[disabled BUGfix,CRASH] related to the cudnn pooling gradient.

theano/sandbox/cuda/dnn.py

@@ -737,7 +737,7 @@ if (err%(name)s != CUDNN_STATUS_SUCCESS) {
 
         out = self(img, desc)
 
-        g_out = GpuDnnPoolGrad()(out, grad, img, desc)
+        g_out = GpuDnnPoolGrad()(img, out, grad, desc)
 
         return g_out, theano.gradient.DisconnectedType()()
 
@@ -745,8 +745,8 @@ if (err%(name)s != CUDNN_STATUS_SUCCESS) {
         # not connected to desc
         return [[1], [0]]
 
-#    def c_code_cache_version(self):
-#        return (3,)
+    def c_code_cache_version(self):
+        return (4,)
 
 
 class GpuDnnPoolGrad(DnnBase):
@@ -754,13 +754,13 @@ class GpuDnnPoolGrad(DnnBase):
     The pooling gradient.
 
     :param inp: the input of the pooling.
-    :param inp_grad: same size as out, but is the corresponding gradient information.
     :param out: the output of the pooling in the forward.
+    :param inp_grad: same size as out, but is the corresponding gradient information.
     :param desc: The pooling descriptor.
     """
     __props__ = ()
 
-    def make_node(self, inp, inp_grad, out, desc):
+    def make_node(self, inp, out, inp_grad, desc):
         inp = as_cuda_ndarray_variable(inp)
         if inp.type.ndim != 4:
             raise TypeError('inp must be 4D tensor')
@@ -777,7 +777,7 @@ class GpuDnnPoolGrad(DnnBase):
                 or desc.type.ctype != 'cudnnPoolingDescriptor_t':
             raise TypeError('desc must be cudnnPoolingDescriptor_t')
 
-        return Apply(self, [inp, inp_grad, out, desc],
+        return Apply(self, [inp, out, inp_grad, desc],
                      [inp.type()])
 
     def c_support_code_struct(self, node, struct_id):
@@ -830,7 +830,10 @@ if (output_grad%(id)d != NULL) { cudnnDestroyTensor4dDescriptor(output_grad%(id)
 """ % dict(id=struct_id)
 
     def c_code(self, node, name, inputs, outputs, sub):
-        inp, inp_grad, out, desc = inputs
+        # Here the name out and inp are based on the cudnn definition.
+        # Not the definition of this class.
+        # This make it complicated.
+        out, inp, inp_grad, desc = inputs
         out_grad, = outputs
 
         set_in = "\n".join([
@@ -900,7 +903,7 @@ if (err%(name)s != CUDNN_STATUS_SUCCESS) {
            output_grad_desc="output_grad"+str(sub['struct_id']))
 
     def c_code_cache_version(self):
-        return (3,)
+        return (4,)
 
 
 def dnn_pool(img, ws, stride=(1, 1), mode='max'):
@@ -1178,36 +1181,34 @@ if cuda_available:
                              border_mode=border_mode, subsample=subsample,
                              direction_hint=direction_hint)]
 
-# DISABLED as there is problems in the handling of borders
-#    @register_opt('cudnn')
+    @register_opt('cudnn')
     @local_optimizer([GpuDownsampleFactorMax])
     def local_pool_dnn(node):
         if not dnn_available():
             return
         if isinstance(node.op, GpuDownsampleFactorMax):
-            if node.op.ignore_border:
+            if not node.op.ignore_border:
                 return
             img, = node.inputs
             ds = node.op.ds
             return [dnn_pool(gpu_contiguous(img), ds, ds)]
 
-# DISABLED as there is problems in the handling of borders
-#    @register_opt('cudnn')
+    @register_opt('cudnn')
     @local_optimizer([GpuDownsampleFactorMaxGrad])
     def local_pool_dnn_grad(node):
         if not dnn_available():
             return
         if isinstance(node.op, GpuDownsampleFactorMaxGrad):
-            if node.op.ignore_border:
+            if not node.op.ignore_border:
                 return
             inp, out, inp_grad = node.inputs
             ds = node.op.ds
 
             desc = GpuDnnPoolDesc(ws=ds, stride=ds, mode="max")()
-
             return [GpuDnnPoolGrad()(gpu_contiguous(inp),
+                                     gpu_contiguous(out),
                                      gpu_contiguous(inp_grad),
-                                     gpu_contiguous(out), desc)]
+                                     desc)]
 
     @register_opt('cudnn')
     @local_optimizer([GpuSoftmax])

theano/sandbox/cuda/tests/test_dnn.py

@@ -11,6 +11,7 @@ import theano.tensor as T
 import theano.tests.unittest_tools as utt
 from theano.sandbox.neighbours import images2neibs, neibs2images
 from theano.tensor.signal.downsample import max_pool_2d
+from theano.tensor.signal.downsample import DownsampleFactorMaxGrad
 
