Move softmaxgrad dnn test to test_dnn.py file

theano/sandbox/gpuarray/tests/test_dnn.py

@@ -16,6 +16,7 @@ from .. import dnn
 from ..basic_ops import GpuAllocEmpty
 
 from .test_basic_ops import mode_with_gpu, mode_without_gpu
+from . import test_nnet
 
 
 def test_dnn_conv_desc_merge():
@@ -686,3 +687,121 @@ def test_version():
     if not dnn.dnn_available():
         raise SkipTest(dnn.dnn_available.msg)
     assert isinstance(dnn.version(), (int, tuple))
+
+
+class test_SoftMax(test_nnet.test_SoftMax):
+    gpu_op = dnn.GpuDnnSoftmax
+    gpu_grad_op = dnn.GpuDnnSoftmaxGrad
+    mode = mode_with_gpu
+
+    def test_softmax_shape_0(self):
+        raise SkipTest("Cudnn do not suport 0 shapes")
+
+    def test_softmax_grad(self):
+        def cmp(n, m, f, f_gpu):
+            data = numpy.arange(n * m, dtype='float32').reshape(n, m)
+            gdata = numpy.asarray(data)[:, :, None, None]
+
+            out = f(data)
+            gout = numpy.asarray(f_gpu(gdata))[:, :, 0, 0]
+            assert numpy.allclose(out, gout), numpy.absolute(out - gout)
+
+        x = T.matrix('x', 'float32')
+        x_gpu = T.tensor4('x_gpu', 'float32')
+        f_z = T.nnet.softmax_op
+        f_gpu = dnn.GpuDnnSoftmax(
+            'bc01',
+            'accurate',
+            'channel'
+        )
+
+        # Verify the grad operation
+        dims = (2, 3, 4, 5)
+        gdata = numpy.arange(
+            numpy.product(dims),
+            dtype='float32'
+        ).reshape(dims)
+        T.verify_grad(f_gpu, [gdata], rng=numpy.random,
+                      mode=mode_with_gpu)
+
+        # Verify that the CPU and GPU implementations return the same results
+        # up to a tolerance.
+
+        self._test_softmax(
+            x,
+            x_gpu,
+            f_z,
+            f_gpu,
+            cmp,
+            self.mode,
+        )
+
+        self._test_softmax(
+            x, x, f_z, f_z, self._cmp,
+            self.mode
+        )
+
+        # Verify that the SoftmaxGrad -> Gpu[Dnn]SoftmaxGrad
+        # optimization is applied when cudnn is required
+        y = T.fvector('y')
+        f = theano.function(
+            [y],
+            T.grad(T.nnet.softmax(y).mean(), y),
+            mode=mode_with_gpu
+        )
+        sorted_f = f.maker.fgraph.toposort()
+        assert(len([i
+                    for i in sorted_f
+                    if isinstance(
+                        i.op,
+                        self.gpu_grad_op
+                    )]) == 1)
+        assert(len([i
+                    for i in sorted_f
+                    if isinstance(
+                        i.op,
+                        theano.tensor.nnet.SoftmaxGrad
+                    )]) == 0)
+
+        # Verify that the SoftmaxGrad -> Gpu[Dnn]SoftmaxGrad
+        # optimization is not applied when cudnn is excluded or not
+        # available
+        mode_wo_cudnn = mode_with_gpu.excluding("cudnn")
+        y = T.fvector('y')
+        f = theano.function(
+            [y],
+            T.grad(T.nnet.softmax(y).mean(), y),
+            mode=mode_wo_cudnn
+        )
+        sorted_f = f.maker.fgraph.toposort()
+        assert(len([i
+                    for i in sorted_f
+                    if isinstance(
+                        i.op,
+                        self.gpu_grad_op
+                    )]) == 0)
+        assert(len([i
+                    for i in sorted_f
+                    if isinstance(
+                        i.op,
+                        theano.tensor.nnet.SoftmaxGrad
+                    )]) == 1)
+
+        # Verify that the SoftmaxGrad -> GpuDnnSoftmaxGrad do not
+        # crash with manual graph
+        y = T.fvector('y')
+        o = theano.tensor.nnet.SoftmaxGrad()(y, y*2)
+        f = theano.function([y], o, mode=mode_with_gpu)
+        sorted_f = f.maker.fgraph.toposort()
+        assert(len([i
+                    for i in sorted_f
+                    if isinstance(
+                        i.op,
+                        self.gpu_grad_op
+                    )]) == 1)
+        assert(len([i
+                    for i in sorted_f
+                    if isinstance(
+                        i.op,
+                        theano.tensor.nnet.SoftmaxGrad
+                    )]) == 0)

theano/sandbox/gpuarray/tests/test_nnet.py

@@ -12,12 +12,13 @@ from theano.sandbox import gpuarray
 # We let that import do the init of the back-end if needed.
 from .test_basic_ops import (mode_with_gpu,
                              mode_without_gpu)
-
 from ..nnet import (
     GpuCrossentropySoftmaxArgmax1HotWithBias,
     GpuCrossentropySoftmax1HotWithBiasDx,
     GpuSoftmaxWithBias, GpuSoftmax)
 
