prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <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
  
  </issue>
  <code>
  [start of examples/stream/cupy_memcpy.py]
  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 of examples/stream/cupy_memcpy.py]
  </code>
  
  I need you to solve this issue by generating a single patch file that I can apply directly to this repository using git apply. Please respond with a single patch file in the following format.
  <patch>
  diff --git a/file.py b/file.py
  --- a/file.py
  +++ b/file.py
  @@ -1,27 +1,35 @@
   def euclidean(a, b):
  -    while b:
  -        a, b = b, a % b
  -    return a
  +    if b == 0:
  +        return a
  +    return euclidean(b, a % b)
   
   
   def bresenham(x0, y0, x1, y1):
       points = []
       dx = abs(x1 - x0)
       dy = abs(y1 - y0)
  -    sx = 1 if x0 < x1 else -1
  -    sy = 1 if y0 < y1 else -1
  -    err = dx - dy
  +    x, y = x0, y0
  +    sx = -1 if x0 > x1 else 1
  +    sy = -1 if y0 > y1 else 1
   
  -    while True:
  -        points.append((x0, y0))
  -        if x0 == x1 and y0 == y1:
  -            break
  -        e2 = 2 * err
  -        if e2 > -dy:
  -            err -= dy
  -            x0 += sx
  -        if e2 < dx:
  -            err += dx
  -            y0 += sy
  +    if dx > dy:
  +        err = dx / 2.0
  +        while x != x1:
  +            points.append((x, y))
  +            err -= dy
  +            if err < 0:
  +                y += sy
  +                err += dx
  +            x += sx
  +    else:
  +        err = dy / 2.0
  +        while y != y1:
  +            points.append((x, y))
  +            err -= dx
  +            if err < 0:
  +                x += sx
  +                err += dy
  +            y += sy
  +
  +    points.append((x, y))
       return points
  
  </patch>
golden_diff: 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))
  
verification_info: {"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"}]}