Eliminate storage views. (pytorch#9466)

Summary:
Storage views were previously used to implement CUDA IPC sharing,
but they weren't necessary.  The new strategy is described in
Note [CUDA IPC and the caching allocator].

This also fixes an unrelated bug, where we weren't actually using
the Tensor forking pickler, because we didn't register a pickler
for torch.Tensor.

Fixes pytorch#9447.  Fixes pytorch#46.

Signed-off-by: Edward Z. Yang <ezyang@fb.com>

CC apaszke
Pull Request resolved: pytorch#9466

Reviewed By: apaszke

Differential Revision: D8859698

Pulled By: ezyang

fbshipit-source-id: 3362cb92f6ae4aa37084c57d79b31004bd0b4a97

aten/src/TH/THStorage.cpp

@@ -27,9 +27,6 @@ void THStorage_free(THStorage *storage) {
       }
       storage->finalizer.~unique_ptr<THFinalizer>();
       storage->data_ptr.~DataPtr();
-      if (storage->flag & TH_STORAGE_VIEW) {
-        THStorage_free(storage->view);
-      }
       THStorage_weakFree(storage);
     }
   }
@@ -227,6 +224,5 @@ void THStorage_swap(THStorage *storage1, THStorage *storage2)
     SWAP(flag);
     SWAP(allocator);
     SWAP(finalizer);
-    SWAP(view);
 #undef SWAP
 }

aten/src/TH/THStorage.hpp

@@ -47,7 +47,6 @@ typedef struct THStorage
     char flag;
     at::Allocator *allocator;
     std::unique_ptr<THFinalizer> finalizer;
-    struct THStorage *view;
 
     template <typename T>
     inline T * data() const {

aten/src/TH/generic/THStorage.h

@@ -22,7 +22,6 @@
 
 #define TH_STORAGE_REFCOUNTED 1
 #define TH_STORAGE_RESIZABLE  2
-#define TH_STORAGE_VIEW       8
 
 // Struct definition is moved to THStorage.hpp (so this file stays C compatible)
 typedef struct THStorage THStorage;

aten/src/THC/THCStorage.cpp

@@ -55,9 +55,6 @@ void THCStorage_free(THCState *state, THCStorage *storage)
       }
       storage->finalizer.~unique_ptr<THFinalizer>();
       storage->data_ptr.~DataPtr();
-      if (storage->flag & TH_STORAGE_VIEW) {
-        THCStorage_free(state, storage->view);
-      }
       THStorage_weakFree(storage);
     }
   }

test/test_multiprocessing.py

@@ -207,18 +207,15 @@ def test_receive():
     def _test_preserve_sharing(self, ctx=mp, repeat=1):
         def do_test():
             x = torch.randn(5, 5)
-            data = [x.storage(), x.storage()[1:4], x, x[2], x[:, 1]]
+            data = [x.storage(), x, x[2], x[:, 1]]
             q = ctx.Queue()
             q.put(data)
             new_data = q.get(timeout=1)
             self.assertEqual(new_data, data, 0)
             storage_cdata = data[0]._cdata
             self.assertEqual(new_data[0]._cdata, storage_cdata)
-            for t in new_data[2:]:
+            for t in new_data[1:]:
                 self.assertEqual(t.storage()._cdata, storage_cdata)
-            # TODO: enable after fixing #46
-            # new_data[0].fill_(10)
-            # self.assertEqual(new_data[1], new_data[0][1:4], 0)
 
         with leak_checker(self):
             for i in range(repeat):
@@ -335,7 +332,13 @@ def test_cuda_small_tensors(self):
             self.assertEqual(v, torch.arange(i * 5., (i + 1) * 5).sum())
             self.assertEqual(device, i % 2)
             self.assertEqual(tensor_size, 5)
-            self.assertEqual(storage_size, 5)
+            # You might think this should be the case, but it's not!  After
+            # data from the CUDA caching allocator goes through IPC, the
+            # size of the storage is the size of the *cached cudaMalloc for
+            # the entire memory block* of the storage, not just the storage.
+            # See Note [CUDA IPC and the caching allocator] for more info
+            #
+            # self.assertEqual(storage_size, 5)
 
     @unittest.skipIf(IS_WINDOWS, 'not applicable to Windows (only fails with fork)')
     @unittest.skipIf(not torch.cuda.is_available(), 'CUDA not available')

test/test_torch.py

@@ -6456,17 +6456,6 @@ def test_storage(self):
         self.assertEqual(v.storage()[0], v.data[0][0])
         self.assertEqual(v.storage()[14], v.data[2][4])
 
-    def test_storageview(self):
-        s1 = torch.LongStorage((3, 4, 5))
-        s2 = torch.LongStorage(s1, 1)
-
-        self.assertEqual(s2.size(), 2)
-        self.assertEqual(s2[0], s1[1])
-        self.assertEqual(s2[1], s1[2])
-
-        s2[1] = 13
-        self.assertEqual(13, s1[2])
-
     def test_nonzero(self):
         num_src = 12
 
