Make CUDACachingAllocator::recordStream() a no-op on null ptrs (pytor…

…ch#20658)

Summary:
Fixes pytorch#20651

Communication collectives in `torch.distributed` call `CUDACachingAllocator::recordStream()` on input and output tensors to prevent their memory blocks being freed too early. `CUDACachingAllocator` uses tensor's data pointer to track memory blocks, which does not accept null pointers. However, empty tensor's `storage().data()` might be null. In this case, as there is no associated memory block for the empty tensor, it should be fine to make `recordStream()` a no-op.

Tests only cover `broadcast` empty tensors for GLOO backend, because GLOO does not support empty inputs (facebookincubator/gloo/issues/179). It can be addressed in either `ProcessGroupGloo` or GLOO itself. Will add more tests when that gap is filled.
Pull Request resolved: pytorch#20658

Differential Revision: D15399371

Pulled By: mrshenli

fbshipit-source-id: d29ebd1c72fddae49531f32695f81b89e42e5a4d

c10/cuda/CUDACachingAllocator.cpp

@@ -378,17 +378,21 @@ struct THCCachingAllocator
 
   void recordStream(void* ptr, cuda::CUDAStream stream)
   {
-    std::lock_guard<std::recursive_mutex> lock(mutex);
-    Block* block = find_allocated_block(ptr);
-    if (!block) {
-      AT_ERROR("invalid device pointer: ", ptr);
-    }
-    if (stream.stream() == block->stream) {
-      // ignore uses on the allocation stream, since those don't require any
-      // special synchronization
-      return;
+    // Empty tensor's storage().data() might be a null ptr. As there is no
+    // blocks associated with those tensors, it is fine to do nothing here.
+    if (ptr) {
+      std::lock_guard<std::recursive_mutex> lock(mutex);
+      Block* block = find_allocated_block(ptr);
+      if (!block) {
+        AT_ERROR("invalid device pointer: ", ptr);
+      }
+      if (stream.stream() == block->stream) {
+        // ignore uses on the allocation stream, since those don't require any
+        // special synchronization
+        return;
+      }
+      block->stream_uses.insert(stream);
     }
-    block->stream_uses.insert(stream);
   }
 
   /** moves a block into a pool of cached free blocks */

test/test_c10d.py

@@ -579,6 +579,14 @@ def opts(self, threads=2):
         opts.threads = threads
         return opts
 
+    def test_empty_tensors(self):
+        store = c10d.FileStore(self.file.name, self.world_size)
+        pg = c10d.ProcessGroupGloo(store, self.rank, self.world_size, self.opts())
+
+        xs = [torch.FloatTensor([])]
+        pg.broadcast(xs).wait()
+        self.assertEqual(0, xs[0].numel())
+
     def test_broadcast_checks(self):
         store = c10d.FileStore(self.file.name, self.world_size)
         pg = c10d.ProcessGroupGloo(store, self.rank, self.world_size, self.opts())
@@ -1344,6 +1352,30 @@ def setUp(self):
     def tearDown(self):
         pass
 
+    def test_empty_tensors(self):
+        store = c10d.FileStore(self.file.name, self.world_size)
+        pg = c10d.ProcessGroupNCCL(store, self.rank, self.world_size)
+
+        xs = [torch.cuda.FloatTensor([])]
+        pg.broadcast(xs).wait()
+        self.assertEqual(0, xs[0].numel())
+
+        pg.allreduce(xs).wait()
+        self.assertEqual(0, xs[0].numel())
+
+        pg.reduce(xs).wait()
+        self.assertEqual(0, xs[0].numel())
+
+        ys = [[torch.cuda.FloatTensor([]) for _ in range(self.world_size)]]
+        pg.allgather(ys, xs).wait()
+        for y in ys[0]:
+            self.assertEqual(0, y.numel())
+
+        ys = [torch.cuda.FloatTensor([])]
+        xs = [[torch.cuda.FloatTensor([]) for _ in range(self.world_size)]]
+        pg.reduce_scatter(ys, xs).wait()
+        self.assertEqual(0, ys[0].numel())
+
     def test_broadcast_ops(self):
         store = c10d.FileStore(self.file.name, self.world_size)
         pg = c10d.ProcessGroupNCCL(store, self.rank, self.world_size)