We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
`concat` with the last axis fails on py3
Same problem in `concat` as #253 

@ShigekiKarita reported this problem too. Thanks!
https://gist.github.com/ShigekiKarita/4293f886765a1ed4a144


--- 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/functions/concat.py`
Content:
```
1 import numpy
2 
3 from chainer import cuda
4 from chainer import function
5 from chainer.utils import type_check
6 
7 _args = 'const float* x, float* y, int cdimx, int cdimy, int rdim, int coffset'
8 _preamble = '''
9 #define COPY(statement) \
10     int l   = i / (rdim * cdimx);  \
11     int c   = i / rdim % cdimx + coffset;  \
12     int r   = i % rdim;  \
13     int idx = r + rdim * (c + cdimy * l);  \
14     statement;
15 '''
16 
17 
18 class Concat(function.Function):
19 
20     """Concatenate multiple tensors towards specified axis."""
21 
22     # concat along the channel dimension by default
23     def __init__(self, axis=1):
24         self.axis = axis
25 
26     def check_type_forward(self, in_types):
27         type_check.expect(in_types.size() > 0)
28         type_check.expect(in_types[0].ndim >
29                           type_check.Variable(self.axis, 'axis'))
30 
31         ndim = in_types[0].ndim.eval()
32         for i in range(1, in_types.size().eval()):
33             type_check.expect(
34                 in_types[0].dtype == in_types[i].dtype,
35                 in_types[0].ndim == in_types[i].ndim,
36             )
37             for d in range(0, ndim):
38                 if d == self.axis:
39                     continue
40                 type_check.expect(in_types[0].shape[d] == in_types[i].shape[d])
41 
42     def check_type_backward(self, in_types, out_types):
43         type_check.expect(
44             in_types.size() > 0,
45             out_types.size() == 1,
46         )
47         y_type, = out_types
48 
49         type_check.expect(y_type.dtype == in_types[0].dtype)
50         ndim = in_types[0].ndim.eval()
51         concat_size = sum(typ.shape[self.axis] for typ in in_types)
52         type_check.expect(concat_size == y_type.shape[self.axis])
53 
54         for d in range(0, ndim):
55             if d == self.axis:
56                 continue
57             type_check.expect(y_type.shape[d] == in_types[0].shape[d])
58 
59     def forward_cpu(self, xs):
60         return numpy.concatenate(xs, axis=self.axis),
61 
62     def forward_gpu(self, xs):
63         # TODO(beam2d): Unify the process into a single kernel.
64         shape = list(xs[0].shape)
65         for x in xs[1:]:
66             shape[self.axis] += x.shape[self.axis]
67         self.shape = shape
68 
69         y = cuda.empty(shape, dtype=xs[0].dtype)
70         self.cdimy = y.shape[self.axis]
71         self.rdim = numpy.prod(shape[self.axis + 1:])
72 
73         coffset = 0
74         kernel = cuda.elementwise(
75             _args, 'COPY(y[idx] = x[i])', 'concat_fwd', preamble=_preamble)
76         for x in xs:
77             cdimx = x.shape[self.axis]
78             kernel(x, y, cdimx, self.cdimy, self.rdim, coffset)
79             coffset += cdimx
80 
81         return y,
82 
83     def backward_cpu(self, xs, gy):
84         sizes = numpy.array([x.shape[self.axis] for x in xs[:-1]]).cumsum()
85         return numpy.split(gy[0], sizes, axis=self.axis)
86 
87     def backward_gpu(self, xs, gy):
88         gxs = tuple(cuda.empty_like(x) for x in xs)
89 
90         coffset = 0
91         kernel = cuda.elementwise(
92             _args, 'COPY(x[i] = y[idx])', 'concat_bwd', preamble=_preamble)
93         for gx in gxs:
94             cdimx = gx.shape[self.axis]
95             kernel(gx, gy[0], cdimx, self.cdimy, self.rdim, coffset)
96             coffset += cdimx
97 
98         return gxs
99 
100 
101 def concat(xs, axis=1):
102     """Concatenates given variables along an axis.
103 
104     Args:
105         xs (tuple of Variables): Variables to be concatenated.
106         axis (int): Axis that the input arrays are concatenated along.
107 
108     Returns:
109         ~chainer.Variable: Output variable.
110 
111     """
112     return Concat(axis=axis)(*xs)
113
```
--- 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/chainer/functions/concat.py b/chainer/functions/concat.py
--- a/chainer/functions/concat.py
+++ b/chainer/functions/concat.py
@@ -68,7 +68,7 @@
 
