Update. Many of @abergeron comments have been taken account.

theano/gpuarray/__init__.py

@@ -28,7 +28,7 @@ from .type import (GpuArrayType, GpuArrayVariable, GpuArrayConstant,
                    GpuArraySharedVariable, gpuarray_shared_constructor,
                    reg_context, get_context, ContextNotDefined, _get_props)
 from .basic_ops import as_gpuarray_variable
-from . import fft, dnn, opt, nerv, extra_ops, multinomial
+from . import fft, dnn, opt, nerv, extra_ops, multinomial, reduction
 
 def transfer(x, target):
     try:

theano/gpuarray/opt.py

@@ -65,6 +65,7 @@ from .subtensor import (GpuIncSubtensor, GpuSubtensor,
                         GpuAdvancedIncSubtensor1,
                         GpuAdvancedIncSubtensor1_dev20)
 from .opt_util import alpha_merge, output_merge, pad_dims, unpad_dims
+from .reduction import gpu_maxandargmax
 
 _logger = logging.getLogger("theano.gpuarray.opt")
 

theano/gpuarray/GpuMaxAndArgmax.py → theano/gpuarray/reduction.py

 import os
 import numpy
-from six import integer_types
 import theano
-from theano.gof import Variable, Op, Apply
-from theano.tensor.type_other import NoneConst
+from theano.gof import Op, Apply
 from theano.tensor.var import TensorConstant
-# from theano.tensor import as_tensor_variable
+
 from .basic_ops import (infer_context_name, as_gpuarray_variable)
 from .type import GpuArrayType
 
@@ -25,26 +23,13 @@ class GpuMaxAndArgmax(Op):
 
     def make_node(self, X, axis=None):
         context_name = infer_context_name(X)
-        # Check axis and convert it to Python variable.
-        if (isinstance(axis, (integer_types, numpy.integer)) or
-           (isinstance(axis, numpy.ndarray) and axis.ndim == 0)):
-            axis = [int(axis)]
-        elif isinstance(axis, (tuple, list, numpy.ndarray)):
-            axis = [int(a) for a in axis]
-        elif isinstance(axis, Variable):
-            if NoneConst.equals(axis):
-                axis = None
-            elif not isinstance(axis, TensorConstant):
-                raise TypeError("MaxAndArgmax needs a constant axis. Got %s" % axis)
-            else:
-                assert (axis.dtype.startswith("int") or axis.dtype.startswith("uint"))
-                if (isinstance(axis.data, (integer_types, numpy.integer)) or
-                   (isinstance(axis.data, numpy.ndarray) and axis.data.ndim == 0)):
-                    axis = [int(axis.data)]
-                elif isinstance(axis.data, (list, numpy.ndarray)):
+        if axis is None:
+            axis = range(X.type.ndim)
+        elif isinstance(axis, TensorConstant) and isinstance(axis.data, (list, numpy.ndarray)):
             axis = [int(i) for i in axis.data]
+        elif not isinstance(axis, list):
+            raise TypeError("Axis must be a list. Got %s" % axis)
         # Make axis entries non-negative, and verify that axes are valid.
-        if isinstance(axis, list):
         for idx in xrange(len(axis)):
             if axis[idx] < 0:
                 axis[idx] += X.type.ndim
@@ -52,22 +37,10 @@ class GpuMaxAndArgmax(Op):
                 raise ValueError('Invalid axis: %s (the number of dimensions of the '
                                  'input is: %s)' % (axis[idx], X.type.ndim))
         # Sort axes and make them unique.
-        axis_set = set()  # used to build "broadcastable" variable below.
-        all_axes = []
-        if isinstance(axis, list):
-            axis_set = set(axis)
-            all_axes = list(axis_set)
-            all_axes.sort()
-            if all_axes == range(X.type.ndim):
-                axis = None
-        else:
-            all_axes = range(X.ndim)
-            axis_set = set(all_axes)
-        if axis is None:
-            axis = NoneConst.clone()
-        else:
-            axis = theano.tensor.as_tensor_variable(all_axes)
-            # assert axis.ndim == 1
+        axis_set = set(axis)  # used to build "broadcastable" variable below.
+        axis = list(axis_set)
+        axis.sort()
+        axis = theano.tensor.as_tensor_variable(axis)
         inputs = [as_gpuarray_variable(X, context_name), axis]
         # We keep the original broadcastable flags for dimensions on which
         # we do not perform the max / argmax.
@@ -78,13 +51,11 @@ class GpuMaxAndArgmax(Op):
         return Apply(self, inputs, outputs)
 
