未验证 提交 e722f683 编写于 作者: D dzhwinter 提交者: GitHub

fix windows compile (#13147)

上级 f0552006
......@@ -865,8 +865,8 @@ struct SwishGradFunctor : public BaseActivationFunctor<T> {
void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
auto temp1 = static_cast<T>(1) /
(static_cast<T>(1) + (static_cast<T>(-beta) * x).exp());
auto temp2 = temp1 * (static_cast<T>(1) - (beta * out));
dx.device(d) = dout * ((beta * out) + temp2);
auto temp2 = temp1 * (static_cast<T>(1) - (static_cast<T>(beta) * out));
dx.device(d) = dout * ((static_cast<T>(beta) * out) + temp2);
}
};
......
......@@ -13,7 +13,6 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/attention_lstm_op.h"
#include <sys/time.h>
#include <string>
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/operators/math/cpu_vec.h"
......
......@@ -92,12 +92,12 @@ class GRUUnitKernel : public framework::OpKernel<T> {
gate_data, frame_size * 3);
// calculate activited gate
Eigen::array<int, 2> extents({{batch_size, frame_size}});
Eigen::array<int, 2> u_offsets({{0, 0}});
Eigen::array<int, 2> extents = {batch_size, frame_size};
Eigen::array<int, 2> u_offsets = {0, 0};
ActCompute(context.Attr<int>("gate_activation"), place,
g.slice(u_offsets, extents), g.slice(u_offsets, extents));
auto u = g.slice(u_offsets, extents); // update gate
Eigen::array<int, 2> r_offsets({{0, frame_size}});
Eigen::array<int, 2> r_offsets = {0, frame_size};
ActCompute(context.Attr<int>("gate_activation"), place,
g.slice(r_offsets, extents), g.slice(r_offsets, extents));
auto r = g.slice(r_offsets, extents); // reset gate
......@@ -107,7 +107,7 @@ class GRUUnitKernel : public framework::OpKernel<T> {
weight_data + frame_size * frame_size * 2, frame_size, 1,
gate_data + frame_size * 2, frame_size * 3);
Eigen::array<int, 2> c_offsets({{0, frame_size * 2}});
Eigen::array<int, 2> c_offsets = {0, frame_size * 2};
ActCompute(context.Attr<int>("activation"), place,
g.slice(c_offsets, extents), g.slice(c_offsets, extents));
auto c = g.slice(c_offsets, extents); // output candidate
......@@ -171,12 +171,12 @@ class GRUUnitGradKernel : public framework::OpKernel<T> {
int batch_size = input->dims()[0];
int frame_size = hidden_prev->dims()[1];
Eigen::array<int, 2> extents({{batch_size, frame_size}});
Eigen::array<int, 2> u_offsets({{0, 0}});
Eigen::array<int, 2> extents = {batch_size, frame_size};
Eigen::array<int, 2> u_offsets = {0, 0};
auto u = g.slice(u_offsets, extents); // update gate
Eigen::array<int, 2> r_offsets({{0, frame_size}});
Eigen::array<int, 2> r_offsets = {0, frame_size};
auto r = g.slice(r_offsets, extents); // reset gate
Eigen::array<int, 2> c_offsets({{0, frame_size * 2}});
Eigen::array<int, 2> c_offsets = {0, frame_size * 2};
auto c = g.slice(c_offsets, extents); // output candidate
// backward for unactivated update gate
......
......@@ -38,7 +38,8 @@ class LabelSmoothKernel : public framework::OpKernel<T> {
auto dist = framework::EigenVector<T>::Flatten(*dist_t);
out.device(dev) =
static_cast<T>(1 - epsilon) * in +
epsilon * dist.broadcast(Eigen::DSizes<int, 1>(in_t->numel()));
static_cast<T>(epsilon) *
dist.broadcast(Eigen::DSizes<int, 1>(in_t->numel()));
} else {
out.device(dev) = static_cast<T>(1 - epsilon) * in +
static_cast<T>(epsilon / label_dim);
......
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