prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  A confusing error occurs when GPU and CPU arrays are mixed on `numerical_grad`
  In this case, unreadable error occurs. We nee to check if arrays are not mixed.
  
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `chainer/gradient_check.py`
  Content:
  ```
  1 import numpy
  2 import six
  3 
  4 from chainer import cuda
  5 
  6 
  7 def numerical_grad_cpu(f, inputs, grad_outputs, eps=1e-3):
  8     grads = tuple(numpy.zeros_like(x) for x in inputs)
  9     for x, gx in zip(inputs, grads):
  10         flat_x = x.ravel()
  11         flat_gx = gx.ravel()
  12         for i in six.moves.range(flat_x.size):
  13             orig = flat_x[i]
  14             flat_x[i] = orig + eps
  15             ys1 = f()
  16             flat_x[i] = orig - eps
  17             ys2 = f()
  18             flat_x[i] = orig
  19 
  20             for y1, y2, gy in zip(ys1, ys2, grad_outputs):
  21                 if gy is not None:
  22                     dot = float(sum(((y1 - y2) * gy).ravel()))
  23                     flat_gx[i] += dot / (2 * eps)
  24 
  25     return grads
  26 
  27 
  28 def numerical_grad_gpu(f, inputs, grad_outputs, eps=1e-3):
  29     grads = tuple(cuda.zeros_like(x) for x in inputs)
  30     for x, gx in zip(inputs, grads):
  31         x = x.ravel()
  32         gx = gx.ravel()
  33         x_cpu = x.get()
  34         gx_cpu = gx.get()
  35         for i in six.moves.range(x_cpu.size):
  36             orig = x_cpu[i]
  37             x_cpu[i] = orig + eps
  38             x.set(x_cpu)
  39             ys1 = f()
  40             x_cpu[i] = orig - eps
  41             x.set(x_cpu)
  42             ys2 = f()
  43             x_cpu[i] = orig
  44             x.set(x_cpu)
  45 
  46             for y1, y2, gy in zip(ys1, ys2, grad_outputs):
  47                 if gy is not None:
  48                     dot = sum(((y1 - y2) * gy).ravel()).get()
  49                     gx_cpu[i] += dot / (2 * eps)
  50         gx.set(gx_cpu)
  51 
  52     return grads
  53 
  54 
  55 def numerical_grad(f, inputs, grad_outputs, eps=1e-3):
  56     """Computes numerical gradient by finite differences.
  57 
  58     This function is used to implement gradient check. For usage example, see
  59     unit tests of :mod:`chainer.functions`.
  60 
  61     Args:
  62         f (function): Python function with no arguments that runs forward
  63             computation and returns the result.
  64         inputs (tuple of arrays): Tuple of arrays that should be treated as
  65             inputs. Each element of them is slightly modified to realize
  66             numerical gradient by finite differences.
  67         grad_outputs (tuple of arrays): Tuple of arrays that are treated as
  68             output gradients.
  69         eps (float): Epsilon value of finite differences.
  70 
  71     Returns:
  72         tuple: Numerical gradient arrays corresponding to ``inputs``.
  73 
  74     """
  75     assert eps > 0
  76     if any(isinstance(x, cuda.GPUArray) for x in inputs):
  77         return numerical_grad_gpu(f, inputs, grad_outputs, eps)
  78     return numerical_grad_cpu(f, inputs, grad_outputs, eps)
  79 
  80 
  81 def assert_allclose(x, y, atol=1e-5, rtol=1e-4, verbose=True):
  82     """Asserts if some corresponding element of x and y differs too much.
  83 
  84     This function can handle both CPU and GPU arrays simultaneously.
  85 
  86     Args:
  87         x: Left-hand-side array.
  88         y: Right-hand-side array.
  89         atol (float): Absolute tolerance.
  90         rtol (float): Relative tolerance.
  91         verbose (bool): If True, it outputs verbose messages on error.