     def perform(self, node, inputs, outputs):
-        X, axes = inputs
+        # NB: I must rewrite this method with pygpu functions instead of numpy functions.
+        x, axes = inputs
         max, max_idx = outputs
-        if axes is None:
-            axes = tuple(range(X.ndim))
-        else:
+        X = numpy.asarray(x)
         axes = tuple(axes)
-            # axes = tuple(int(ax) for ax in axes)
         max[0] = theano._asarray(numpy.max(X, axes), dtype=node.outputs[0].dtype)
         # Numpy does not support multiple axes for argmax
         # Work around
@@ -110,45 +81,33 @@ class GpuMaxAndArgmax(Op):
         # Recall: fail = sub['fail']
         max_typecode = pygpu.gpuarray.dtype_to_typecode(node.inputs[0].dtype)
         argmax_typecode = pygpu.gpuarray.dtype_to_typecode(self.argmax_dtype)
-        # axes_ctype = pygpu.gpuarray.dtype_to_ctype(node.inputs[1].dtype)
         axes_ctype = 'int64_t'
+        assert node.inputs[1].ndim == 1
         ret = """
+        GpuArray temp;
         GpuArray* %(name)s_input = &%(X)s->ga;
         size_t %(name)s_input_ndim = PyGpuArray_NDIM(%(X)s);
-        """
-        if NoneConst.equals(node.inputs[1]):
-            ret += """
-            unsigned  %(name)s_redux_len = %(name)s_input_ndim;
-            unsigned* %(name)s_axes_to_reduce = new unsigned[%(name)s_redux_len];
-            for(unsigned i = 0; i < %(name)s_redux_len; ++i) {
-                %(name)s_axes_to_reduce[i] = i;
-            }
-            """
-        else:
-            assert node.inputs[1].ndim == 1
-            ret += """
+
         unsigned  %(name)s_redux_len = PyArray_DIM(%(axes)s, 0);
-            unsigned* %(name)s_axes_to_reduce = new unsigned[%(name)s_redux_len];
-            for(unsigned i = 0; i < %(name)s_redux_len; ++i) {
-                %(name)s_axes_to_reduce[i] =
-                    (unsigned)( ((%(axes_ctype)s*)PyArray_DATA(%(axes)s)) [i * (PyArray_STRIDES(%(axes)s)[0] / sizeof(%(axes_ctype)s))] );
+        unsigned* %(name)s_axes_to_reduce = (unsigned*)malloc(%(name)s_redux_len * sizeof(unsigned));
+        for (unsigned i = 0; i < %(name)s_redux_len; ++i) {
+            %(name)s_axes_to_reduce[i] = (unsigned) (*(%(axes_ctype)s*)PyArray_GETPTR1(%(axes)s, i));
         }
-            """
-        ret += """
+
         size_t  %(name)s_output_ndim = %(name)s_input_ndim - %(name)s_redux_len;
         size_t* %(name)s_output_dims = NULL;
-        if(%(name)s_output_ndim == 0) {
+        if (%(name)s_output_ndim == 0) {
             /* Current backend function GpuArray_maxandargmax does not work when
              * all axes need to be reduced. So to handle this case, we create a view
              * of the input as a matrix with 1 row and as many columns as elements
              * in the input, so that the 2nd dimenson of the matrix will be reduced. */
             size_t total_size = 1;
-            for(size_t i = 0; i < %(name)s_input_ndim; ++i) {
+            for (size_t i = 0; i < %(name)s_input_ndim; ++i) {
                 total_size *= PyGpuArray_DIM(%(X)s, i);
             }
             size_t newdims[2] = {1, total_size};
