Revert "[bugfix] Explicitly unpin tensoradapter allocated arrays (#3997)" (#4061)

This reverts commit fdd1fe19.

include/dgl/runtime/ndarray.h

@@ -415,8 +415,6 @@ struct NDArray::Container {
   std::vector<int64_t> stride_;
   /*! \brief The internal array object */
   std::atomic<int> ref_counter_{0};
-
-  bool from_tensor_dispatcher_{false};
 };
 
 // implementations of inline functions

src/runtime/ndarray.cc

@@ -78,12 +78,6 @@ struct NDArray::Internal {
   // This enables us to create NDArray from memory allocated by other
   // frameworks that are DLPack compatible
   static void DLPackDeleter(NDArray::Container* ptr) {
-    if (ptr->from_tensor_dispatcher_) {
-       if (IsDataPinned(&(ptr->dl_tensor))) {
-        UnpinData(&(ptr->dl_tensor));
-      }
-    }
-
     DLManagedTensor* tensor = static_cast<DLManagedTensor*>(ptr->manager_ctx);
     if (tensor->deleter != nullptr) {
       (*tensor->deleter)(tensor);
@@ -216,11 +210,8 @@ NDArray NDArray::Empty(std::vector<int64_t> shape,
                        DLDataType dtype,
                        DLContext ctx) {
   TensorDispatcher* td = TensorDispatcher::Global();
-  if (td->IsAvailable()) {
-    auto nd = td->Empty(shape, dtype, ctx);
-    nd.data_->from_tensor_dispatcher_ = true;
-    return nd;
-  }
+  if (td->IsAvailable())
+    return td->Empty(shape, dtype, ctx);
 
   NDArray ret = Internal::Create(shape, dtype, ctx);
   // setup memory content

tests/pytorch/test_unpin_tensoradapter.py

-import backend as F
-import dgl
-import gc
-import unittest
-import torch
-
-
-@unittest.skipIf(F.ctx().type == 'cpu', reason='Pinning memory tests require GPU.')
-def test_unpin_tensoradapater():
-    # run a sufficient number of iterations such that the memory pool should be
-    # re-used
-    for j in range(3):
-        t = F.zerocopy_from_dlpack(dgl.ndarray.empty(
-            [10000, 10],
-            F.reverse_data_type_dict[F.float32],
-            ctx=dgl.utils.to_dgl_context(torch.device('cpu'))).to_dlpack()).zero_()
-        assert not F.is_pinned(t)
-        dgl.utils.pin_memory_inplace(t)
-        assert F.is_pinned(t)
-        del t