We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Device-to-host copy in `examples/stream/cupy_memcpy.py` is not asynchronous
I've interested in asynchronous memcpy for better performance. I checked the `cupy_memcpy.py` sample, and noticed that the last line `x_pinned_cpu = x_gpu.get()` [1] shouldn't work asynchronously. Unfortunately I don't know how to properly fix it.

One issue is trivial: this line re-binds the variable `x_pinned_cpu`, instead of updating the value bound to this variable.

But there is another more tricky problem. The function `cupy.ndarray.get` creates the new `numpy.ndarray` instance by calling `numpy.empty`, and passes the new host pointer to the `copy_to_host_async` function [2]. IIUC, as the new array not allocated in pinned memory, the copy couldn't be asynchronous with other computations at the GPU.

* [1] https://github.com/cupy/cupy/blob/v5.0.0rc1/examples/stream/cupy_memcpy.py#L24
* [2] https://github.com/cupy/cupy/blob/v5.0.0rc1/cupy/core/core.pyx#L1805

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `examples/stream/cupy_memcpy.py`
Content:
```
1 # nvprof --print-gpu-trace python examples/stream/cupy_memcpy.py
2 import cupy
3 import numpy
4 
5 pinned_memory_pool = cupy.cuda.PinnedMemoryPool()
6 cupy.cuda.set_pinned_memory_allocator(pinned_memory_pool.malloc)
7 
8 
9 def _pin_memory(array):
10     mem = cupy.cuda.alloc_pinned_memory(array.nbytes)
11     ret = numpy.frombuffer(mem, array.dtype, array.size).reshape(array.shape)
12     ret[...] = array
13     return ret
14 
15 
16 x_cpu = numpy.array([1, 2, 3], dtype=numpy.float32)
17 x_pinned_cpu = _pin_memory(x_cpu)
18 x_gpu = cupy.core.ndarray((3,), dtype=numpy.float32)
19 with cupy.cuda.stream.Stream():
20     x_gpu.set(x_pinned_cpu)
21 
22 stream = cupy.cuda.stream.Stream()
23 stream.use()
24 x_pinned_cpu = x_gpu.get()
25
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/examples/stream/cupy_memcpy.py b/examples/stream/cupy_memcpy.py
--- a/examples/stream/cupy_memcpy.py
+++ b/examples/stream/cupy_memcpy.py
@@ -13,12 +13,38 @@
     return ret
 
 
-x_cpu = numpy.array([1, 2, 3], dtype=numpy.float32)
-x_pinned_cpu = _pin_memory(x_cpu)
-x_gpu = cupy.core.ndarray((3,), dtype=numpy.float32)
-with cupy.cuda.stream.Stream():
-    x_gpu.set(x_pinned_cpu)
-
-stream = cupy.cuda.stream.Stream()
-stream.use()
-x_pinned_cpu = x_gpu.get()
+SIZE = 1024 * 1024
+x_cpu_src = numpy.arange(SIZE, dtype=numpy.float32)
+x_gpu_src = cupy.arange(SIZE, dtype=numpy.float32)
+
+
+# synchronous
+stream = cupy.cuda.Stream.null
+start = stream.record()
+x_gpu_dst = cupy.empty(x_cpu_src.shape, x_cpu_src.dtype)
+x_gpu_dst.set(x_cpu_src)
+x_cpu_dst = x_gpu_src.get()
+end = stream.record()
+
+print('Synchronous Device to Host / Host to Device (ms)')
+print(cupy.cuda.get_elapsed_time(start, end))
+
+
+# asynchronous
+x_gpu_dst = cupy.empty(x_cpu_src.shape, x_cpu_src.dtype)
+x_cpu_dst = numpy.empty(x_gpu_src.shape, x_gpu_src.dtype)
+
+x_pinned_cpu_src = _pin_memory(x_cpu_src)
+x_pinned_cpu_dst = _pin_memory(x_cpu_dst)
+
+with cupy.cuda.stream.Stream() as stream_htod:
+    start = stream_htod.record()
+    x_gpu_dst.set(x_pinned_cpu_src)
+    with cupy.cuda.stream.Stream() as stream_dtoh:
+        x_gpu_src.get(out=x_pinned_cpu_dst)
+        stream_dtoh.synchronize()
+    stream_htod.synchronize()
+    end = stream_htod.record()
+
+print('Asynchronous Device to Host / Host to Device (ms)')
+print(cupy.cuda.get_elapsed_time(start, end))

{"golden_diff": "diff --git a/examples/stream/cupy_memcpy.py b/examples/stream/cupy_memcpy.py\n--- a/examples/stream/cupy_memcpy.py\n+++ b/examples/stream/cupy_memcpy.py\n@@ -13,12 +13,38 @@\n     return ret\n \n \n-x_cpu = numpy.array([1, 2, 3], dtype=numpy.float32)\n-x_pinned_cpu = _pin_memory(x_cpu)\n-x_gpu = cupy.core.ndarray((3,), dtype=numpy.float32)\n-with cupy.cuda.stream.Stream():\n-    x_gpu.set(x_pinned_cpu)\n-\n-stream = cupy.cuda.stream.Stream()\n-stream.use()\n-x_pinned_cpu = x_gpu.get()\n+SIZE = 1024 * 1024\n+x_cpu_src = numpy.arange(SIZE, dtype=numpy.float32)\n+x_gpu_src = cupy.arange(SIZE, dtype=numpy.float32)\n+\n+\n+# synchronous\n+stream = cupy.cuda.Stream.null\n+start = stream.record()\n+x_gpu_dst = cupy.empty(x_cpu_src.shape, x_cpu_src.dtype)\n+x_gpu_dst.set(x_cpu_src)\n+x_cpu_dst = x_gpu_src.get()\n+end = stream.record()\n+\n+print('Synchronous Device to Host / Host to Device (ms)')\n+print(cupy.cuda.get_elapsed_time(start, end))\n+\n+\n+# asynchronous\n+x_gpu_dst = cupy.empty(x_cpu_src.shape, x_cpu_src.dtype)\n+x_cpu_dst = numpy.empty(x_gpu_src.shape, x_gpu_src.dtype)\n+\n+x_pinned_cpu_src = _pin_memory(x_cpu_src)\n+x_pinned_cpu_dst = _pin_memory(x_cpu_dst)\n+\n+with cupy.cuda.stream.Stream() as stream_htod:\n+    start = stream_htod.record()\n+    x_gpu_dst.set(x_pinned_cpu_src)\n+    with cupy.cuda.stream.Stream() as stream_dtoh:\n+        x_gpu_src.get(out=x_pinned_cpu_dst)\n+        stream_dtoh.synchronize()\n+    stream_htod.synchronize()\n+    end = stream_htod.record()\n+\n+print('Asynchronous Device to Host / Host to Device (ms)')\n+print(cupy.cuda.get_elapsed_time(start, end))\n", "issue": "Device-to-host copy in `examples/stream/cupy_memcpy.py` is not asynchronous\nI've interested in asynchronous memcpy for better performance. I checked the `cupy_memcpy.py` sample, and noticed that the last line `x_pinned_cpu = x_gpu.get()` [1] shouldn't work asynchronously. Unfortunately I don't know how to properly fix it.\r\n\r\nOne issue is trivial: this line re-binds the variable `x_pinned_cpu`, instead of updating the value bound to this variable.\r\n\r\nBut there is another more tricky problem. The function `cupy.ndarray.get` creates the new `numpy.ndarray` instance by calling `numpy.empty`, and passes the new host pointer to the `copy_to_host_async` function [2]. IIUC, as the new array not allocated in pinned memory, the copy couldn't be asynchronous with other computations at the GPU.\r\n\r\n* [1] https://github.com/cupy/cupy/blob/v5.0.0rc1/examples/stream/cupy_memcpy.py#L24\r\n* [2] https://github.com/cupy/cupy/blob/v5.0.0rc1/cupy/core/core.pyx#L1805\n", "before_files": [{"content": "# nvprof --print-gpu-trace python examples/stream/cupy_memcpy.py\nimport cupy\nimport numpy\n\npinned_memory_pool = cupy.cuda.PinnedMemoryPool()\ncupy.cuda.set_pinned_memory_allocator(pinned_memory_pool.malloc)\n\n\ndef _pin_memory(array):\n    mem = cupy.cuda.alloc_pinned_memory(array.nbytes)\n    ret = numpy.frombuffer(mem, array.dtype, array.size).reshape(array.shape)\n    ret[...] = array\n    return ret\n\n\nx_cpu = numpy.array([1, 2, 3], dtype=numpy.float32)\nx_pinned_cpu = _pin_memory(x_cpu)\nx_gpu = cupy.core.ndarray((3,), dtype=numpy.float32)\nwith cupy.cuda.stream.Stream():\n    x_gpu.set(x_pinned_cpu)\n\nstream = cupy.cuda.stream.Stream()\nstream.use()\nx_pinned_cpu = x_gpu.get()\n", "path": "examples/stream/cupy_memcpy.py"}], "after_files": [{"content": "# nvprof --print-gpu-trace python examples/stream/cupy_memcpy.py\nimport cupy\nimport numpy\n\npinned_memory_pool = cupy.cuda.PinnedMemoryPool()\ncupy.cuda.set_pinned_memory_allocator(pinned_memory_pool.malloc)\n\n\ndef _pin_memory(array):\n    mem = cupy.cuda.alloc_pinned_memory(array.nbytes)\n    ret = numpy.frombuffer(mem, array.dtype, array.size).reshape(array.shape)\n    ret[...] = array\n    return ret\n\n\nSIZE = 1024 * 1024\nx_cpu_src = numpy.arange(SIZE, dtype=numpy.float32)\nx_gpu_src = cupy.arange(SIZE, dtype=numpy.float32)\n\n\n# synchronous\nstream = cupy.cuda.Stream.null\nstart = stream.record()\nx_gpu_dst = cupy.empty(x_cpu_src.shape, x_cpu_src.dtype)\nx_gpu_dst.set(x_cpu_src)\nx_cpu_dst = x_gpu_src.get()\nend = stream.record()\n\nprint('Synchronous Device to Host / Host to Device (ms)')\nprint(cupy.cuda.get_elapsed_time(start, end))\n\n\n# asynchronous\nx_gpu_dst = cupy.empty(x_cpu_src.shape, x_cpu_src.dtype)\nx_cpu_dst = numpy.empty(x_gpu_src.shape, x_gpu_src.dtype)\n\nx_pinned_cpu_src = _pin_memory(x_cpu_src)\nx_pinned_cpu_dst = _pin_memory(x_cpu_dst)\n\nwith cupy.cuda.stream.Stream() as stream_htod:\n    start = stream_htod.record()\n    x_gpu_dst.set(x_pinned_cpu_src)\n    with cupy.cuda.stream.Stream() as stream_dtoh:\n        x_gpu_src.get(out=x_pinned_cpu_dst)\n        stream_dtoh.synchronize()\n    stream_htod.synchronize()\n    end = stream_htod.record()\n\nprint('Asynchronous Device to Host / Host to Device (ms)')\nprint(cupy.cuda.get_elapsed_time(start, end))\n", "path": "examples/stream/cupy_memcpy.py"}]}