-            %(name)s_input = new GpuArray;
-            if(GA_NO_ERROR !=
+            %(name)s_input = &temp;
+            if (GA_NO_ERROR !=
                 GpuArray_reshape(%(name)s_input, &%(X)s->ga, 2, newdims, GA_ANY_ORDER, 0)
             ) {
                 %(fail)s
@@ -156,34 +115,34 @@ class GpuMaxAndArgmax(Op):
             %(name)s_redux_len = 1;
             %(name)s_axes_to_reduce[0] = 1;
         } else {
-            %(name)s_output_dims = new size_t[%(name)s_output_ndim];
-            if(%(name)s_redux_len == 1) {
-                for(unsigned i = 0; i < %(name)s_axes_to_reduce[0]; ++i) {
+            %(name)s_output_dims = (size_t*)malloc(%(name)s_output_ndim * sizeof(size_t));
+            if (%(name)s_redux_len == 1) {
+                for (unsigned i = 0; i < %(name)s_axes_to_reduce[0]; ++i) {
                     %(name)s_output_dims[i] = PyGpuArray_DIM(%(X)s, i);
                 }
-                for(unsigned i = %(name)s_axes_to_reduce[0] + 1; i < %(name)s_input_ndim; ++i) {
+                for (unsigned i = %(name)s_axes_to_reduce[0] + 1; i < %(name)s_input_ndim; ++i) {
                     %(name)s_output_dims[i-1] = PyGpuArray_DIM(%(X)s, i);
                 }
             } else {
                 int64_t current_input_pos = -1;
                 int64_t current_output_pos = -1;
-                for(unsigned i = 0; i < %(name)s_redux_len; ++i) {
-                    for(++current_input_pos; current_input_pos < %(name)s_axes_to_reduce[i]; ++current_input_pos) {
+                for (unsigned i = 0; i < %(name)s_redux_len; ++i) {
+                    for (++current_input_pos; current_input_pos < %(name)s_axes_to_reduce[i]; ++current_input_pos) {
                         %(name)s_output_dims[++current_output_pos] = PyGpuArray_DIM(%(X)s, current_input_pos);
                     }
                 }
-                for(++current_input_pos; current_input_pos < %(name)s_input_ndim; ++current_input_pos) {
+                for (++current_input_pos; current_input_pos < %(name)s_input_ndim; ++current_input_pos) {
                     %(name)s_output_dims[++current_output_pos] = PyGpuArray_DIM(%(X)s, current_input_pos);
                 }
             }
         }
-        if(theano_prep_output(&%(max)s, %(name)s_output_ndim, %(name)s_output_dims, %(max_typecode)s, GA_C_ORDER, %(X)s->context)) {
+        if (theano_prep_output(&%(max)s, %(name)s_output_ndim, %(name)s_output_dims, %(max_typecode)s, GA_C_ORDER, %(X)s->context)) {
             %(fail)s
         }
-        if(theano_prep_output(&%(argmax)s, %(name)s_output_ndim, %(name)s_output_dims, %(argmax_typecode)s, GA_C_ORDER, %(X)s->context)) {
+        if (theano_prep_output(&%(argmax)s, %(name)s_output_ndim, %(name)s_output_dims, %(argmax_typecode)s, GA_C_ORDER, %(X)s->context)) {
             %(fail)s
         }
-        if(GA_NO_ERROR !=
+        if (GA_NO_ERROR !=
             GpuArray_maxandargmax(&%(max)s->ga, &%(argmax)s->ga, %(name)s_input, %(name)s_redux_len, %(name)s_axes_to_reduce)
         ) {
             %(fail)s
@@ -200,9 +159,8 @@ class GpuMaxAndArgmax(Op):
 
