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Optimize CPU deform_conv2d forward pass with parallel im2col #9442

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Optimize CPU deform_conv2d forward pass with parallel im2col #9442

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8a89fb8
perf: optimize CPU deform_conv2d forward pass
developer0hye Mar 15, 2026
137d2b7
style: fix clang-format lint for method chain in deform_conv2d_kernel
developer0hye Mar 17, 2026
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98 changes: 49 additions & 49 deletions torchvision/csrc/ops/cpu/deform_conv2d_kernel.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -68,6 +68,7 @@
// https://github.com/open-mmlab/mmdetection/blob/master/mmdet/ops/dcn/src/deform_conv_cuda.cpp

#include <ATen/ATen.h>
#include <ATen/Parallel.h>
#include <torch/library.h>

namespace vision {
Expand Down Expand Up @@ -139,58 +140,60 @@ void deformable_im2col_kernel(
int out_w,
bool use_mask,
scalar_t* columns) {
for (int index = 0; index != n; ++index) {
const int out_x = index % out_w;
const int out_y = (index / out_w) % out_h;
const int out_b = (index / (out_w * out_h)) % batch_sz;
const int in_c = index / (out_w * out_h * batch_sz);
const int out_c = in_c * weight_h * weight_w;
at::parallel_for(0, n, 0, [&](int64_t begin, int64_t end) {
for (int64_t index = begin; index != end; ++index) {
const int out_x = index % out_w;
const int out_y = (index / out_w) % out_h;
const int out_b = (index / (out_w * out_h)) % batch_sz;
const int in_c = index / (out_w * out_h * batch_sz);
const int out_c = in_c * weight_h * weight_w;

int c_per_offset_grp = n_in_channels / n_offset_grps;
const int grp_idx = in_c / c_per_offset_grp;
int c_per_offset_grp = n_in_channels / n_offset_grps;
const int grp_idx = in_c / c_per_offset_grp;

auto columns_ptr = columns +
(out_c * (batch_sz * out_h * out_w) + out_b * (out_h * out_w) +
out_y * out_w + out_x);
auto columns_ptr = columns +
(out_c * (batch_sz * out_h * out_w) + out_b * (out_h * out_w) +
out_y * out_w + out_x);

auto input_ptr = input +
(out_b * (n_in_channels * height * width) + in_c * (height * width));
auto input_ptr = input +
(out_b * (n_in_channels * height * width) + in_c * (height * width));

auto offset_ptr = offset +
(out_b * n_offset_grps + grp_idx) * 2 * weight_h * weight_w * out_h *
out_w;
auto offset_ptr = offset +
(out_b * n_offset_grps + grp_idx) * 2 * weight_h * weight_w * out_h *
out_w;

auto mask_ptr = mask;
if (use_mask) {
mask_ptr += (out_b * n_offset_grps + grp_idx) * weight_h * weight_w *
out_h * out_w;
}

for (int i = 0; i < weight_h; ++i) {
for (int j = 0; j < weight_w; ++j) {
const int mask_idx = i * weight_w + j;
const int offset_idx = 2 * mask_idx;
auto mask_ptr = mask;
if (use_mask) {
mask_ptr += (out_b * n_offset_grps + grp_idx) * weight_h * weight_w *
out_h * out_w;
}

scalar_t mask_value = 1;
if (use_mask) {
mask_value =
mask_ptr[mask_idx * (out_h * out_w) + out_y * out_w + out_x];
for (int i = 0; i < weight_h; ++i) {
for (int j = 0; j < weight_w; ++j) {
const int mask_idx = i * weight_w + j;
const int offset_idx = 2 * mask_idx;

scalar_t mask_value = 1;
if (use_mask) {
mask_value =
mask_ptr[mask_idx * (out_h * out_w) + out_y * out_w + out_x];
}

const scalar_t offset_h =
offset_ptr[offset_idx * (out_h * out_w) + out_y * out_w + out_x];
const scalar_t offset_w = offset_ptr
[(offset_idx + 1) * (out_h * out_w) + out_y * out_w + out_x];
const scalar_t y =
(out_y * stride_h - pad_h) + i * dilation_h + offset_h;
const scalar_t x =
(out_x * stride_w - pad_w) + j * dilation_w + offset_w;
*columns_ptr =
mask_value * bilinear_interpolate(input_ptr, height, width, y, x);
columns_ptr += batch_sz * out_h * out_w;
}

const scalar_t offset_h =
offset_ptr[offset_idx * (out_h * out_w) + out_y * out_w + out_x];
const scalar_t offset_w = offset_ptr
[(offset_idx + 1) * (out_h * out_w) + out_y * out_w + out_x];
const scalar_t y =
(out_y * stride_h - pad_h) + i * dilation_h + offset_h;
const scalar_t x =
(out_x * stride_w - pad_w) + j * dilation_w + offset_w;
*columns_ptr =
mask_value * bilinear_interpolate(input_ptr, height, width, y, x);
columns_ptr += batch_sz * out_h * out_w;
}
}
}
});
}

void deformable_im2col(
Expand Down Expand Up @@ -1013,7 +1016,7 @@ at::Tensor deform_conv2d_forward_kernel(
out_w});
}

at::Tensor out_buf = at::zeros(
at::Tensor out_buf = at::empty(
{batch_sz / n_parallel_imgs,
out_channels,
n_parallel_imgs * out_h,
Expand All @@ -1035,7 +1038,7 @@ at::Tensor deform_conv2d_forward_kernel(
weight_c.size(3)});

// Sample points and perform convolution
auto columns = at::zeros(
auto columns = at::empty(
{n_in_channels * weight_h * weight_w, n_parallel_imgs * out_h * out_w},
input_c.options());
for (int b = 0; b < batch_sz / n_parallel_imgs; b++) {
Expand Down Expand Up @@ -1064,10 +1067,7 @@ at::Tensor deform_conv2d_forward_kernel(
columns = columns.view(
{n_weight_grps, columns.size(0) / n_weight_grps, columns.size(1)});
for (int g = 0; g < n_weight_grps; g++) {
out_buf[b][g] = out_buf[b][g]
.flatten(1)
.addmm_(weight_c[g].flatten(1), columns[g])
.view_as(out_buf[b][g]);
out_buf[b][g].flatten(1).addmm_(weight_c[g].flatten(1), columns[g], 0, 1);
}
columns =
columns.view({columns.size(0) * columns.size(1), columns.size(2)});
Expand Down
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