@@ -6732,14 +6721,14 @@ def test_parsing_intlist(self):
 
     def _test_serialization_data(self):
         a = [torch.randn(5, 5).float() for i in range(2)]
-        b = [a[i % 2] for i in range(4)]
-        b += [a[0].storage()]
-        b += [a[0].storage()[1:4]]
-        b += [torch.arange(1, 11).int()]
-        t1 = torch.FloatTensor().set_(a[0].storage()[1:4], 0, (3,), (1,))
-        t2 = torch.FloatTensor().set_(a[0].storage()[1:4], 0, (3,), (1,))
-        b += [(t1.storage(), t1.storage(), t2.storage())]
-        b += [a[0].storage()[0:2]]
+        b = [a[i % 2] for i in range(4)]  # 0-3
+        b += [a[0].storage()]  # 4
+        b += [a[0].reshape(-1)[1:4].storage()]  # 5
+        b += [torch.arange(1, 11).int()]  # 6
+        t1 = torch.FloatTensor().set_(a[0].reshape(-1)[1:4].clone().storage(), 0, (3,), (1,))
+        t2 = torch.FloatTensor().set_(a[0].reshape(-1)[1:4].clone().storage(), 0, (3,), (1,))
+        b += [(t1.storage(), t1.storage(), t2.storage())]  # 7
+        b += [a[0].reshape(-1)[0:2].storage()]  # 8
         return b
 
     def _test_serialization_assert(self, b, c):
@@ -6754,7 +6743,10 @@ def _test_serialization_assert(self, b, c):
         self.assertEqual(c[4], torch.FloatStorage(25).fill_(10), 0)
         c[1].fill_(20)
         self.assertEqual(c[1], c[3], 0)
-        self.assertEqual(c[4][1:4], c[5], 0)
+        # I have to do it in this roundabout fashion, because there's no
+        # way to slice storages
+        for i in range(4):
+            self.assertEqual(c[4][i + 1], c[5][i])
 
         # check that serializing the same storage view object unpickles
         # it as one object not two (and vice versa)
@@ -6914,7 +6906,7 @@ def test_serialization_backwards_compat(self):
         a = [torch.arange(1 + i, 26 + i).view(5, 5).float() for i in range(2)]
         b = [a[i % 2] for i in range(4)]
         b += [a[0].storage()]
-        b += [a[0].storage()[1:4]]
+        b += [a[0].reshape(-1)[1:4].clone().storage()]
         path = download_file('https://download.pytorch.org/test_data/legacy_serialized.pt')
         c = torch.load(path)
         self.assertEqual(b, c, 0)
@@ -6928,7 +6920,6 @@ def test_serialization_backwards_compat(self):
         self.assertEqual(c[4], torch.FloatStorage(25).fill_(10), 0)
         c[1].fill_(20)
         self.assertEqual(c[1], c[3], 0)
-        self.assertEqual(c[4][1:4], c[5], 0)
 
         # test some old tensor serialization mechanism
         class OldTensorBase(object):

torch/csrc/generic/Storage.cpp

@@ -88,44 +88,8 @@ static PyObject * THPStorage_(pynew)(PyTypeObject *type, PyObject *args, PyObjec
 
   // torch.Storage(view_source, [offset, [size]])
   if (num_args < 4 && THPStorage_(Check)(first_arg)) {
-#ifdef THD_GENERIC_FILE
-    THPUtils_setError("distributed storages don't support storage views");
+    THPUtils_setError("storage views not supported");
     return NULL;
-#else
-    THPStorage *storage_arg = (THPStorage *)first_arg;
-    int64_t numel = storage_arg->cdata->size;
-    int64_t offset = 0;
-
-    if (num_args >= 2) {
-      PyObject *second_arg = PyTuple_GET_ITEM(args, 1);
-      if (!THPUtils_checkLong(second_arg))
-        goto invalid_arguments;
-      offset = THPUtils_unpackLong(second_arg);
-    }
-
-    int64_t size = numel - offset;
-    if (num_args >= 3) {
-      PyObject *third_arg = PyTuple_GET_ITEM(args, 2);
-      if (!THPUtils_checkLong(third_arg))
-        goto invalid_arguments;
-      size = THPUtils_unpackLong(third_arg);
-    }
-
-    THPUtils_assert(offset >= 0 && offset <= numel, "specified an offset of "
-        "%" PRId64 ", but the viewed storage has only %" PRId64 " element(s)", offset, numel);
-    THPUtils_assert(size >= 1 && size <= numel - offset, "specified a size of "
-        "%" PRId64 ", but the viewed storage has only %" PRId64 " element(s) after offset %" PRId64,
-        size, numel - offset, offset);
-
-    real *data_ptr = THWStorage_(data)(LIBRARY_STATE storage_arg->cdata) + offset;
-    // TODO: Hmmmm
-    THWStoragePtr storage(THWStorage_(newWithDataAndAllocator)(LIBRARY_STATE {data_ptr, storage_arg->cdata->data_ptr.device()} /* non-owning */, size, nullptr));
-    storage->flag = TH_STORAGE_REFCOUNTED | TH_STORAGE_VIEW;
-    storage->view = storage_arg->cdata;
-    THWStorage_(retain)(LIBRARY_STATE storage_arg->cdata);
-    self->cdata = storage.release();
-    return (PyObject*)self.release();
-#endif
   }
 