     def c_code_cleanup(self, node, name, inputs, outputs, sub):
         return """
-        delete[] %(name)s_output_dims;
-        if(%(name)s_input != &%(X)s->ga) delete %(name)s_input;
-        delete[] %(name)s_axes_to_reduce;
+        free(%(name)s_output_dims);
+        free(%(name)s_axes_to_reduce);
         """ % {'name': name, 'X': inputs[0]}
 
 

theano/gpuarray/tests/test_GpuMaxAndArgmax.py

 from unittest import TestCase
+
+from theano.gpuarray import GpuArrayType
 from theano.tests import unittest_tools as utt
 import numpy as np
 import theano
 import theano.tensor as T
 
+from .config import mode_with_gpu, mode_without_gpu
+from .test_basic_ops import rand_gpuarray
+
+test_shape = (1000, 100, 10, 5, 2)
+
 
-def randomTensor(*shapes):
+def numpy_random_array(*shapes):
     dimlist = shapes
     size = 1
     for dimsize in dimlist:
         size *= dimsize
-    return np.random.normal(size=size).astype(np.float32).reshape(dimlist)
+    return np.random.normal(size=size).astype(theano.config.floatX).reshape(dimlist)
 
 
-def numpyMaxAndArgmax(X, axis=None):
+def numpy_maxandargmax(X, axis=None):
     if axis is None:
         axis = range(X.ndim)
     elif not isinstance(axis, (tuple, list)):
@@ -33,107 +40,93 @@ def numpyMaxAndArgmax(X, axis=None):
     reshaped_x = transposed_x.reshape(new_shape)
     return (ref_max, np.argmax(reshaped_x, axis=-1))
 
+# We run all tests with 5-D tensors of 10 000 000 elements.
+# NB: In each test, any first call of theano function should be ignored
+# with Theano config flag profiling.ignore_first_call=True.
+
+
+def check_if_gpu_maxandargmax_in_graph(theano_function):
+    assert len([node for node in theano_function.maker.fgraph.apply_nodes
+                if isinstance(node.op, theano.gpuarray.reduction.GpuMaxAndArgmax)]) > 0
+
+
+def check_if_gpu_maxandargmax_not_in_graph(theano_function):
+    assert len([node for node in theano_function.maker.fgraph.apply_nodes
+                if isinstance(node.op, theano.gpuarray.reduction.GpuMaxAndArgmax)]) == 0
+
+
+def run_gpu_tensor5(test_matrix=None, axis=None):
+    M = GpuArrayType(dtype=theano.config.floatX, broadcastable=(False,) * 5)()
+    f = theano.function([M], [T.max(M, axis=axis), T.argmax(M, axis=axis)], name='GPU-function', mode=mode_with_gpu)
+    check_if_gpu_maxandargmax_in_graph(f)
+    if test_matrix is None:
+        test_matrix = rand_gpuarray(*test_shape)
+    f(test_matrix)
+    theano_max, theano_argmax = f(test_matrix)
+    ref_max, ref_argmax = numpy_maxandargmax(np.asarray(test_matrix), axis=axis)
+    utt.assert_allclose(ref_max, theano_max)
+    utt.assert_allclose(ref_argmax, theano_argmax)
 
-class TestGpuMaxAndArgmax(TestCase):
-    # We run all tests with 5-D tensors of 10 000 000 elements.
-    # NB: In each test, any first call of theano function should be ignored
-    # with Theano config flag profiling.ignore_first_call=True.
-    # To just check if GpuMaxAndArgmax is called:
-    # $ theano-cache purge && THEANO_FLAGS=floatX=float32,device=cuda,profile=True,profiling.ignore_first_call=True \
-    # nosetests --verbose theano/gpuarray/tests/test_GpuMaxAndArgmax.py:TestGpuMaxAndArgmax.test_none
 
-    def _basic_test_tensor5(self, axis=None):
+def run_cpu_tensor5(test_matrix=None, axis=None):
     M = T.tensor5()
-        max_M = T.max(M, axis=axis)
-        argmax_M = T.argmax(M, axis=axis)
-        f = theano.function([M], [max_M, argmax_M])
-        test_matrix = randomTensor(1000, 100, 10, 5, 2)
+    f = theano.function([M], [T.max(M, axis=axis), T.argmax(M, axis=axis)], name='cpu-function', mode=mode_without_gpu)
+    check_if_gpu_maxandargmax_not_in_graph(f)
+    if test_matrix is None:
+        test_matrix = numpy_random_array(*test_shape)
     f(test_matrix)
     theano_max, theano_argmax = f(test_matrix)
-        ref_max, ref_argmax = numpyMaxAndArgmax(test_matrix, axis=axis)
+    ref_max, ref_argmax = numpy_maxandargmax(test_matrix, axis=axis)
     utt.assert_allclose(ref_max, theano_max)
     utt.assert_allclose(ref_argmax, theano_argmax)
 
