|
|
|
|
@ -31,93 +31,28 @@ class SeqExpandKernel : public framework::OpKernel<T> {
|
|
|
|
|
auto* out = context.Output<LoDTensor>("Out");
|
|
|
|
|
const T* x_data = x->data<T>();
|
|
|
|
|
auto x_dims = x->dims();
|
|
|
|
|
auto x_lod = x->lod();
|
|
|
|
|
|
|
|
|
|
framework::Vector<size_t> level;
|
|
|
|
|
size_t num = (x_lod.size() == 0) ? (x->dims()[0] + 1) : x_lod[0].size();
|
|
|
|
|
for (int i = 0; i < num; ++i) {
|
|
|
|
|
level.push_back(i);
|
|
|
|
|
}
|
|
|
|
|
x_lod.push_back(level);
|
|
|
|
|
|
|
|
|
|
size_t repeat = static_cast<size_t>(context.Attr<int>("repeat"));
|
|
|
|
|
framework::Vector<size_t> scales;
|
|
|
|
|
if (repeat != 0) {
|
|
|
|
|
for (int i = 0; i < x_lod[0].size() - 1; ++i) {
|
|
|
|
|
scales.push_back(repeat);
|
|
|
|
|
}
|
|
|
|
|
std::vector<int64_t> dims = framework::vectorize(x->dims());
|
|
|
|
|
dims[0] = dims[0] * repeat;
|
|
|
|
|
auto out_dims = framework::make_ddim(dims);
|
|
|
|
|
out->Resize(out_dims);
|
|
|
|
|
} else {
|
|
|
|
|
auto* y = context.Input<LoDTensor>("Y");
|
|
|
|
|
auto y_lod = y->lod();
|
|
|
|
|
auto y_abs_lod = y_lod.ToAbsOffset();
|
|
|
|
|
auto x_abs_lod = x_lod.ToAbsOffset();
|
|
|
|
|
for (int i = 0; i < y_abs_lod[0].size() - 1; ++i) {
|
|
|
|
|
scales.push_back((y_abs_lod[0][i + 1] - y_abs_lod[0][i]) /
|
|
|
|
|
(x_abs_lod[0][i + 1] - x_abs_lod[0][i]));
|
|
|
|
|
}
|
|
|
|
|
out->Resize(y->dims());
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
framework::Vector<size_t> indexes;
|
|
|
|
|
for (int size_t i = 0; i < x_lod[0]; ++i) {
|
|
|
|
|
indexes[i] = x_lod[0];
|
|
|
|
|
}
|
|
|
|
|
framework::LoD out_lod;
|
|
|
|
|
auto level0 = framework::expand_lod(indexes, x_lod[0], scales, false);
|
|
|
|
|
out_lod.push_back(level0);
|
|
|
|
|
for (int i = 1; i < x_lod.size(); ++i) {
|
|
|
|
|
for (int j = 0; j < indexes.size(); ++j) {
|
|
|
|
|
indexes[j] = x_lod[i - 1][indexes[j]];
|
|
|
|
|
}
|
|
|
|
|
out_lod.push_back(framework::expand_lod(x_lod[i], indexes, scales, true));
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
auto* y = context.Input<LoDTensor>("Y");
|
|
|
|
|
PADDLE_ENFORCE_EQ(x_dims[0], y->lod().back().size() - 1,
|
|
|
|
|
"The size of last lod level in Input(Y)"
|
|
|
|
|
"must be equal to dims[0] of Input(X).");
|
|
|
|
|
out->set_lod(y->lod());
|
|
|
|
|
out->Resize(y->dims());
|
|
|
|
|
auto place = context.GetEigenDevice<Place>();
|
|
|
|
|
size_t element_len = framework::product(x_dims) / x_dims[0];
|
|
|
|
|
T* out_data = out->mutable_data<T>(context.GetPlace());
|
|
|
|
|
|
|
|
|
|
// copy data
|
|
|
|
|
auto place = context.GetPlace();
|
|
|
|
|
size_t count = 0;
|
|
|
|
|
if (platform::is_cpu_place(place)) {
|
|
|
|
|
auto& cpu_place = boost::get<platform::CPUPlace>(place);
|
|
|
|
|
for (size_t i = 0; i < scales.size(); ++i) {
|
|
|
|
|
count = element_len * (x_abs_lod[0][i + 1] - x_abs_lod[0][i]);
|
|
|
|
|
for (size_t j = 0; j < scales[i]; ++j) {
|
|
|
|
|
memory::Copy(cpu_place, out_data, cpu_place, x_data,
|
|
|
|
|
sizeof(T) * count);
|
|
|
|
|
out_data += count;
|
|
|
|
|
}
|
|
|
|
|
x_data += count;
|
|
|
|
|
}
|
|
|
|
|
} else {
|
|
|
|
|
#ifdef PADDLE_WITH_CUDA
|
|
|
|
|
auto& gpu_place = boost::get<platform::GPUPlace>(place);
|
|
|
|
|
auto stream = reinterpret_cast<const platform::CUDADeviceContext&>(
|
|
|
|
|
context.device_context())
|
|
|
|
|
.stream();
|
|
|
|
|
for (size_t i = 0; i < scales.size(); ++i) {
