提交 133bac2b 编写于 作者: M minqiyang

Accelerate embedding op grad

test=develop
上级 c26f2b21
...@@ -68,6 +68,7 @@ class LookupTableKernel : public framework::OpKernel<T> { ...@@ -68,6 +68,7 @@ class LookupTableKernel : public framework::OpKernel<T> {
const auto *table = table_t.value().data<T>(); const auto *table = table_t.value().data<T>();
auto *output = output_t->mutable_data<T>(context.GetPlace()); auto *output = output_t->mutable_data<T>(context.GetPlace());
auto blas = math::GetBlas<platform::CPUDeviceContext, T>(context);
for (int64_t i = 0; i < ids_numel; ++i) { for (int64_t i = 0; i < ids_numel; ++i) {
if (padding_idx != kNoPadding && ids[i] == padding_idx) { if (padding_idx != kNoPadding && ids[i] == padding_idx) {
memset(output + i * row_width, 0, row_width * sizeof(T)); memset(output + i * row_width, 0, row_width * sizeof(T));
...@@ -75,8 +76,8 @@ class LookupTableKernel : public framework::OpKernel<T> { ...@@ -75,8 +76,8 @@ class LookupTableKernel : public framework::OpKernel<T> {
PADDLE_ENFORCE_GE(ids[i], 0); PADDLE_ENFORCE_GE(ids[i], 0);
auto id_index = table_t.Index(ids[i]); auto id_index = table_t.Index(ids[i]);
PADDLE_ENFORCE_GE(id_index, 0, "the input key should be exists."); PADDLE_ENFORCE_GE(id_index, 0, "the input key should be exists.");
memcpy(output + i * row_width, table + id_index * row_width, blas.VCOPY(row_width, table + id_index * row_width,
row_width * sizeof(T)); output + i * row_width);
} }
} }
} }
...@@ -111,27 +112,16 @@ class LookupTableGradKernel : public framework::OpKernel<T> { ...@@ -111,27 +112,16 @@ class LookupTableGradKernel : public framework::OpKernel<T> {
auto *ids_data = ids->data<int64_t>(); auto *ids_data = ids->data<int64_t>();
int64_t ids_num = ids->numel(); int64_t ids_num = ids->numel();
framework::Vector<int64_t> new_rows; std::vector<int64_t> new_rows;
new_rows.reserve(ids_num); new_rows.reserve(ids_num);
for (int64_t i = 0; i < ids_num; i++) { std::memcpy(new_rows.data(), ids_data, ids_num * sizeof(int64_t));
new_rows.push_back(ids_data[i]);
}
d_table->set_rows(new_rows); d_table->set_rows(new_rows);
auto *d_table_value = d_table->mutable_value(); auto *d_table_value = d_table->mutable_value();
d_table_value->Resize({ids_num, table_dim[1]}); d_table_value->Resize({ids_num, table_dim[1]});
d_table_value->mutable_data<T>(context.GetPlace()); // memory optimization will NOT reuse Tensor with SelectedRows
// so we could just share the tensor here directly.
d_table->set_height(table_dim[0]); d_table_value->ShareDataWith(*d_output);
auto *d_output_data = d_output->data<T>();
auto *d_table_data = d_table_value->data<T>();
auto d_output_dims = d_output->dims();
PADDLE_ENFORCE_EQ(
d_table_value->dims(),
framework::flatten_to_2d(d_output_dims, d_output_dims.size() - 1));
memcpy(d_table_data, d_output_data, sizeof(T) * d_output->numel());
} else { } else {
auto *ids = context.Input<LoDTensor>("Ids"); auto *ids = context.Input<LoDTensor>("Ids");
auto *d_output = context.Input<LoDTensor>(framework::GradVarName("Out")); auto *d_output = context.Input<LoDTensor>(framework::GradVarName("Out"));
......
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