-    def _basic_test_assert_equals(self, axis1, axis2):
-        M1 = T.tensor5()
-        M2 = T.tensor5()
-        f1 = theano.function([M1], [T.max(M1, axis=axis1), T.argmax(M1, axis=axis1)])
-        f2 = theano.function([M2], [T.max(M2, axis=axis2), T.argmax(M2, axis=axis2)])
-        test_matrix = randomTensor(1000, 100, 10, 5, 2)
-        f1(test_matrix)
-        f2(test_matrix)
-        theano1 = f1(test_matrix)
-        theano2 = f2(test_matrix)
-        ref1 = numpyMaxAndArgmax(test_matrix, axis1)
-        ref2 = numpyMaxAndArgmax(test_matrix, axis2)
-        utt.assert_allclose(ref1, ref2)
-        utt.assert_allclose(theano1, theano2)
-        utt.assert_allclose(ref1, theano1)
-
-    def test_none(self):
-        self._basic_test_tensor5(None)
-
-    def test_all_axes(self):
-        self._basic_test_tensor5((0, 1, 2, 3, 4))
-
-    def test_1_axe(self):
-        self._basic_test_tensor5(3)
-
-    def test_2_axes(self):
-        self._basic_test_tensor5((0, 3))
-
-    def test_3_axes(self):
-        self._basic_test_tensor5((0, 3, 4))
-
-    def test_4_axes(self):
-        self._basic_test_tensor5((0, 1, 2, 4))
-
-    def test_simple(self):
-        self._basic_test_tensor5(None)
-        self._basic_test_tensor5((0, 1, 2, 3, 4))
-        self._basic_test_tensor5((4, 1, 3, 2))
-
-    def test_assert_equals(self):
-        self._basic_test_assert_equals(None, (0, 1, 2, 3, 4))
-        self._basic_test_assert_equals(0, (0, 0))
-        self._basic_test_assert_equals((4, 1, 3, 2), (1, 2, 3, 4))
-        self._basic_test_assert_equals((4, 3, 2, 1, 0), None)
-        self._basic_test_assert_equals((1, 3, 4), (1, 4, 4, 1, 3, 1, 3, 4, 3, 1, 1, 3, 1, 4, 1, 4))
-
-    def test_simple_1_axis(self):
-        self._basic_test_tensor5(0)
-        self._basic_test_tensor5(1)
-        self._basic_test_tensor5(2)
-        self._basic_test_tensor5(3)
-        self._basic_test_tensor5(4)
-
-    def test_simple_2_axis(self):
-        self._basic_test_tensor5((0, 0))
-        self._basic_test_tensor5((0, 1))
-        self._basic_test_tensor5((0, 2))
-        self._basic_test_tensor5((0, 3))
-        self._basic_test_tensor5((0, 4))
-
-        self._basic_test_tensor5((1, 0))
-        self._basic_test_tensor5((1, 1))
-        self._basic_test_tensor5((1, 2))
-        self._basic_test_tensor5((1, 3))
-        self._basic_test_tensor5((1, 4))
-
-        self._basic_test_tensor5((2, 0))
-        self._basic_test_tensor5((2, 1))
-        self._basic_test_tensor5((2, 2))
-        self._basic_test_tensor5((2, 3))
-        self._basic_test_tensor5((2, 4))
-
-        self._basic_test_tensor5((3, 0))
-        self._basic_test_tensor5((3, 1))
-        self._basic_test_tensor5((3, 2))
-        self._basic_test_tensor5((3, 3))
-        self._basic_test_tensor5((3, 4))
-
-        self._basic_test_tensor5((4, 0))
-        self._basic_test_tensor5((4, 1))
-        self._basic_test_tensor5((4, 2))
-        self._basic_test_tensor5((4, 3))
-        self._basic_test_tensor5((4, 4))
+
+def run_tensor5(axis=None):
+    test_cpu_matrix = numpy_random_array(*test_shape)
+    test_gpu_matrix = rand_gpuarray(*test_shape)
+    run_cpu_tensor5(test_cpu_matrix, axis)
+    run_gpu_tensor5(test_gpu_matrix, axis)
+
+
+def test_none():
+    run_tensor5(None)
+
+
+def test_all_axes():
+    run_tensor5((0, 1, 2, 3, 4))
+
+
+def test_all_axes_unsorted():
+    run_tensor5((4, 1, 3, 0, 2))
+
+
+def test_axis_1():
+    run_tensor5(0)
+
+
+def test_axis_2():
+    run_tensor5(1)
+
+
+def test_axis_3():
+    run_tensor5(2)
+
+
+def test_axis_4():
+    run_tensor5(3)
+
+
+def test_axis_5():
+    run_tensor5(4)
+
+
+def test_2_axes():
+    run_tensor5((0, 3))
+
+
+def test_3_axes():
+    run_tensor5((0, 3, 4))
+
+
+def test_4_axes():
+    run_tensor5((0, 1, 2, 4))

theano/tensor/basic.py

@@ -1186,32 +1186,12 @@ class MaxAndArgmax(Op):
     def make_node(self, x, axis=None):
         x = _as_tensor_variable(x)
 
