提交 0a6262d5 编写于 作者: P peterzhang2029

fix warning

上级 665eb015
...@@ -43,24 +43,26 @@ class BilinearTensorProductKernel : public framework::OpKernel<T> { ...@@ -43,24 +43,26 @@ class BilinearTensorProductKernel : public framework::OpKernel<T> {
auto batch_size = x->dims()[0]; auto batch_size = x->dims()[0];
auto weight_dims = weight->dims(); auto weight_dims = weight->dims();
int Out_dim = weight_dims[0];
int X_dim = weight_dims[1];
int Y_dim = weight_dims[2];
auto place = ctx.GetEigenDevice<Place>(); auto place = ctx.GetEigenDevice<Place>();
// Create the intermediate variable to caculate the result of // Create the intermediate variable to caculate the result of
// Input(X) multiplied by Input(Weight_i), the formula is: // Input(X) multiplied by Input(Weight_i), the formula is:
// left_mul = X Weight_i. // left_mul = X Weight_i.
Tensor left_mul; Tensor left_mul;
left_mul.mutable_data<T>(framework::make_ddim({batch_size, weight_dims[2]}), left_mul.mutable_data<T>(framework::make_ddim({batch_size, Y_dim}),
ctx.GetPlace()); ctx.GetPlace());
auto left_mul_mat = EigenMatrix<T>::From(left_mul); auto left_mul_mat = EigenMatrix<T>::From(left_mul);
for (size_t i = 0; i < weight_dims[0]; ++i) { for (int i = 0; i < Out_dim; ++i) {
auto output_col_vec = output_mat.chip(i, 1); auto output_col_vec = output_mat.chip(i, 1);
Tensor weight_mat = weight->Slice(i, i + 1).Resize( Tensor weight_mat =
framework::make_ddim({weight_dims[1], weight_dims[2]})); weight->Slice(i, i + 1).Resize(framework::make_ddim({X_dim, Y_dim}));
math::gemm<Place, T>(ctx.device_context(), CblasNoTrans, CblasNoTrans, math::gemm<Place, T>(ctx.device_context(), CblasNoTrans, CblasNoTrans,
batch_size, weight_dims[2], weight_dims[1], 1, batch_size, Y_dim, X_dim, 1, x->data<T>(),
x->data<T>(), weight_mat.data<T>(), 0, weight_mat.data<T>(), 0, left_mul.data<T>());
left_mul.data<T>());
output_col_vec.device(place) = output_col_vec.device(place) =
(left_mul_mat * y_mat).sum(Eigen::DSizes<int, 1>(1)); (left_mul_mat * y_mat).sum(Eigen::DSizes<int, 1>(1));
} }
...@@ -87,6 +89,9 @@ class BilinearTensorProductGradKernel : public framework::OpKernel<T> { ...@@ -87,6 +89,9 @@ class BilinearTensorProductGradKernel : public framework::OpKernel<T> {
auto batch_size = x->dims()[0]; auto batch_size = x->dims()[0];
auto weight_dims = weight->dims(); auto weight_dims = weight->dims();
int Out_dim = weight_dims[0];
int X_dim = weight_dims[1];
int Y_dim = weight_dims[2];
auto x_mat = EigenMatrix<T>::From(*x); auto x_mat = EigenMatrix<T>::From(*x);
auto y_mat = EigenMatrix<T>::From(*y); auto y_mat = EigenMatrix<T>::From(*y);
...@@ -95,13 +100,13 @@ class BilinearTensorProductGradKernel : public framework::OpKernel<T> { ...@@ -95,13 +100,13 @@ class BilinearTensorProductGradKernel : public framework::OpKernel<T> {
// Create the intermediate variable to caculate the Output(Y@Grad). // Create the intermediate variable to caculate the Output(Y@Grad).
Tensor x_scale; Tensor x_scale;
x_scale.mutable_data<T>(framework::make_ddim({batch_size, weight_dims[1]}), x_scale.mutable_data<T>(framework::make_ddim({batch_size, X_dim}),
ctx.GetPlace()); ctx.GetPlace());
auto x_scale_mat = EigenMatrix<T>::From(x_scale); auto x_scale_mat = EigenMatrix<T>::From(x_scale);
// Create the intermediate variable to caculate the Output(X@Grad). // Create the intermediate variable to caculate the Output(X@Grad).
