#include "megdnn/oprs.h"
#include "src/common/utils.h"
namespace megdnn {
void LocalBase::deduce_layout_fwd(
const TensorLayout& src, const TensorLayout& filter, TensorLayout& dst) {
auto errmsg = megdnn_layout_msg(src) + ", " + megdnn_layout_msg(filter) + ", " +
megdnn_layout_msg(dst) + ", " + "is_xcorr=" +
std::to_string((param().mode == Mode::CROSS_CORRELATION)) + ", " +
"pad_h=" + std::to_string(param().pad_h) + ", " +
"pad_w=" + std::to_string(param().pad_w) + ", " +
"stride_h=" + std::to_string(param().stride_h) + ", " +
"stride_w=" + std::to_string(param().stride_w);
auto errmsg_c = errmsg.c_str();
MEGDNN_MARK_USED_VAR(errmsg_c);
TensorLayout src_contig = src;
src_contig.init_contiguous_stride();
src_contig.stride[0] = src.stride[0];
megdnn_assert_eq_layout(src_contig, src);
megdnn_assert_contiguous(filter);
megdnn_assert(src.ndim == 4_z, "%s", errmsg_c);
megdnn_assert(filter.ndim == 6_z, "%s", errmsg_c);
megdnn_assert(
param().dilate_h == 1 && param().dilate_w == 1,
"dilation in local not supported");
megdnn_assert(
param().sparse == Param::Sparse::DENSE && param().dilate_h == 1 &&
param().dilate_w == 1 && src.dtype.category() == DTypeCategory::FLOAT &&
dst.dtype == src.dtype && "unsupported conv param for Local opr");
size_t n = src[0];
size_t ic = src[1];
size_t ih = src[2];
size_t iw = src[3];
megdnn_assert_eq_size_t(filter[2], ic);
size_t fh = filter[3];
size_t fw = filter[4];
size_t oc = filter[5];
size_t sh = param().stride_h;
size_t sw = param().stride_w;
size_t ph = param().pad_h;
size_t pw = param().pad_w;
size_t oh, ow;
infer_conv_shape2d(ih, iw, fh, fw, sh, sw, ph, pw, oh, ow);
dst = TensorLayout(TensorShape({n, oc, oh, ow}), src.dtype);
}
void LocalBase::check_layout_fwd(
const TensorLayout& src, const TensorLayout& filter, const TensorLayout& dst) {
TensorLayout dst_expected{dst.dtype};
megdnn_assert_eq_dtype(src, filter);
megdnn_assert_eq_dtype(src, dst);
deduce_layout_fwd(src, filter, dst_expected);
dst_expected.stride[0] = dst.stride[0];
megdnn_assert_eq_layout(dst_expected, dst);
megdnn_assert(src.dtype == filter.dtype && src.dtype == dst.dtype);
megdnn_assert(
src.dtype == dtype::Float32() ||
DNN_FLOAT16_SELECT(src.dtype == dtype::Float16(), true));
}
void LocalForward::deduce_layout(
const TensorLayout& src, const TensorLayout& filter, TensorLayout& dst) {
deduce_layout_fwd(src, filter, dst);
}
void LocalForward::check_exec(
const TensorLayout& src, const TensorLayout& filter, const TensorLayout& dst,
size_t workspace_in_bytes) {
check_layout_fwd(src, filter, dst);
auto required_workspace_in_bytes = get_workspace_in_bytes(src, filter, dst);
megdnn_assert(workspace_in_bytes >= required_workspace_in_bytes);
}
void LocalBackwardData::check_exec(
const TensorLayout& filter, const TensorLayout& diff, const TensorLayout& grad,
size_t workspace_in_bytes) {
check_layout_fwd(grad, filter, diff);
auto required_workspace_in_bytes = get_workspace_in_bytes(filter, diff, grad);
megdnn_assert(workspace_in_bytes >= required_workspace_in_bytes);
}
void LocalBackwardFilter::check_exec(
const TensorLayout& src, const TensorLayout& diff, const TensorLayout& grad,
size_t workspace_in_bytes) {
check_layout_fwd(src, grad, diff);
auto required_workspace_in_bytes = get_workspace_in_bytes(src, diff, grad);
megdnn_assert(workspace_in_bytes >= required_workspace_in_bytes);
}
}