-        if isinstance(axis, (integer_types, numpy.integer)):
-            axis = [int(axis)]
-        elif isinstance(axis, numpy.ndarray) and axis.ndim == 0:
-            axis = [int(axis)]
-        elif isinstance(axis, (tuple, list, numpy.ndarray)):
-            axis = [int(a) for a in axis]
-            if axis == list(range(x.type.ndim)):
-                axis = None
-        elif isinstance(axis, Variable):
-            if NoneConst.equals(axis):
-                axis = None
-            elif not isinstance(axis, TensorConstant):
-                raise TypeError(
-                    "MaxAndArgmax needs a constant axis. Got %s" % axis)
-            else:
-                assert (axis.dtype.startswith("int") or
-                        axis.dtype.startswith("uint"))
-                if isinstance(axis.data, (integer_types, numpy.integer)) or \
-                   (isinstance(axis.data, numpy.ndarray) and
-                        axis.data.ndim == 0):
-                    axis = [int(axis.data)]
-                elif isinstance(axis.data, (list, numpy.ndarray)):
-                    axis = [int(i) for i in axis.data]
+        if axis is None:
+            axis = range(x.type.ndim)
+        elif not isinstance(axis, list):
+            raise TypeError("Axis must be a list. Got %s" % axis)
 
         # Make axis entries non-negative, and sort them
-        if isinstance(axis, list):
         for idx in xrange(len(axis)):
             if axis[idx] < 0:
                 axis[idx] += x.type.ndim
@@ -1219,7 +1199,6 @@ class MaxAndArgmax(Op):
 
         # Verify that axes are valid
         all_axes = []
-        if isinstance(axis, list):
         for ax in axis:
             if ax < 0 or ax >= x.type.ndim:
                 raise ValueError(
@@ -1227,12 +1206,6 @@ class MaxAndArgmax(Op):
                     'input is: %s)' % (ax, x.type.ndim))
             if ax not in all_axes:
                 all_axes.append(ax)
-        else:
-            all_axes = list(range(x.ndim))
-
-        if axis is None or axis == list(range(x.type.ndim)):
-            axis = NoneConst.clone()
-        else:
         axis = _as_tensor_variable(all_axes)
         assert axis.ndim == 1
         inputs = [x, axis]
@@ -1272,7 +1245,7 @@ class MaxAndArgmax(Op):
         x, axis = inp
         max, argmax = out
         fail = sub["fail"]
-        if NoneConst.equals(node.inputs[1]):
+        if NoneConst.equals(node.inputs[1]) or len(node.inputs[1].data) == node.inputs[0].ndim:
             axis_code = "axis = NPY_MAXDIMS;"
         else:
             assert node.inputs[1].ndim == 1
@@ -1637,6 +1610,26 @@ def max_and_argmax(a, axis=None, keepdims=False):
         will broadcast correctly against the original tensor.
 
     """
+    # Check axis and convert it to a Python list of integers.
+    if axis is None:
+        axis = range(a.type.ndim)
+    elif (isinstance(axis, (integer_types, numpy.integer)) or
+            (isinstance(axis, numpy.ndarray) and axis.ndim == 0)):
+        axis = [int(axis)]
+    elif isinstance(axis, (tuple, list, numpy.ndarray)):
+        axis = [int(i) for i in axis]
+    elif isinstance(axis, Variable):
+        if NoneConst.equals(axis):
+            axis = range(a.type.ndim)
+        elif not isinstance(axis, TensorConstant):
+            raise TypeError("max and argmax computation needs a constant axis. Got %s" % axis)
+        else:
+            assert (axis.dtype.startswith("int") or axis.dtype.startswith("uint"))
+            if (isinstance(axis.data, (integer_types, numpy.integer)) or
+                    (isinstance(axis.data, numpy.ndarray) and axis.data.ndim == 0)):
+                axis = [int(axis.data)]
+            elif isinstance(axis.data, (list, numpy.ndarray)):
+                axis = [int(i) for i in axis.data]
 
     out, argout = _max_and_argmax(a, axis)