         y = cuda.empty(shape, dtype=xs[0].dtype)
         self.cdimy = y.shape[self.axis]
-        self.rdim = numpy.prod(shape[self.axis + 1:])
+        self.rdim = numpy.prod(shape[self.axis + 1:], dtype=int)
 
         coffset = 0
         kernel = cuda.elementwise(

{"golden_diff": "diff --git a/chainer/functions/concat.py b/chainer/functions/concat.py\n--- a/chainer/functions/concat.py\n+++ b/chainer/functions/concat.py\n@@ -68,7 +68,7 @@\n \n         y = cuda.empty(shape, dtype=xs[0].dtype)\n         self.cdimy = y.shape[self.axis]\n-        self.rdim = numpy.prod(shape[self.axis + 1:])\n+        self.rdim = numpy.prod(shape[self.axis + 1:], dtype=int)\n \n         coffset = 0\n         kernel = cuda.elementwise(\n", "issue": "`concat` with the last axis fails on py3\nSame problem in `concat` as #253 \n\n@ShigekiKarita reported this problem too. Thanks!\nhttps://gist.github.com/ShigekiKarita/4293f886765a1ed4a144\n\n", "before_files": [{"content": "import numpy\n\nfrom chainer import cuda\nfrom chainer import function\nfrom chainer.utils import type_check\n\n_args = 'const float* x, float* y, int cdimx, int cdimy, int rdim, int coffset'\n_preamble = '''\n#define COPY(statement) \\\n    int l   = i / (rdim * cdimx);  \\\n    int c   = i / rdim % cdimx + coffset;  \\\n    int r   = i % rdim;  \\\n    int idx = r + rdim * (c + cdimy * l);  \\\n    statement;\n'''\n\n\nclass Concat(function.Function):\n\n    \"\"\"Concatenate multiple tensors towards specified axis.\"\"\"\n\n    # concat along the channel dimension by default\n    def __init__(self, axis=1):\n        self.axis = axis\n\n    def check_type_forward(self, in_types):\n        type_check.expect(in_types.size() > 0)\n        type_check.expect(in_types[0].ndim >\n                          type_check.Variable(self.axis, 'axis'))\n\n        ndim = in_types[0].ndim.eval()\n        for i in range(1, in_types.size().eval()):\n            type_check.expect(\n                in_types[0].dtype == in_types[i].dtype,\n                in_types[0].ndim == in_types[i].ndim,\n            )\n            for d in range(0, ndim):\n                if d == self.axis:\n                    continue\n                type_check.expect(in_types[0].shape[d] == in_types[i].shape[d])\n\n    def check_type_backward(self, in_types, out_types):\n        type_check.expect(\n            in_types.size() > 0,\n            out_types.size() == 1,\n        )\n        y_type, = out_types\n\n        type_check.expect(y_type.dtype == in_types[0].dtype)\n        ndim = in_types[0].ndim.eval()\n        concat_size = sum(typ.shape[self.axis] for typ in in_types)\n        type_check.expect(concat_size == y_type.shape[self.axis])\n\n        for d in range(0, ndim):\n            if d == self.axis:\n                continue\n            type_check.expect(y_type.shape[d] == in_types[0].shape[d])\n\n    def forward_cpu(self, xs):\n        return numpy.concatenate(xs, axis=self.axis),\n\n    def forward_gpu(self, xs):\n        # TODO(beam2d): Unify the process into a single kernel.\n        shape = list(xs[0].shape)\n        for x in xs[1:]:\n            shape[self.axis] += x.shape[self.axis]\n        self.shape = shape\n\n        y = cuda.empty(shape, dtype=xs[0].dtype)\n        self.cdimy = y.shape[self.axis]\n        self.rdim = numpy.prod(shape[self.axis + 1:])\n\n        coffset = 0\n        kernel = cuda.elementwise(\n            _args, 'COPY(y[idx] = x[i])', 'concat_fwd', preamble=_preamble)\n        for x in xs:\n            cdimx = x.shape[self.axis]\n            kernel(x, y, cdimx, self.cdimy, self.rdim, coffset)\n            coffset += cdimx\n\n        return y,\n\n    def backward_cpu(self, xs, gy):\n        sizes = numpy.array([x.shape[self.axis] for x in xs[:-1]]).cumsum()\n        return numpy.split(gy[0], sizes, axis=self.axis)\n\n    def backward_gpu(self, xs, gy):\n        gxs = tuple(cuda.empty_like(x) for x in xs)\n\n        coffset = 0\n        kernel = cuda.elementwise(\n            _args, 'COPY(x[i] = y[idx])', 'concat_bwd', preamble=_preamble)\n        for gx in gxs:\n            cdimx = gx.shape[self.axis]\n            kernel(gx, gy[0], cdimx, self.cdimy, self.rdim, coffset)\n            coffset += cdimx\n\n        return gxs\n\n\ndef concat(xs, axis=1):\n    \"\"\"Concatenates given variables along an axis.