Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into py_reader_doc

paddle/fluid/operators/adam_op.h

@@ -293,11 +293,18 @@ class AdamOpKernel : public framework::OpKernel<T> {
       auto& grad_tensor = grad_merge.value();
       const T* grad_data = grad_tensor.template data<T>();
       int64_t* rows = nullptr;
+// When compiled without CUDA, the CUDAMutableData() interface should not be
+// provided.
+#if defined(PADDLE_WITH_CUDA)
       if (platform::is_gpu_place(ctx.GetPlace())) {
         rows = grad_merge.mutable_rows()->CUDAMutableData(ctx.GetPlace());
       } else {
+#endif
         rows = grad_merge.mutable_rows()->data();
+
+#if defined(PADDLE_WITH_CUDA)
       }
+#endif
       auto row_numel = grad_tensor.numel() / grad_merge.rows().size();
 
       SparseAdamFunctor<T> functor(

paddle/fluid/operators/detection/target_assign_op.h

@@ -106,7 +106,11 @@ class TargetAssignKernel : public framework::OpKernel<T> {
     int64_t k = x->dims()[2];
 
     auto x_lod = x->lod().back();
+#if defined(PADDLE_WITH_CUDA)
     size_t* x_lod_data = x_lod.MutableData(ctx.GetPlace());
+#else
+    size_t* x_lod_data = x_lod.data();
+#endif
 
     TargetAssignFunctor<T, WT> functor(x_data, match_idx_data, x_lod_data,
                                        mismatch_value, n, m, p, k, out_data,
@@ -121,7 +125,11 @@ class TargetAssignKernel : public framework::OpKernel<T> {
       PADDLE_ENFORCE_EQ(neg_indices->lod().size(), 1UL);
       const int* neg_idx_data = neg_indices->data<int>();
       auto neg_lod = neg_indices->lod().back();
+#if defined(PADDLE_WITH_CUDA)
       size_t* neg_lod_data = neg_lod.MutableData(ctx.GetPlace());
+#else
+      size_t* neg_lod_data = neg_lod.data();
+#endif
       NegTargetAssignFunctor<DeviceContext, T, WT> neg_trg_functor;
       neg_trg_functor(device_ctx, neg_idx_data, neg_lod_data, n, m, k,
                       mismatch_value, out_data, out_wt_data);

paddle/fluid/operators/math/sequence2batch.h

@@ -78,7 +78,7 @@ class LoDTensor2BatchFunctor {
     auto lods = lod_tensor.lod();
     PADDLE_ENFORCE_EQ(lods.size(), 1UL, "Only support one level sequence now.");
 
-    auto lod = lods[0];
+    const auto& lod = lods[0];
 
     std::vector<SeqInfo> seq_info;
     for (size_t seq_id = 0; seq_id < lod.size() - 1; ++seq_id) {

python/paddle/fluid/io.py

@@ -66,7 +66,8 @@ def is_persistable(var):
             res = fluid.io.is_persistable(param)
     """
     if var.desc.type() == core.VarDesc.VarType.FEED_MINIBATCH or \
-            var.desc.type() == core.VarDesc.VarType.FETCH_LIST:
+            var.desc.type() == core.VarDesc.VarType.FETCH_LIST or \
+            var.desc.type() == core.VarDesc.VarType.READER:
         return False
     return var.persistable
 

python/paddle/fluid/layers/io.py

@@ -456,11 +456,11 @@ def py_reader(capacity,
               name=None,
               use_double_buffer=True):
     """
-    Create a python reader for data feeding in Python
+    Create a Python reader for data feeding in Python
 
     This layer returns a Reader Variable.
-    The Reader provides :code:`decorate_paddle_reader` and
+    The Reader provides :code:`decorate_paddle_reader()` and
-    :code:`decorate_tensor_provider` to set a Python generator as the data
+    :code:`decorate_tensor_provider()` to set a Python generator as the data
     source in Python side. When :code:`Executor::Run()` is invoked in C++
     side, the data from the generator would be read automatically. Unlike
     :code:`DataFeeder.feed()`, the data reading process and
@@ -561,12 +561,14 @@ def py_reader(capacity,
         >>> test_exe = fluid.ParallelExecutor(use_cuda=True,
         >>>                 loss_name=test_loss.name, main_program=test_main_prog)
         >>> for epoch_id in range(10):
+        >>>     train_reader.start()
         >>>     try:
         >>>         while True:
         >>>             train_exe.run(fetch_list=[train_loss.name])
         >>>     except fluid.core.EOFException:
         >>>         train_reader.reset()
         >>>
+        >>>     test_reader.start()
         >>>     try:
         >>>         while True:
         >>>             test_exe.run(fetch_list=[test_loss.name])