Tensor y_scale; Tensor y_scale;
y_scale.mutable_data<T>(framework::make_ddim({batch_size, weight_dims[2]}), y_scale.mutable_data<T>(framework::make_ddim({batch_size, Y_dim}),
ctx.GetPlace()); ctx.GetPlace());
auto y_scale_mat = EigenMatrix<T>::From(y_scale); auto y_scale_mat = EigenMatrix<T>::From(y_scale);
...@@ -121,11 +126,11 @@ class BilinearTensorProductGradKernel : public framework::OpKernel<T> { ...@@ -121,11 +126,11 @@ class BilinearTensorProductGradKernel : public framework::OpKernel<T> {
// Caculate the Output(X@Grad) and Output(Y@Grad). // Caculate the Output(X@Grad) and Output(Y@Grad).
if (d_x || d_y) { if (d_x || d_y) {
Eigen::DSizes<int, 2> bcast_for_x(1, weight_dims[2]); Eigen::DSizes<int, 2> bcast_for_x(1, Y_dim);
Eigen::DSizes<int, 2> bcast_for_y(1, weight_dims[1]); Eigen::DSizes<int, 2> bcast_for_y(1, X_dim);
for (int i = 0; i < weight_dims[0]; ++i) { for (int i = 0; i < Out_dim; ++i) {
Tensor weight_i = weight->Slice(i, i + 1).Resize( Tensor weight_i = weight->Slice(i, i + 1).Resize(
framework::make_ddim({weight_dims[1], weight_dims[2]})); framework::make_ddim({X_dim, Y_dim}));
auto output_vec = d_out_mat.chip(i, 1); auto output_vec = d_out_mat.chip(i, 1);
if (d_x) { if (d_x) {
y_scale_mat.device(place) = y_scale_mat.device(place) =
...@@ -133,9 +138,8 @@ class BilinearTensorProductGradKernel : public framework::OpKernel<T> { ...@@ -133,9 +138,8 @@ class BilinearTensorProductGradKernel : public framework::OpKernel<T> {
.broadcast(bcast_for_x) * .broadcast(bcast_for_x) *
y_mat; y_mat;
math::gemm<Place, T>(ctx.device_context(), CblasNoTrans, CblasTrans, math::gemm<Place, T>(ctx.device_context(), CblasNoTrans, CblasTrans,
batch_size, weight_dims[1], weight_dims[2], 1, batch_size, X_dim, Y_dim, 1, y_scale.data<T>(),
y_scale.data<T>(), weight_i.data<T>(), 1, weight_i.data<T>(), 1, d_x->data<T>());
d_x->data<T>());
} }
if (d_y) { if (d_y) {
x_scale_mat.device(place) = x_scale_mat.device(place) =
...@@ -143,9 +147,8 @@ class BilinearTensorProductGradKernel : public framework::OpKernel<T> { ...@@ -143,9 +147,8 @@ class BilinearTensorProductGradKernel : public framework::OpKernel<T> {
.broadcast(bcast_for_y) * .broadcast(bcast_for_y) *
x_mat; x_mat;
math::gemm<Place, T>(ctx.device_context(), CblasNoTrans, CblasNoTrans, math::gemm<Place, T>(ctx.device_context(), CblasNoTrans, CblasNoTrans,
batch_size, weight_dims[2], weight_dims[1], 1, batch_size, Y_dim, X_dim, 1, x_scale.data<T>(),
x_scale.data<T>(), weight_i.data<T>(), 1, weight_i.data<T>(), 1, d_y->data<T>());
d_y->data<T>());
} }
} }
} }
...@@ -153,19 +156,18 @@ class BilinearTensorProductGradKernel : public framework::OpKernel<T> { ...@@ -153,19 +156,18 @@ class BilinearTensorProductGradKernel : public framework::OpKernel<T> {
// Caculate the gradient of Input(Weight). // Caculate the gradient of Input(Weight).
if (d_weight) { if (d_weight) {
d_weight->mutable_data<T>(ctx.GetPlace()); d_weight->mutable_data<T>(ctx.GetPlace());
Eigen::DSizes<int, 2> bcast_for_weight(1, weight_dims[1]); Eigen::DSizes<int, 2> bcast_for_weight(1, X_dim);
for (int i = 0; i < weight_dims[0]; ++i) { for (int i = 0; i < Out_dim; ++i) {
Tensor d_weight_i = d_weight->Slice(i, i + 1).Resize( Tensor d_weight_i = d_weight->Slice(i, i + 1).Resize(
framework::make_ddim({weight_dims[1], weight_dims[2]})); framework::make_ddim({X_dim, Y_dim}));
auto output_vec = d_out_mat.chip(i, 1); auto output_vec = d_out_mat.chip(i, 1);
x_scale_mat.device(place) = x_scale_mat.device(place) =
output_vec.reshape(Eigen::DSizes<int, 2>(batch_size, 1)) output_vec.reshape(Eigen::DSizes<int, 2>(batch_size, 1))
.broadcast(bcast_for_weight) * .broadcast(bcast_for_weight) *
x_mat; x_mat;
math::gemm<Place, T>(ctx.device_context(), CblasTrans, CblasNoTrans, math::gemm<Place, T>(ctx.device_context(), CblasTrans, CblasNoTrans,
weight_dims[1], weight_dims[2], batch_size, 1, X_dim, Y_dim, batch_size, 1, x_scale.data<T>(),
x_scale.data<T>(), y->data<T>(), 0, y->data<T>(), 0, d_weight_i.data<T>());
d_weight_i.data<T>());
} }
} }
......
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