  92 
  93     """
  94     x = cuda.to_cpu(x)
  95     y = cuda.to_cpu(y)
  96     try:
  97         numpy.testing.assert_allclose(
  98             x, y, atol=atol, rtol=rtol, verbose=verbose)
  99     except Exception:
  100         print('error:', numpy.abs(x - y).max())
  101         raise
  102
  ```
  --- 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()
  
  ```
  
golden_diff: diff --git a/chainer/gradient_check.py b/chainer/gradient_check.py
  --- a/chainer/gradient_check.py
  +++ b/chainer/gradient_check.py
  @@ -73,9 +73,18 @@
   
       """
       assert eps > 0
  -    if any(isinstance(x, cuda.GPUArray) for x in inputs):
  +    inputs = tuple(inputs)
  +    grad_outputs = tuple(grad_outputs)
  +    gpu = any(isinstance(x, cuda.GPUArray) for x in inputs + grad_outputs)
  +
  +    cpu = any(isinstance(x, numpy.ndarray) for x in inputs + grad_outputs)
  +
  +    if gpu and cpu:
  +        raise RuntimeError('Do not mix GPU and CPU arrays in `numerical_grad`')
  +    elif gpu:
           return numerical_grad_gpu(f, inputs, grad_outputs, eps)
  -    return numerical_grad_cpu(f, inputs, grad_outputs, eps)
  +    else:
  +        return numerical_grad_cpu(f, inputs, grad_outputs, eps)
   
   
   def assert_allclose(x, y, atol=1e-5, rtol=1e-4, verbose=True):
  
verification_info: {"golden_diff": "diff --git a/chainer/gradient_check.py b/chainer/gradient_check.py\n--- a/chainer/gradient_check.py\n+++ b/chainer/gradient_check.py\n@@ -73,9 +73,18 @@\n \n     \"\"\"\n     assert eps > 0\n-    if any(isinstance(x, cuda.GPUArray) for x in inputs):\n+    inputs = tuple(inputs)\n+    grad_outputs = tuple(grad_outputs)\n+    gpu = any(isinstance(x, cuda.GPUArray) for x in inputs + grad_outputs)\n+\n+    cpu = any(isinstance(x, numpy.ndarray) for x in inputs + grad_outputs)\n+\n+    if gpu and cpu:\n+        raise RuntimeError('Do not mix GPU and CPU arrays in `numerical_grad`')\n+    elif gpu:\n         return numerical_grad_gpu(f, inputs, grad_outputs, eps)\n-    return numerical_grad_cpu(f, inputs, grad_outputs, eps)\n+    else:\n+        return numerical_grad_cpu(f, inputs, grad_outputs, eps)\n \n \n def assert_allclose(x, y, atol=1e-5, rtol=1e-4, verbose=True):\n", "issue": "A confusing error occurs when GPU and CPU arrays are mixed on `numerical_grad`\nIn this case, unreadable error occurs. We nee to check if arrays are not mixed.\n\n", "before_files": [{"content": "import numpy\nimport six\n\nfrom chainer import cuda\n\n\ndef numerical_grad_cpu(f, inputs, grad_outputs, eps=1e-3):\n    grads = tuple(numpy.zeros_like(x) for x in inputs)\n    for x, gx in zip(inputs, grads):\n        flat_x = x.ravel()\n        flat_gx = gx.ravel()\n        for i in six.moves.range(flat_x.size):\n            orig = flat_x[i]\n            flat_x[i] = orig + eps\n            ys1 = f()\n            flat_x[i] = orig - eps\n            ys2 = f()\n            flat_x[i] = orig\n\n            for y1, y2, gy in zip(ys1, ys2, grad_outputs):\n                if gy is not None:\n                    dot = float(sum(((y1 - y2) * gy).ravel()))\n                    flat_gx[i] += dot / (2 * eps)\n\n    return grads\n\n\ndef numerical_grad_gpu(f, inputs, grad_outputs, eps=1e-3):\n    grads = tuple(cuda.zeros_like(x) for x in inputs)\n    for x, gx in zip(inputs, grads):\n        x = x.ravel()\n        gx = gx.ravel()\n        x_cpu = x.get()\n        gx_cpu = gx.get()\n        for i in six.moves.range(x_cpu.size):\n            orig = x_cpu[i]\n            x_cpu[i] = orig + eps\n            x.set(x_cpu)\n            ys1 = f()\n            x_cpu[i] = orig - eps\n            x.set(x_cpu)\n            ys2 = f()\n            x_cpu[i] = orig\n            x.set(x_cpu)\n\n            for y1, y2, gy in zip(ys1, ys2, grad_outputs):\n                if gy is not None:\n                    dot = sum(((y1 - y2) * gy).ravel()).get()\n                    gx_cpu[i] += dot / (2 * eps)\n        gx.set(gx_cpu)\n\n    return grads\n\n\ndef numerical_grad(f, inputs, grad_outputs, eps=1e-3):\n    \"\"\"Computes numerical gradient by finite differences.\n\n    This function is used to implement gradient check. For usage example, see\n    unit tests of :mod:`chainer.functions`.\n\n    Args:\n        f (function): Python function with no arguments that runs forward\n            computation and returns the result.\n        inputs (tuple of arrays): Tuple of arrays that should be treated as\n            inputs. Each element of them is slightly modified to realize\n            numerical gradient by finite differences.\n        grad_outputs (tuple of arrays): Tuple of arrays that are treated as\n            output gradients.\n        eps (float): Epsilon value of finite differences.