Merge pull request #3254 from aonotas/new-style-exp-func

New style exp function

chainer/functions/math/exponential.py

@@ -2,11 +2,12 @@
 
 from chainer import cuda
 from chainer import function
+from chainer import function_node
 from chainer import utils
 from chainer.utils import type_check
 
 
-class Exp(function.Function):
+class Exp(function_node.FunctionNode):
 
     @property
     def label(self):
@@ -26,13 +27,14 @@ def forward_gpu(self, x):
         self.retain_outputs((0,))
         return cuda.cupy.exp(x[0]),
 
-    def backward(self, x, gy):
-        return utils.force_array(self.output_data[0] * gy[0]),
+    def backward(self, indexes, gy):
+        y = self.get_retained_outputs()[0]
+        return y * gy[0],
 
 
 def exp(x):
     """Elementwise exponential function."""
-    return Exp()(x)
+    return Exp().apply((x,))[0]
 
 
 class Log(function.Function):

tests/chainer_tests/function_hooks_tests/test_timer.py

@@ -20,6 +20,7 @@ def check_history(self, t, function_type, return_type):
 
 
 class SimpleLink(chainer.Link):
+
     def __init__(self):
         super(SimpleLink, self).__init__()
         with self.init_scope():
@@ -88,7 +89,7 @@ def setUp(self):
         self.gy = numpy.random.uniform(-0.1, 0.1, (3, 5)).astype(numpy.float32)
 
     def check_forward(self, x):
-        self.f(chainer.Variable(x))
+        self.f.apply((chainer.Variable(x),))
         self.assertEqual(1, len(self.h.call_history))
         check_history(self, self.h.call_history[0], functions.Exp, float)
 
@@ -101,7 +102,7 @@ def test_forward_gpu(self):
 
     def check_backward(self, x, gy):
         x = chainer.Variable(x)
-        y = self.f(x)
+        y = self.f.apply((x,))[0]
         y.grad = gy
         y.backward()
         self.assertEqual(2, len(self.h.call_history))
@@ -163,15 +164,15 @@ def setUp(self):
 
     def test_summary(self):
         x = self.x
-        self.f(chainer.Variable(x))
-        self.f(chainer.Variable(x))
+        self.f.apply((chainer.Variable(x),))
+        self.f.apply((chainer.Variable(x),))
         self.assertEqual(2, len(self.h.call_history))
         self.assertEqual(1, len(self.h.summary()))
 
     def test_print_report(self):
         x = self.x
-        self.f(chainer.Variable(x))
-        self.f(chainer.Variable(x))
+        self.f.apply((chainer.Variable(x),))
+        self.f.apply((chainer.Variable(x),))
         io = six.StringIO()
         self.h.print_report(file=io)
         expect = r'''\AFunctionName  ElapsedTime  Occurrence

tests/chainer_tests/functions_tests/math_tests/test_exponential.py

@@ -42,6 +42,17 @@ def check_backward_cpu(self, op):
     def check_backward_gpu(self, op):
         self.check_backward(op, cuda.to_gpu(self.x), cuda.to_gpu(self.gy))
 
+    def check_double_backward(self, op, x_data, y_grad, y_grad_grad):
+        gradient_check.check_double_backward(
+            op, x_data, y_grad, y_grad_grad, atol=1e-4, rtol=1e-3, dtype='d')
+
+    def check_double_backward_cpu(self, op):
+        self.check_double_backward(op, self.x, self.gy, self.ggy)
+
+    def check_double_backward_gpu(self, op):
+        self.check_double_backward(op, cuda.to_gpu(
+            self.x), cuda.to_gpu(self.gy), cuda.to_gpu(self.ggy))
+
     def check_label(self, op, expected):
         self.assertEqual(op().label, expected)
 
@@ -55,6 +66,7 @@ class TestExp(UnaryFunctionsTestBase):
     def make_data(self):
         x = numpy.random.uniform(-1, 1, self.shape).astype(self.dtype)
         gy = numpy.random.uniform(-1, 1, self.shape).astype(self.dtype)
+        self.ggy = numpy.random.uniform(-1, 1, self.shape).astype(self.dtype)
         return x, gy
 
     @condition.retry(3)
@@ -78,6 +90,15 @@ def test_backward_gpu(self):
     def test_label(self):
         self.check_label(F.Exp, 'exp')
 
+    @condition.retry(3)
+    def test_double_backward_cpu(self):
+        self.check_double_backward_cpu(F.exp)
+
+    @attr.gpu
+    @condition.retry(3)
+    def test_double_backward_gpu(self):
+        self.check_double_backward_gpu(F.exp)
+
 
 @testing.parameterize(*testing.product({
     'shape': [(3, 2), ()],