / offset.dims()[0];
+  int64_t input_mask_dim = mask ? mask->numel() / mask->dims()[0] : 0;
+
+  const T* input_ptr = x.data<T>();
+  const T* offset_ptr = offset.data<T>();
+  const T* mask_ptr = mask ? mask->data<T>() : nullptr;
+  T* col_buffer_ptr = col_buffer.data<T>();
+
+  auto blas = funcs::GetBlas<Context, T>(dev_ctx);
+
+  bool using_int32_index =
+      (x.numel() <= std::numeric_limits<int>::max()) &&
+      (offset.numel() <= std::numeric_limits<int>::max()) &&
+      (filter.numel() <= std::numeric_limits<int>::max()) &&
+      (mask ? mask->numel() <= std::numeric_limits<int>::max() : true) &&
+      (out->numel() <= std::numeric_limits<int>::max());
+
+  for (int64_t i = 0; i < batch_size / im2col_step; ++i) {
+    const T* temp_mask_ptr =
+        mask_ptr ? mask_ptr + i * im2col_step * input_mask_dim : nullptr;
+    if (using_int32_index) {
+      funcs::ModulatedDeformableIm2col<T, Context, int>(
+          dev_ctx,
+          input_ptr + i * im2col_step * input_dim,
+          offset_ptr + i * im2col_step * input_offset_dim,
+          temp_mask_ptr,
+          input_shape_vec,
+          col_buffer_shape_vec,
+          filter_shape_vec,
+          paddings,
+          strides,
+          dilations,
+          deformable_groups,
+          col_buffer_ptr);
+    } else {
+      funcs::ModulatedDeformableIm2col<T, Context, int64_t>(
+          dev_ctx,
+          input_ptr + i * im2col_step * input_dim,
+          offset_ptr + i * im2col_step * input_offset_dim,
+          temp_mask_ptr,
+          input_shape_vec,
+          col_buffer_shape_vec,
+          filter_shape_vec,
+          paddings,
+          strides,
+          dilations,
+          deformable_groups,
+          col_buffer_ptr);
+    }
+
+    DenseTensor output_3d = output_4d.Slice(i, i + 1).Resize(slice_ddim(
+        output_4d.dims(),
+        1,
+        output_4d.dims().size()));  // group * C/group * (im2step * H * W)
+
+    // get the product of pixel and weight
+    for (int g = 0; g < groups; ++g) {
+      DenseTensor weight_3d_slice = weight_3d.Slice(g, g + 1).Resize(
+          slice_ddim(weight_3d.dims(), 1, weight_3d.dims().size()));
+      DenseTensor col_buffer_3d_slice = col_buffer_3d.Slice(g, g + 1).Resize(
+          slice_ddim(col_buffer_3d.dims(), 1, col_buffer_3d.dims().size()));
+      DenseTensor output_3d_slice = output_3d.Slice(g, g + 1).Resize(
+          slice_ddim(output_3d.dims(),
+                     1,
+                     output_3d.dims().size()));  // C * ((im2col_step)*H*W))
+      blas.MatMul(weight_3d_slice,
+                  false,
+                  col_buffer_3d_slice,
+                  false,
+                  T(1.0),
+                  &output_3d_slice,
+                  T(0.0));
+    }
+  }
+
+  //  swap axis to get the right result when im2col_step is greater than 1
+  if (im2col_step > 1) {
+    std::vector<int> axis(4);
+    axis[0] = 0;
+    axis[1] = 2;
+    axis[2] = 1;
+    axis[3] = 3;
+
+    DenseTensor real_output_buffer = Transpose<T, Context>(
+        dev_ctx,
+        output_4d.Resize(
+            make_ddim({batch_size / im2col_step,
+                       output_shape_vec[1],
+                       im2col_step,
+                       output_shape_vec[2] * output_shape_vec[3]})),
+        axis);
+
+    out->ShareDataWith(real_output_buffer).Resize(output_shape_vec);
+  } else {
+    out->ShareDataWith(output_buffer).Resize(output_shape_vec);
+  }
+}
+
+}  // namespace phi