+mode_wo_cudnn = mode_with_gpu.excluding("cudnn")
+
 
 def test_GpuCrossentropySoftmaxArgmax1HotWithBias():
     """
@@ -294,6 +295,9 @@ def softmax_unittest_template(dtypeInput):
 
 
 class test_SoftMax(unittest.TestCase):
+    gpu_op = GpuSoftmax
+    mode = mode_wo_cudnn
+
     def _test_softmax(
         self,
         x,
@@ -301,8 +305,7 @@ class test_SoftMax(unittest.TestCase):
         f_z,
         f_gpu_z,
         cmp,
-        gpu_mode,
-        check_types
+        gpu_mode
     ):
         """
         This is basic test for GpuSoftmax and GpuDnnSoftmax
@@ -314,8 +317,8 @@ class test_SoftMax(unittest.TestCase):
         f_gpu_z_out = f_gpu_z(x_gpu)
 
         f = theano.function([x], f_z_out, mode=mode_without_gpu)
-        f_gpu = theano.function([x_gpu], f_gpu_z_out, mode=gpu_mode)
-        check_types(f, f_gpu)
+        f_gpu = theano.function([x_gpu], f_gpu_z_out, mode=self.mode)
+        self._check_types(f, f_gpu, T.nnet.Softmax, self.gpu_op)
 
         # we need to test n>32*1024 to check that we make the block loop.
         cmp(1, 5, f, f_gpu)
@@ -349,195 +352,39 @@ class test_SoftMax(unittest.TestCase):
         gout = f_gpu(data)
         assert numpy.allclose(out, gout), numpy.absolute(out - gout)
 
-    def _check_types(self, graph, graph_gpu, topo_idx, f_type, f_gpu_type):
+    def _check_types(self, graph, graph_gpu, f_type, f_gpu_type):
         assert isinstance(graph.maker.fgraph.toposort()[-1].op, f_type)
-        assert isinstance(
-            graph_gpu.maker.fgraph.toposort()[topo_idx].op,
-            f_gpu_type
-        )
+        assert len([node for node in graph_gpu.maker.fgraph.toposort()
+                    if isinstance(node.op, f_gpu_type)]) == 1
 
     def test_softmax(self):
         x = T.fmatrix('x')
         z = T.nnet.softmax_op
 
-        def check_types_without_cudnn(graph, graph_gpu):
-            self._check_types(
-                graph,
-                graph_gpu,
-                -2,
-                type(z),
-                GpuSoftmax
-            )
-
-        mode_wo_cudnn = mode_with_gpu.excluding("cudnn")
         f, f_gpu = self._test_softmax(
             x,
             x,
             z,
             z,
             self._cmp,
-            mode_wo_cudnn,
-            check_types_without_cudnn
+            self.mode,
         )
 
         # cuDNN R1 cannot handle these test cases but the Theano softmax can so
         # we test them only for the Theano softmax.
         self._cmp(2 << 15, 5, f, f_gpu)
-        self._cmp(0, 10, f, f_gpu)
 
-    def test_softmax_cudnn(self):
-        from .. import dnn
-        if not dnn.dnn_available():
-            raise SkipTest(dnn.dnn_available.msg)
+    def test_softmax_shape_0(self):
         x = T.fmatrix('x')
         z = T.nnet.softmax_op
 