   // torch.Storage(sequence)
@@ -161,9 +125,6 @@ static PyObject * THPStorage_(pynew)(PyTypeObject *type, PyObject *args, PyObjec
 #endif
   }
 
-#ifndef THD_GENERIC_FILE
-invalid_arguments:
-#endif
   THPUtils_invalidArguments(args, kwargs, THPStorageStr " constructor", 6,
           "no arguments",
           "(int size)",
@@ -199,30 +160,8 @@ static PyObject * THPStorage_(get)(THPStorage *self, PyObject *index)
     return THPUtils_(newReal)(value);
   /* Slice index */
   } else if (PySlice_Check(index)) {
-#ifdef THD_GENERIC_FILE
-    THPUtils_setError("distributed storages don't support slicing");
+    THPUtils_setError("storages don't support slicing");
     return NULL;
-#else
-    Py_ssize_t start, stop, slicelength, step;
-    int64_t len = THWStorage_(size)(LIBRARY_STATE self->cdata);
-    if (!THPUtils_parseSlice(index, len, &start, &stop, &step, &slicelength))
-      return NULL;
-    if (step != 1) {
-      THPUtils_setError("Trying to slice with a step of %" PRId64 ", but only a step of "
-          "1 is supported", (int64_t)step);
-      return NULL;
-    }
-
-    real *data = THWStorage_(data)(LIBRARY_STATE self->cdata);
-    THWStoragePtr new_storage(THWStorage_(newWithDataAndAllocator)(LIBRARY_STATE {static_cast<void*>(data + start), self->cdata->data_ptr.device()} /* non-owning */, slicelength, nullptr));
-    new_storage->flag = TH_STORAGE_REFCOUNTED | TH_STORAGE_VIEW;
-    new_storage->view = self->cdata;
-    THWStorage_(retain)(LIBRARY_STATE self->cdata);
-
-    PyObject *_ret = THPStorage_(New)(new_storage);
-    new_storage.release();
-    return _ret;
-#endif
   }
   PyErr_Format(PyExc_TypeError, "can't index a " THPStorageStr " with %s",
       THPUtils_typename(index));

torch/csrc/generic/StorageMethods.cpp

@@ -292,26 +292,6 @@ PyObject * THPStorage_(_setCdata)(THPStorage *self, PyObject *new_cdata)
   END_HANDLE_TH_ERRORS
 }
 
-#ifndef THD_GENERIC_FILE
-PyObject * THPStorage_(_rootStorage)(THPStorage *self)
-{
-  HANDLE_TH_ERRORS
-  if (!(self->cdata->flag & TH_STORAGE_VIEW)) {
-    return Py_BuildValue("(ON)", self, PyLong_FromLong(0));
-  }
-  THWStorage *root = self->cdata;
-  while (root->flag & TH_STORAGE_VIEW)
-    root = root->view;
-  size_t offset = THWStorage_(data)(LIBRARY_STATE self->cdata) - THWStorage_(data)(LIBRARY_STATE root);
-  THWStorage_(retain)(LIBRARY_STATE root);
-  THPObjectPtr storage(THPStorage_(New)(root));
-  PyObject *result = Py_BuildValue("(NN)", storage.get(), PyLong_FromLong(offset));
-  storage.release();
-  return result;
-  END_HANDLE_TH_ERRORS
-}
-#endif
-
 static PyMethodDef THPStorage_(methods)[] = {
   {"copy_", (PyCFunction)THPStorage_(copy_), METH_VARARGS | METH_KEYWORDS, NULL},
   {"element_size", (PyCFunction)THPStorage_(elementSize), METH_NOARGS, NULL},
@@ -335,7 +315,6 @@ static PyMethodDef THPStorage_(methods)[] = {
 #endif
   {"_set_cdata", (PyCFunction)THPStorage_(_setCdata), METH_O, NULL},
 #ifndef THD_GENERIC_FILE
-  {"_root_storage", (PyCFunction)THPStorage_(_rootStorage), METH_NOARGS, NULL},
 #endif
   {NULL}
 };