Merge pull request #5155 from sygi/argmax-only-cpu

theano/tensor/basic.py

@@ -1419,6 +1419,170 @@ class MaxAndArgmax(Op):
 _max_and_argmax = MaxAndArgmax()
 
 
+class Argmax(Op):
+    """
+    Calculate the argmax over a given axis or over all axes.
+    """
+    nin = 2  # tensor, axis
+    nout = 1
+    E_axis = 'invalid axis'
+    __props__ = ()
+
+    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(
+                    "Argmax 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]
+
+        # 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
+            axis.sort()
+
+        # 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(
+                        'Invalid axis: %s (the number of dimensions of the '
+                        '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]
+
+        # We keep the original broadcastable flags for dimensions on which
+        # we do not perform the argmax.
+        broadcastable = [b for i, b in enumerate(x.type.broadcastable)
+                         if i not in all_axes]
+        outputs = [tensor('int64', broadcastable, name='argmax')]
+        return Apply(self, inputs, outputs)
+
+    def perform(self, node, inp, outs):
+        x, axes = inp
+        max_idx, = outs
+        if axes is None:
+            axes = tuple(range(x.ndim))
+        else:
+            axes = tuple(int(ax) for ax in axes)
+
+        # Numpy does not support multiple axes for argmax
+        # Work around
+        keep_axes = numpy.array([i for i in range(x.ndim) if i not in axes],
+                                dtype='int64')
+        # Not-reduced axes in front
+        transposed_x = numpy.transpose(x, numpy.concatenate((keep_axes,
+                                                             axes)))
+        kept_shape = transposed_x.shape[:len(keep_axes)]
+        reduced_shape = transposed_x.shape[len(keep_axes):]
+        new_shape = kept_shape + (numpy.prod(reduced_shape),)
+        reshaped_x = transposed_x.reshape(new_shape)
+
+        max_idx[0] = theano._asarray(numpy.argmax(reshaped_x, axis=-1),
+                                     dtype='int64')
+
+    def c_code(self, node, name, inp, out, sub):
+        x, axis = inp
+        argmax, = out
+        fail = sub["fail"]
+        if NoneConst.equals(node.inputs[1]):
+            axis_code = "axis = NPY_MAXDIMS;"
+        else:
+            assert node.inputs[1].ndim == 1
+            # Fall back to perform() if there are multiple axes
+            if len(node.inputs[1].data) > 1:
+                raise NotImplementedError()
+            axis_code = """
+            axis = ((dtype_%(axis)s*)PyArray_DATA(%(axis)s))[0];
+            if(axis > PyArray_NDIM(%(x)s)-1 || axis < -PyArray_NDIM(%(x)s)){
+                PyErr_SetString(PyExc_ValueError,
+                "Argmax, bad axis argument");
+                %(fail)s
+            }
+            """ % locals()
+        ret = """
+        int axis;
+
+        Py_CLEAR(%(argmax)s);//todo pass them as out parameter.
+        %(axis_code)s
+
+        %(argmax)s = (PyArrayObject*)PyArray_ArgMax(%(x)s, axis, NULL);
+        if(%(argmax)s == NULL){
+            %(fail)s;
+        }
+        if(!PyArray_CheckExact(%(argmax)s)){
+            %(argmax)s = (PyArrayObject*)PyArray_FromAny((PyObject*)%(argmax)s, NULL, 0, 0, NPY_ARRAY_ENSUREARRAY, NULL);
+            if(%(argmax)s == NULL){
+                %(fail)s;
+            }
+        }
+        if(PyArray_TYPE(%(argmax)s) != NPY_INT64){
+            PyObject * tmp = PyArray_Cast(%(argmax)s, NPY_INT64);
+            if (NULL == tmp){
+                %(fail)s;
+            }
+            Py_DECREF(%(argmax)s);
+            %(argmax)s = (PyArrayObject*)tmp;
+        }
+        """
+        return ret % locals()
+
+    def c_code_cache_version(self):
+        return (0,)
+
+    def infer_shape(self, node, shapes):
+        ishape, axis_shape = shapes
+        axis = node.inputs[1]
+        if axis.data is None:
+            return [()]
+        rval = tuple([ishape[i] for (i, b) in enumerate(
+            node.inputs[0].type.broadcastable) if i not in axis.data])
+        return [rval]
+
+    def grad(self, inp, grads):
+        x, axis = inp
+
+        axis_grad = grad_undefined(
+            self, 1, axis,
+            "argmax is not defined for non-integer axes so"
+            " argmax(x, axis+eps) is undefined")
+
+        return [x.zeros_like(), axis_grad]
+
+_argmax = Argmax()
+
+
 def makeKeepDims(x, y, axis):
     """
     Reintroduces in y with length one the axes of x which have been left out
@@ -1541,9 +1705,6 @@ def argmax(x, axis=None, keepdims=False):
         will broadcast correctly against the original tensor.
 
     """
-    # In python (using MaxAndArgmax.perform()) this leads to a wasteful
-    # implementation that goes through the data twice instead of once
-    # but when Argmax.c_impl() is in place, it should be fine.
     argout = max_and_argmax(x, axis)[1]
 
     if keepdims:

theano/tensor/nnet/tests/test_nnet.py

@@ -1314,10 +1314,10 @@ def test_argmax_pushdown():
         # for node in fgraph.toposort():
         # print node.op
         assert len(fgraph.toposort()) == 2  # an output_guard is second
-        assert fgraph.toposort()[0].op == tensor.basic._max_and_argmax
+        assert fgraph.toposort()[0].op == tensor.basic._argmax
         assert str(fgraph.toposort()[1].op) == 'OutputGuard'
         assert check_stack_trace(
-            fgraph, ops_to_check=tensor.basic._max_and_argmax)
+            fgraph, ops_to_check=tensor.basic._argmax)
         x = tensor.matrix()
         # test that the max_and_argmax is not pushed down if the max is used
         out = tensor.max_and_argmax(
@@ -1362,7 +1362,7 @@ def test_argmax_pushdown_bias():
     # print 'AFTER'
     # for node in fgraph.toposort():
     #    print node.op
-    types_to_check = (tensor.DimShuffle, tensor.Elemwise, tensor.MaxAndArgmax)
+    types_to_check = (tensor.DimShuffle, tensor.Elemwise, tensor.Argmax)
     assert len(fgraph.toposort()) == 4
     for i, type in enumerate(types_to_check):
         assert isinstance(fgraph.toposort()[i].op, type)

theano/tensor/opt_uncanonicalize.py

@@ -73,6 +73,9 @@ def local_max_and_argmax(node):
             new = CAReduce(scal.maximum, axis)(node.inputs[0])
             return [new, None]
 
+        if len(node.outputs[0].clients) == 0:
+            return [None, T._argmax(node.inputs[0], node.inputs[1])]
+
 
 @register_uncanonicalize
 @gof.local_optimizer([T.neg])