|
|
|
|
|
count = element_len * (x_abs_lod[0][i + 1] - x_abs_lod[0][i]);
|
|
|
|
|
for (size_t j = 0; j < scales[i]; ++j) {
|
|
|
|
|
memory::Copy(gpu_place, out_data, gpu_place, x_data,
|
|
|
|
|
sizeof(T) * count, stream);
|
|
|
|
|
out_data += count;
|
|
|
|
|
}
|
|
|
|
|
x_data += count;
|
|
|
|
|
}
|
|
|
|
|
#else
|
|
|
|
|
PADDLE_THROW("Paddle is not compiled with GPU");
|
|
|
|
|
#endif
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
out->set_lod(out_lod);
|
|
|
|
|
for (size_t i = 0; i < lod.size; i++) {
|
|
|
|
|
for (size_t j = 0; j < lod[i].size(); j++) {
|
|
|
|
|
LOG(INFO) << "lod[" << i << "][" << j "] = " << lod[i][j];
|
|
|
|
|
}
|
|
|
|
|
auto out_starts = out->lod().back();
|
|
|
|
|
|
|
|
|
|
for (size_t i = 0; i < out_starts.size() - 1; i++) {
|
|
|
|
|
int scale = out_starts[i + 1] - out_starts[i];
|
|
|
|
|
Eigen::TensorMap<
|
|
|
|
|
Eigen::Tensor<const T, 2, Eigen::RowMajor, Eigen::DenseIndex>>
|
|
|
|
|
x_t(x_data, 1, element_len);
|
|
|
|
|
Eigen::TensorMap<Eigen::Tensor<T, 2, Eigen::RowMajor, Eigen::DenseIndex>>
|
|
|
|
|
out_t(out_data, scale, element_len);
|
|
|
|
|
Eigen::array<int, 2> cast({scale, 1});
|
|
|
|
|
out_t.device(place) = x_t.broadcast(cast);
|
|
|
|
|
x_data += element_len;
|
|
|
|
|
out_data += element_len * scale;
|
|
|
|
|
}
|
|
|
|
|
}
|
|
|
|
|
};
|
|
|
|
|
@ -130,25 +65,24 @@ class SeqExpandGradKernel : public framework::OpKernel<T> {
|
|
|
|
|
auto* x = context.Input<LoDTensor>("X");
|
|
|
|
|
auto* out = context.Input<LoDTensor>("Out");
|
|
|
|
|
auto* d_x = context.Output<LoDTensor>(framework::GradVarName("X"));
|
|
|
|
|
auto out_lod = out->lod();
|
|
|
|
|
auto out_abs_lod = out_lod.ToAbsOffset();
|
|
|
|
|
auto out_last_level = out->lod().back();
|
|
|
|
|
d_x->set_lod(x->lod());
|
|
|
|
|
const T* d_out_data = d_out->data<T>();
|
|
|
|
|
auto d_out_dims = d_out->dims();
|
|
|
|
|
T* d_x_data = d_x->mutable_data<T>(context.GetPlace());
|
|
|
|
|
size_t element_len = framework::product(d_out_dims) / d_out_dims[0];
|
|
|
|
|
for (size_t i = 0; i < out->NumElements(); ++i) {
|
|
|
|
|
size_t ele_count = out_abs_lod[0][i + 1] - out_abs_lod[0][i];
|
|
|
|
|
size_t repeat = out->NumElements(0, i);
|
|
|
|
|
Eigen::TensorMap<Eigen::Tensor<const T, 2>> d_out_t(
|
|
|
|
|
d_out_data, static_cast<int>(repeat),
|
|
|
|
|
static_cast<int>((ele_count * element_len) / repeat));
|
|
|
|
|
Eigen::TensorMap<Eigen::Tensor<T, 1>> d_x_t(
|
|
|
|
|
d_x_data, static_cast<int>((ele_count * element_len) / repeat));
|
|
|
|
|
|
|
|
|
|
for (size_t i = 0; i < out_last_level.size() - 1; ++i) {
|
|
|
|
|
size_t repeat = out_last_level[i + 1] - out_last_level[i];
|
|
|
|
|
Eigen::TensorMap<
|
|
|
|
|
Eigen::Tensor<const T, 2, Eigen::RowMajor, Eigen::DenseIndex>>
|
|
|
|
|
d_out_t(d_out_data, static_cast<int>(repeat), element_len);
|
|
|
|
|
Eigen::TensorMap<Eigen::Tensor<T, 1, Eigen::RowMajor, Eigen::DenseIndex>>
|
|
|
|
|
d_x_t(d_x_data, static_cast<int>(element_len));
|
|
|
|
|
auto place = context.GetEigenDevice<Place>();
|
|
|
|
|
d_x_t.device(place) = d_out_t.sum(Eigen::array<int, 1>({{0}}));
|
|
|
|
|
d_out_data += (ele_count * element_len);
|
|
|
|
|
d_x_data += ((ele_count * element_len) / repeat);
|
|
|
|
|
d_out_data += (repeat * element_len);
|
|
|
|
|
d_x_data += element_len;
|
|
|
|
|
}
|
|
|
|
|
}
|
|
|
|
|
};
|
|
|
|
|
|
wanghaoshuang
7 years ago