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#include "caffe2/operators/margin_loss_l2r_op.h"
#include <cmath>
#include "caffe2/core/context.h"
#include "caffe2/core/operator.h"
#include "caffe2/utils/eigen_utils.h"
namespace caffe2 {
namespace {
#define PAIRWISE_DIFF(vec, N) \
((vec.matrix() * Eigen::MatrixXf::Ones(1, N) - \
Eigen::MatrixXf::Ones(N, 1) * vec.matrix().transpose()) \
.array())
#define CWISE_GT(vec1, vec2) ((vec1) > (vec2))
#define CWISE_LT(vec1, vec2) ((vec1) < (vec2))
#define CWISE_SIGN(vec) \
CWISE_GT((vec), 0).cast<float>() - CWISE_LT((vec), 0).cast<float>()
} // namespace
template <>
float SessionMarginLossOp<float, CPUContext>::SessionMarginLoss(
int start_index,
int end_index,
const Tensor& pred,
const Tensor& label,
Tensor** dpred) {
CAFFE_ENFORCE_LE(0.0, start_index);
CAFFE_ENFORCE_GE(pred.numel(), start_index);
const auto* pred_data = pred.template data<float>();
const auto* label_data = label.template data<float>();
int N = end_index - start_index + 1;
ConstEigenVectorArrayMap<float> pred_vec(&pred_data[start_index], N);
ConstEigenVectorArrayMap<float> label_vec(&label_data[start_index], N);
auto* dpred_data = (*dpred)->template mutable_data<float>();
EigenVectorArrayMap<float> dpred_vec(&dpred_data[start_index], N);
dpred_vec = 0;
ReinitializeTensor(&margin_diff_, {N * N}, at::dtype<float>().device(CPU));
auto* margin_diff_data = margin_diff_.template mutable_data<float>();
EigenArrayMap<float> margin_diff_mat(margin_diff_data, N, N);
ReinitializeTensor(
&label_relation_sign_, {N * N}, at::dtype<float>().device(CPU));
auto* label_relation_sign_data =
label_relation_sign_.template mutable_data<float>();
EigenArrayMap<float> label_relation_sign_mat(label_relation_sign_data, N, N);
// in case that all docs in a session have zero ratings, no op
if (label_vec.abs().sum() < 1e-6) {
return 0;
}
if (N <= 0) {
return 0;
}
float weight = 1.0f / N;
// define label relation, return N * N MATRIX, element (i, j) will be sign(label_i - label_i)
label_relation_sign_mat = PAIRWISE_DIFF(label_vec, N).cwiseSign();
margin_diff_mat =
(margin_ - (label_relation_sign_mat * PAIRWISE_DIFF(pred_vec, N))) *
label_relation_sign_mat.abs();
float loss = 0.5f * weight *
(margin_diff_mat * CWISE_GT(margin_diff_mat, 0).cast<float>()).sum();
dpred_vec = -weight *
((CWISE_GT(margin_diff_mat, 0).cast<float>()) * label_relation_sign_mat)
.rowwise()
.sum();
return loss;
}
template <>
bool SessionMarginLossOp<float, CPUContext>::RunOnDevice() {
auto& pred = Input(PRED);
auto& label = Input(LABEL);
auto& sid = Input(SESSION_LENS);
auto* dpred = Output(DPRED);
const auto* session_lengths = sid.template data<int>();
CAFFE_ENFORCE(pred.dim() == 1);
CAFFE_ENFORCE(pred.numel() == label.numel());
dpred->Resize(pred.numel());
auto* loss = Output(LOSS, {sid.numel()}, at::dtype<float>());
auto loss_vec = loss->template mutable_data<float>();
int start_id = 0;
for (int i = 0; i < sid.numel(); i++) {
loss_vec[i] = SessionMarginLoss(
start_id, session_lengths[i] + start_id - 1, pred, label, &dpred);
start_id += session_lengths[i];
}
return true;
}
template <>
bool SessionMarginLossGradientOp<float, CPUContext>::RunOnDevice() {
auto& pred = Input(PRED);
auto& sids = Input(SESSION_LENS);
auto& precomputed_dpred = Input(PRECOMPUTED_DPRED);
auto& dLoss = Input(DLOSS);
CAFFE_ENFORCE(pred.dim() == 1);
CAFFE_ENFORCE(precomputed_dpred.dim() == 1);
CAFFE_ENFORCE(precomputed_dpred.numel() > 0);
CAFFE_ENFORCE(pred.numel() == precomputed_dpred.numel());
const auto* session_lengths = sids.template data<int>();
CAFFE_ENFORCE(dLoss.numel() == sids.numel());
ConstEigenVectorArrayMap<float> precomputed_dpred_vec(
precomputed_dpred.template data<float>(), precomputed_dpred.numel());
auto* dpred = Output(DPRED, {precomputed_dpred.numel()}, at::dtype<float>());
EigenVectorArrayMap<float> dpred_vec(
dpred->template mutable_data<float>(), dpred->numel());
auto multiplier = dLoss.template data<float>();
int count = 0;
for (int j = 0; j < sids.numel(); j++) {
dpred_vec.segment(count, session_lengths[j]) = multiplier[j] *
precomputed_dpred_vec.segment(count, session_lengths[j]);
count += session_lengths[j];
}
return true;
}
namespace {
REGISTER_CPU_OPERATOR(
SessionMarginLoss,
SessionMarginLossOp<float, CPUContext>);
REGISTER_CPU_OPERATOR(
SessionMarginLossGradient,
SessionMarginLossGradientOp<float, CPUContext>);
OPERATOR_SCHEMA(SessionMarginLoss).NumInputs(3).NumOutputs(2).SetDoc(R"DOC(
This method optimizes the pairwise margin loss in a session with margin control.
If multiple sessions are in a batch, pairwise loss will only be computed in a session and the total loss will be the sum of pairwise loss from each session.
The exact loss function in a session is similar to https://pytorch.org/docs/stable/generated/torch.nn.MarginRankingLoss.html#torch.nn.MarginRankingLoss
)DOC");
OPERATOR_SCHEMA(SessionMarginLossGradient).NumInputs(4).NumOutputs(1);
class GetSessionMarginLossGradient : public GradientMakerBase {
using GradientMakerBase::GradientMakerBase;
vector<OperatorDef> GetGradientDefs() override {
return SingleGradientDef(
"SessionMarginLossGradient",
"",
vector<string>{I(0), I(2), O(1), GO(0)},
vector<string>{GI(0)});
}
};
REGISTER_GRADIENT(SessionMarginLoss, GetSessionMarginLossGradient);
} // namespace
} // namespace caffe2
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