 
 # Skip test if cuda_ndarray is not available.
@@ -61,15 +62,23 @@ def test_pooling():
             for stride in (2, 3):
                 if stride > ws:
                     continue
-                out1 = cuda.dnn.dnn_pool(
-                    x, ws=(ws, ws),
-                    stride=(stride, stride),
-                    mode='max' if func is T.max else "average")
+                if ws == stride and func is T.max:
+                    # We will check that the opt introduced it.
+                    out1 = max_pool_2d(x, (ws, ws), ignore_border=True)
+                else:
+                    out1 = cuda.dnn.dnn_pool(
+                        x, ws=(ws, ws),
+                        stride=(stride, stride),
+                        mode='max' if func is T.max else "average")
                 out2 = pool_2d_i2n(x, ds=(ws, ws), strides=(stride, stride),
                                    pool_function=func)
 
                 f1 = theano.function([x], out1, mode=mode_with_gpu)
+                assert any([isinstance(node.op, cuda.dnn.GpuDnnPool)
+                            for node in f1.maker.fgraph.apply_nodes])
                 f2 = theano.function([x], out2, mode=mode_with_gpu)
+                assert not any([isinstance(node.op, cuda.dnn.GpuDnnPool)
+                                for node in f2.maker.fgraph.apply_nodes])
                 for shp in [(1, 10, 100, 100),
                             (1, 3, 99, 99),
                             (32, 1, 147, 197),
@@ -81,6 +90,53 @@ def test_pooling():
                     assert numpy.allclose(a, b,
                                           atol=numpy.finfo(numpy.float32).eps)
 
+        # Test the grad
+        for shp in [(1, 1, 2, 2),
+                    (1, 1, 3, 3)]:
+            data = numpy.random.normal(0, 1, shp).astype("float32")*10
+
+            ws = 2
+            strides = 2
+
+            # This test the CPU grad + opt + GPU implemtentation
+            def fn(x):
+                return max_pool_2d(x, (ws, ws), ignore_border=True)
+            theano.tests.unittest_tools.verify_grad(fn, [data],
+                                                    cast_to_output_type=False,
+                                                    mode=mode_with_gpu)
+            # Confirm that the opt would have inserted it.
+            f = theano.function([x], theano.grad(fn(x).sum(), x),
+                                mode=mode_with_gpu)
+            assert any([isinstance(node.op, cuda.dnn.GpuDnnPoolGrad)
+                        for node in f.maker.fgraph.toposort()])
+
+            # Test the GPU grad + GPU implementation
+            def fn(x):
+                dnn_op = cuda.dnn.dnn_pool(
+                    x, ws=(ws, ws),
+                    stride=(stride, stride),
+                    mode='max' if func is T.max else "average")
+                return dnn_op
+            theano.tests.unittest_tools.verify_grad(fn, [data],
+                                                    cast_to_output_type=False,
+                                                    mode=mode_with_gpu)
+            # Confirm that we get the good op.
+            f = theano.function([x], theano.grad(fn(x).sum(), x),
+                                mode=mode_with_gpu)
+            assert any([isinstance(node.op, cuda.dnn.GpuDnnPoolGrad)
+                        for node in f.maker.fgraph.toposort()])
+            g_out = f(data)
+
+            if func is T.max:
+                # Compare again the CPU result
+                out = max_pool_2d(x, (ws, ws), ignore_border=True)
+                f = theano.function([x], theano.grad(out.sum(), x),
+                                    mode=mode_without_gpu)
+                assert any([isinstance(node.op, DownsampleFactorMaxGrad)
+                            for node in f.maker.fgraph.toposort()])
+                c_out = f(data)
+                assert numpy.allclose(c_out, g_out)
+
 
 def test_pooling_opt():
     if not cuda.dnn.dnn_available():
@@ -90,7 +146,7 @@ def test_pooling_opt():
 
     f = theano.function(
         [x],
-        max_pool_2d(x, ds=(2, 2)),
+        max_pool_2d(x, ds=(2, 2), ignore_border=True),
         mode=mode_with_gpu)
 
     assert any([isinstance(n.op, cuda.dnn.GpuDnnPool)
@@ -98,7 +154,7 @@ def test_pooling_opt():
 
     f = theano.function(
         [x],
-        T.grad(max_pool_2d(x, ds=(2, 2)).sum(), x),
+        T.grad(max_pool_2d(x, ds=(2, 2), ignore_border=True).sum(), x),
         mode=mode_with_gpu.including("cudnn"))
 
     assert any([isinstance(n.op, cuda.dnn.GpuDnnPoolGrad)