-        def check_types_with_cudnn(graph, graph_gpu):
-            self._check_types(
-                graph,
-                graph_gpu,
-                -3,
-                type(z),
-                dnn.GpuDnnSoftmax
-            )
-
         f, f_gpu = self._test_softmax(
             x,
             x,
             z,
             z,
             self._cmp,
-            mode_with_gpu,
-            check_types_with_cudnn
+            self.mode,
         )
-
-    def test_cudnn_softmax_grad(self):
-        from .. import dnn
-        if not dnn.dnn_available():
-            raise SkipTest(dnn.dnn_available.msg)
-
-        def cmp(n, m, f, f_gpu):
-            data = numpy.arange(n * m, dtype='float32').reshape(n, m)
-            gdata = numpy.asarray(data)[:, :, None, None]
-
-            out = f(data)
-            gout = numpy.asarray(f_gpu(gdata))[:, :, 0, 0]
-            assert numpy.allclose(out, gout), numpy.absolute(out - gout)
-
-        x = T.matrix('x', 'float32')
-        x_gpu = T.tensor4('x_gpu', 'float32')
-        f_z = T.nnet.softmax_op
-        f_gpu = dnn.GpuDnnSoftmax(
-            'bc01',
-            'accurate',
-            'channel'
-        )
-
-        # Verify the grad operation
-        dims = (2, 3, 4, 5)
-        gdata = numpy.arange(
-            numpy.product(dims),
-            dtype='float32'
-        ).reshape(dims)
-        T.verify_grad(f_gpu, [gdata], rng=numpy.random,
-                      mode=mode_with_gpu)
-
-        def check_types(graph, graph_gpu):
-            self._check_types(
-                graph,
-                graph_gpu,
-                -1,
-                type(f_z),
-                dnn.GpuDnnSoftmax
-            )
-
-        def check_types_opt(graph, graph_gpu):
-            assert isinstance(graph.maker.fgraph.toposort()[-1].op, type(f_z))
-            assert len([n for n in graph_gpu.maker.fgraph.toposort()
-                        if isinstance(
-                            n.op,
-                            dnn.GpuDnnSoftmax
-                        )]) == 1
-
-        # Verify that the CPU and GPU implementations return the same results
-        # up to a tolerance.
-
-        self._test_softmax(
-            x,
-            x_gpu,
-            f_z,
-            f_gpu,
-            cmp,
-            mode_with_gpu,
-            check_types
-        )
-
-        mode_w_cudnn = mode_with_gpu.including("cudnn")
-        self._test_softmax(
-            x, x, f_z, f_z, self._cmp,
-            mode_w_cudnn, check_types_opt
-        )
-
-        # Verify that the SoftmaxGrad -> GpuDnnSoftmaxGrad optimization is
-        # applied when cudnn is required
-        y = T.fvector('y')
-        f = theano.function(
-            [y],
-            T.grad(T.nnet.softmax(y).mean(), y),
-            mode=mode_with_gpu
-        )
-        sorted_f = f.maker.fgraph.toposort()
-        assert(len([i
-                    for i in sorted_f
-                    if isinstance(
-                        i.op,
-                        dnn.GpuDnnSoftmaxGrad
-                    )]) == 1)
-        assert(len([i
-                    for i in sorted_f
-                    if isinstance(
-                        i.op,
-                        theano.tensor.nnet.SoftmaxGrad
-                    )]) == 0)
-
-        # Verify that the SoftmaxGrad -> GpuDnnSoftmaxGrad optimization is not
-        # applied when cudnn is excluded or not available
-        mode_wo_cudnn = mode_with_gpu.excluding("cudnn")
-        y = T.fvector('y')
-        f = theano.function(
-            [y],
-            T.grad(T.nnet.softmax(y).mean(), y),
-            mode=mode_wo_cudnn
-        )
-        sorted_f = f.maker.fgraph.toposort()
-        assert(len([i
-                    for i in sorted_f
-                    if isinstance(
-                        i.op,
-                        dnn.GpuDnnSoftmaxGrad
-                    )]) == 0)
-        assert(len([i
-                    for i in sorted_f
-                    if isinstance(
-                        i.op,
-                        theano.tensor.nnet.SoftmaxGrad
-                    )]) == 1)
-
-        # Verify that the SoftmaxGrad -> GpuDnnSoftmaxGrad do not
-        # crash with manual graph
-        y = T.fvector('y')
-        o = theano.tensor.nnet.SoftmaxGrad()(y, y*2)
-        f = theano.function([y], o, mode=mode_with_gpu)
-        sorted_f = f.maker.fgraph.toposort()
-        assert(len([i
-                    for i in sorted_f
-                    if isinstance(
-                        i.op,
-                        dnn.GpuDnnSoftmaxGrad
-                    )]) == 1)
-        assert(len([i
-                    for i in sorted_f
-                    if isinstance(
-                        i.op,
-                        theano.tensor.nnet.SoftmaxGrad
-                    )]) == 0)
+        # Theano can handle that case, but cudnn can't
+        self._cmp(0, 10, f, f_gpu)