\n\n    Args:\n        xs (tuple of Variables): Variables to be concatenated.\n        axis (int): Axis that the input arrays are concatenated along.\n\n    Returns:\n        ~chainer.Variable: Output variable.\n\n    \"\"\"\n    return Concat(axis=axis)(*xs)\n", "path": "chainer/functions/concat.py"}], "after_files": [{"content": "import numpy\n\nfrom chainer import cuda\nfrom chainer import function\nfrom chainer.utils import type_check\n\n_args = 'const float* x, float* y, int cdimx, int cdimy, int rdim, int coffset'\n_preamble = '''\n#define COPY(statement) \\\n    int l   = i / (rdim * cdimx);  \\\n    int c   = i / rdim % cdimx + coffset;  \\\n    int r   = i % rdim;  \\\n    int idx = r + rdim * (c + cdimy * l);  \\\n    statement;\n'''\n\n\nclass Concat(function.Function):\n\n    \"\"\"Concatenate multiple tensors towards specified axis.\"\"\"\n\n    # concat along the channel dimension by default\n    def __init__(self, axis=1):\n        self.axis = axis\n\n    def check_type_forward(self, in_types):\n        type_check.expect(in_types.size() > 0)\n        type_check.expect(in_types[0].ndim >\n                          type_check.Variable(self.axis, 'axis'))\n\n        ndim = in_types[0].ndim.eval()\n        for i in range(1, in_types.size().eval()):\n            type_check.expect(\n                in_types[0].dtype == in_types[i].dtype,\n                in_types[0].ndim == in_types[i].ndim,\n            )\n            for d in range(0, ndim):\n                if d == self.axis:\n                    continue\n                type_check.expect(in_types[0].shape[d] == in_types[i].shape[d])\n\n    def check_type_backward(self, in_types, out_types):\n        type_check.expect(\n            in_types.size() > 0,\n            out_types.size() == 1,\n        )\n        y_type, = out_types\n\n        type_check.expect(y_type.dtype == in_types[0].dtype)\n        ndim = in_types[0].ndim.eval()\n        concat_size = sum(typ.shape[self.axis] for typ in in_types)\n        type_check.expect(concat_size == y_type.shape[self.axis])\n\n        for d in range(0, ndim):\n            if d == self.axis:\n                continue\n            type_check.expect(y_type.shape[d] == in_types[0].shape[d])\n\n    def forward_cpu(self, xs):\n        return numpy.concatenate(xs, axis=self.axis),\n\n    def forward_gpu(self, xs):\n        # TODO(beam2d): Unify the process into a single kernel.\n        shape = list(xs[0].shape)\n        for x in xs[1:]:\n            shape[self.axis] += x.shape[self.axis]\n        self.shape = shape\n\n        y = cuda.empty(shape, dtype=xs[0].dtype)\n        self.cdimy = y.shape[self.axis]\n        self.rdim = numpy.prod(shape[self.axis + 1:], dtype=int)\n\n        coffset = 0\n        kernel = cuda.elementwise(\n            _args, 'COPY(y[idx] = x[i])', 'concat_fwd', preamble=_preamble)\n        for x in xs:\n            cdimx = x.shape[self.axis]\n            kernel(x, y, cdimx, self.cdimy, self.rdim, coffset)\n            coffset += cdimx\n\n        return y,\n\n    def backward_cpu(self, xs, gy):\n        sizes = numpy.array([x.shape[self.axis] for x in xs[:-1]]).cumsum()\n        return numpy.split(gy[0], sizes, axis=self.axis)\n\n    def backward_gpu(self, xs, gy):\n        gxs = tuple(cuda.empty_like(x) for x in xs)\n\n        coffset = 0\n        kernel = cuda.elementwise(\n            _args, 'COPY(x[i] = y[idx])', 'concat_bwd', preamble=_preamble)\n        for gx in gxs:\n            cdimx = gx.shape[self.axis]\n            kernel(gx, gy[0], cdimx, self.cdimy, self.rdim, coffset)\n            coffset += cdimx\n\n        return gxs\n\n\ndef concat(xs, axis=1):\n    \"\"\"Concatenates given variables along an axis.\n\n    Args:\n        xs (tuple of Variables): Variables to be concatenated.\n        axis (int): Axis that the input arrays are concatenated along.\n\n    Returns:\n        ~chainer.Variable: Output variable.\n\n    \"\"\"\n    return Concat(axis=axis)(*xs)\n", "path": "chainer/functions/concat.py"}]}