\n\n    Returns:\n        tuple: Numerical gradient arrays corresponding to ``inputs``.\n\n    \"\"\"\n    assert eps > 0\n    if any(isinstance(x, cuda.GPUArray) for x in inputs):\n        return numerical_grad_gpu(f, inputs, grad_outputs, eps)\n    return numerical_grad_cpu(f, inputs, grad_outputs, eps)\n\n\ndef assert_allclose(x, y, atol=1e-5, rtol=1e-4, verbose=True):\n    \"\"\"Asserts if some corresponding element of x and y differs too much.\n\n    This function can handle both CPU and GPU arrays simultaneously.\n\n    Args:\n        x: Left-hand-side array.\n        y: Right-hand-side array.\n        atol (float): Absolute tolerance.\n        rtol (float): Relative tolerance.\n        verbose (bool): If True, it outputs verbose messages on error.\n\n    \"\"\"\n    x = cuda.to_cpu(x)\n    y = cuda.to_cpu(y)\n    try:\n        numpy.testing.assert_allclose(\n            x, y, atol=atol, rtol=rtol, verbose=verbose)\n    except Exception:\n        print('error:', numpy.abs(x - y).max())\n        raise\n", "path": "chainer/gradient_check.py"}], "after_files": [{"content": "import numpy\nimport six\n\nfrom chainer import cuda\n\n\ndef numerical_grad_cpu(f, inputs, grad_outputs, eps=1e-3):\n    grads = tuple(numpy.zeros_like(x) for x in inputs)\n    for x, gx in zip(inputs, grads):\n        flat_x = x.ravel()\n        flat_gx = gx.ravel()\n        for i in six.moves.range(flat_x.size):\n            orig = flat_x[i]\n            flat_x[i] = orig + eps\n            ys1 = f()\n            flat_x[i] = orig - eps\n            ys2 = f()\n            flat_x[i] = orig\n\n            for y1, y2, gy in zip(ys1, ys2, grad_outputs):\n                if gy is not None:\n                    dot = float(sum(((y1 - y2) * gy).ravel()))\n                    flat_gx[i] += dot / (2 * eps)\n\n    return grads\n\n\ndef numerical_grad_gpu(f, inputs, grad_outputs, eps=1e-3):\n    grads = tuple(cuda.zeros_like(x) for x in inputs)\n    for x, gx in zip(inputs, grads):\n        x = x.ravel()\n        gx = gx.ravel()\n        x_cpu = x.get()\n        gx_cpu = gx.get()\n        for i in six.moves.range(x_cpu.size):\n            orig = x_cpu[i]\n            x_cpu[i] = orig + eps\n            x.set(x_cpu)\n            ys1 = f()\n            x_cpu[i] = orig - eps\n            x.set(x_cpu)\n            ys2 = f()\n            x_cpu[i] = orig\n            x.set(x_cpu)\n\n            for y1, y2, gy in zip(ys1, ys2, grad_outputs):\n                if gy is not None:\n                    dot = sum(((y1 - y2) * gy).ravel()).get()\n                    gx_cpu[i] += dot / (2 * eps)\n        gx.set(gx_cpu)\n\n    return grads\n\n\ndef numerical_grad(f, inputs, grad_outputs, eps=1e-3):\n    \"\"\"Computes numerical gradient by finite differences.\n\n    This function is used to implement gradient check. For usage example, see\n    unit tests of :mod:`chainer.functions`.\n\n    Args:\n        f (function): Python function with no arguments that runs forward\n            computation and returns the result.\n        inputs (tuple of arrays): Tuple of arrays that should be treated as\n            inputs. Each element of them is slightly modified to realize\n            numerical gradient by finite differences.\n        grad_outputs (tuple of arrays): Tuple of arrays that are treated as\n            output gradients.\n        eps (float): Epsilon value of finite differences.\n\n    Returns:\n        tuple: Numerical gradient arrays corresponding to ``inputs``.\n\n    \"\"\"\n    assert eps > 0\n    inputs = tuple(inputs)\n    grad_outputs = tuple(grad_outputs)\n    gpu = any(isinstance(x, cuda.GPUArray) for x in inputs + grad_outputs)\n\n    cpu = any(isinstance(x, numpy.ndarray) for x in inputs + grad_outputs)\n\n    if gpu and cpu:\n        raise RuntimeError('Do not mix GPU and CPU arrays in `numerical_grad`')\n    elif gpu:\n        return numerical_grad_gpu(f, inputs, grad_outputs, eps)\n    else:\n        return numerical_grad_cpu(f, inputs, grad_outputs, eps)\n\n\ndef assert_allclose(x, y, atol=1e-5, rtol=1e-4, verbose=True):\n    \"\"\"Asserts if some corresponding element of x and y differs too much.\n\n    This function can handle both CPU and GPU arrays simultaneously.\n\n    Args:\n        x: Left-hand-side array.\n        y: Right-hand-side array.\n        atol (float): Absolute tolerance.\n        rtol (float): Relative tolerance.\n        verbose (bool): If True, it outputs verbose messages on error.\n\n    \"\"\"\n    x = cuda.to_cpu(x)\n    y = cuda.to_cpu(y)\n    try:\n        numpy.testing.assert_allclose(\n            x, y, atol=atol, rtol=rtol, verbose=verbose)\n    except Exception:\n        print('error:', numpy.abs(x - y).max())\n        raise\n", "path": "chainer/gradient_check.py"}]}