Apply more feedback fixes.

cpp/src/arrow/gpu/cuda_context.h

@@ -121,12 +121,12 @@ class ARROW_EXPORT CudaContext : public std::enable_shared_from_this<CudaContext
 
   /// \brief Return the device address that is reachable from kernels
   /// running in the context
-  /// \param[in] addr memory address value
+  /// \param[in] addr device or host memory address
   /// \param[out] devaddr the device address
   /// \return Status
   ///
   /// The device address is defined as a memory address accessible by
-  /// device. While it is often a device memory address but it can be
+  /// device. While it is often a device memory address, it can be
   /// also a host memory address, for instance, when the memory is
   /// allocated as host memory (using cudaMallocHost or cudaHostAlloc)
   /// or as managed memory (using cudaMallocManaged) or the host

python/pyarrow/_cuda.pyx

@@ -19,7 +19,7 @@
 from pyarrow.compat import tobytes
 from pyarrow.lib cimport *
 from pyarrow.includes.libarrow_cuda cimport *
-from pyarrow.lib import py_buffer, allocate_buffer, as_buffer
+from pyarrow.lib import py_buffer, allocate_buffer, as_buffer, ArrowTypeError
 from pyarrow.util import get_contiguous_span
 cimport cpython as cp
 
@@ -324,12 +324,8 @@ cdef class Context:
                 desc['shape'], desc.get('strides'),
                 np.dtype(desc['typestr']).itemsize)
             return self.foreign_buffer(addr + start, end - start)
-        from numba.cuda.cudadrv.driver import MemoryPointer
-        if isinstance(obj, MemoryPointer):
-            addr = obj.device_pointer.value
-            return self.foreign_buffer(addr, obj.size)
-        raise NotImplementedError('cannot create device buffer view from'
-                                  ' `%s` object' % (type(obj)))
+        raise ArrowTypeError('cannot create device buffer view from'
+                             ' `%s` object' % (type(obj)))
 
 
 cdef class IpcMemHandle:

python/pyarrow/tests/test_cuda.py

@@ -256,7 +256,7 @@ def test_context_from_object(size):
         ctx.buffer_from_object(pa.py_buffer(b"123"))
 
     # Trying to create a device buffer from numpy.array
-    with pytest.raises(NotImplementedError,
+    with pytest.raises(pa.ArrowTypeError,
                        match=('cannot create device buffer view from'
                               ' `<class \'numpy.ndarray\'>` object')):
         ctx.buffer_from_object(np.array([1, 2, 3]))

python/pyarrow/tests/test_cuda_numba_interop.py

@@ -83,19 +83,8 @@ def test_from_object(c, dtype, size):
     ctx, nb_ctx = context_choices[c]
     arr, cbuf = make_random_buffer(size, target='device', dtype=dtype, ctx=ctx)
 
-    # Creating device buffer from numba MemoryObject:
-    mem = cbuf.to_numba()
-    cbuf2 = ctx.buffer_from_object(mem)
-    assert cbuf2.size == cbuf.size
-    arr2 = np.frombuffer(cbuf2.copy_to_host(), dtype=dtype)
-    np.testing.assert_equal(arr, arr2)
-
     # Creating device buffer from numba DeviceNDArray:
     darr = nb_cuda.to_device(arr)
-    darr = DeviceNDArray(size,
-                         np.dtype(dtype).itemsize,
-                         np.dtype(dtype),
-                         gpu_data=mem)
     cbuf2 = ctx.buffer_from_object(darr)
     assert cbuf2.size == cbuf.size
     arr2 = np.frombuffer(cbuf2.copy_to_host(), dtype=dtype)
@@ -116,23 +105,13 @@ def test_from_object(c, dtype, size):
             arr2 = np.frombuffer(cbuf2.copy_to_host(), dtype=dtype)
             np.testing.assert_equal(arr[s], arr2[s2])
 
-        # slice with negative strides
-        rdarr = darr[::-1]
-        if 1:
-            # workaround numba bug [numba issue 3705]:
-            from numba.cuda.cudadrv.driver import MemoryPointer
-            import ctypes
-            rdarr.shape = arr[::-1].shape
-            addr = (darr.__cuda_array_interface__['data'][0]
-                    + (arr[::-1].__array_interface__['data'][0]
-                       - arr.__array_interface__['data'][0]))
-            mem = MemoryPointer(nb_ctx, pointer=ctypes.c_void_p(addr),
-                                size=size)
-            rdarr.gpu_data = mem
-        cbuf2 = ctx.buffer_from_object(rdarr)
-        assert cbuf2.size == cbuf.size
-        arr2 = np.frombuffer(cbuf2.copy_to_host(), dtype=dtype)
-        np.testing.assert_equal(arr, arr2)
+        # cannot test negative strides due to numba bug, see its issue 3705
+        if 0:
+            rdarr = darr[::-1]
+            cbuf2 = ctx.buffer_from_object(rdarr)
+            assert cbuf2.size == cbuf.size
+            arr2 = np.frombuffer(cbuf2.copy_to_host(), dtype=dtype)
+            np.testing.assert_equal(arr, arr2)
 
     # Creating device buffer from a 2-dimensional numba DeviceNDArray:
     if size >= 8:

python/pyarrow/util.py

@@ -17,9 +17,9 @@
 
 # Miscellaneous utility code
 
+import functools
 import six
 import warnings
-import functools
 
 
 try: