Merge pull request #1054 from pascanur/new_optimizations_scan

theano/scan_module/scan_op.py

@@ -1375,13 +1375,13 @@ class Scan(PureOp):
         def compute_gradient(y, g_y):
             if 'int' in str(g_y.dtype):
                 raise TypeError("Gradients may never be integers but g_y "
-                        "has type "+str(g_y.type))
+                        "has type " + str(g_y.type))
 
             wrt = [x for x in theano.gof.graph.inputs([y])
                     if x in diff_inputs]
             grads = gradient.grad(
-                    cost = None,
-                    known_grads = {y : g_y },
+                    cost=None,
+                    known_grads={y: g_y},
                     wrt=wrt, consider_constant=wrt,
                     disconnected_inputs='ignore',
                     return_disconnected='None')
@@ -1727,7 +1727,7 @@ class Scan(PureOp):
         node = outs[0].owner
         for idx in xrange(self.n_shared_outs):
             disconnected = True
-            connected_flags = self.connection_pattern(node)[idx+start]
+            connected_flags = self.connection_pattern(node)[idx + start]
             for dC_dout, connected in zip(dC_douts, connected_flags):
                 if (not isinstance(dC_dout.type, DisconnectedType) and
                         connected):

theano/scan_module/scan_utils.py

@@ -191,8 +191,6 @@ def get_updates_and_outputs(ls):
             this function know how to put it in that order?
 
     """
-
-
     def is_outputs(elem):
         if (isinstance(elem, (list, tuple)) and
             all([isinstance(x, theano.Variable) for x in elem])):
@@ -206,7 +204,7 @@ def get_updates_and_outputs(ls):
             # Make sure the updates will be applied in a deterministic order
             if not isinstance(elem, gof.python25.OrderedDict):
                 warnings.warn("Expected OrderedDict or OrderedUpdates, got "\
-                        +str(type(elem))+". This can make your script non-"
+                        + str(type(elem)) + ". This can make your script non-"
                         "deterministic.")
             return True
         # Dictionaries can be given as lists of tuples
@@ -253,7 +251,6 @@ def get_updates_and_outputs(ls):
                 'values, you can use `tensor.constant` to turn them into '
                  'Theano variables.')
 
-
     if is_outputs(ls):
         return None, _list(ls), OrderedDict()
     if is_updates(ls):
@@ -389,7 +386,7 @@ def equal_computations(xs, ys, in_xs=None, in_ys=None):
             elif (isinstance(dx, tensor.Constant) and
                   isinstance(dy, tensor.Constant)):
                 if not (numpy.all(dx.data == dy.data) and
-                        dx.dtype == dy.dtype and
+                        dx.type.dtype == dy.type.dtype and
                         dx.data.shape == dy.data.shape):
                     return False
                 else:
@@ -413,7 +410,7 @@ def equal_computations(xs, ys, in_xs=None, in_ys=None):
                         if (isinstance(dx, tensor.Constant) and
                             isinstance(dy, tensor.Constant)):
                             if not (numpy.all(dx.data == dy.data) and
-                                dx.dtype == dy.dtype and
+                                dx.type.dtype == dy.type.dtype and
                                 dx.data.shape == dy.data.shape):
                                 return False
                             else:

theano/scan_module/tests/test_scan.py

@@ -2346,9 +2346,7 @@ class T_Scan(unittest.TestCase):
         # this new assert is here to test if scan_merging works ..
         nb_scan = len([n for n in topo
             if isinstance(n.op, theano.scan_module.scan_op.Scan)])
-        # For this to work we need an optimization that it will be pushed in
-        # a new pull request
-        self.assertTrue(nb_scan == 2)
+        self.assertTrue(nb_scan == 1)
         nb_shape_i = len([n for n in topo
             if isinstance(n.op, theano.tensor.opt.Shape_i)])
         if theano.config.mode != 'FAST_COMPILE':
@@ -2364,7 +2362,8 @@ class T_Scan(unittest.TestCase):
         sx, upx = theano.scan(sum, sequences=[x])
         sy, upy = theano.scan(sum, sequences=[y])
 
-        f = theano.function([x, y], [sx, sy], mode=mode_with_opt)
+        f = theano.function([x, y], [sx, sy],
+                            mode=mode_with_opt.excluding('scanOp_pushout_seqs_ops'))
         topo = f.maker.fgraph.toposort()
         scans = filter(lambda n: isinstance(
             n.op, theano.scan_module.scan_op.Scan), topo)
@@ -2373,7 +2372,8 @@ class T_Scan(unittest.TestCase):
         sx, upx = theano.scan(sum, sequences=[x], n_steps=2)
         sy, upy = theano.scan(sum, sequences=[y], n_steps=3)
 
-        f = theano.function([x, y], [sx, sy], mode=mode_with_opt)
+        f = theano.function([x, y], [sx, sy],
+                            mode=mode_with_opt.excluding('scanOp_pushout_seqs_ops'))
         topo = f.maker.fgraph.toposort()
         scans = filter(lambda n: isinstance(
             n.op, theano.scan_module.scan_op.Scan), topo)
@@ -2382,7 +2382,8 @@ class T_Scan(unittest.TestCase):
         sx, upx = theano.scan(sum, sequences=[x], n_steps=4)
         sy, upy = theano.scan(sum, sequences=[y], n_steps=4)
 
-        f = theano.function([x, y], [sx, sy], mode=mode_with_opt)
+        f = theano.function([x, y], [sx, sy],
+                            mode=mode_with_opt.excluding('scanOp_pushout_seqs_ops'))
         topo = f.maker.fgraph.toposort()
         scans = filter(lambda n: isinstance(
             n.op, theano.scan_module.scan_op.Scan), topo)
@@ -2391,7 +2392,8 @@ class T_Scan(unittest.TestCase):
         sx, upx = theano.scan(sum, sequences=[x])
         sy, upy = theano.scan(sum, sequences=[x])
 
-        f = theano.function([x], [sx, sy], mode=mode_with_opt)
+        f = theano.function([x], [sx, sy],
+                            mode=mode_with_opt.excluding('scanOp_pushout_seqs_ops'))
         topo = f.maker.fgraph.toposort()
         scans = filter(lambda n:
                        isinstance(n.op, theano.scan_module.scan_op.Scan), topo)
@@ -2401,7 +2403,7 @@ class T_Scan(unittest.TestCase):
         sy, upy = theano.scan(sum, sequences=[x], mode='FAST_COMPILE')
 
         f = theano.function([x], [sx, sy],
-                            mode=mode_with_opt)
+                            mode=mode_with_opt.excluding('scanOp_pushout_seqs_ops'))
         topo = f.maker.fgraph.toposort()
         scans = filter(lambda n:
                        isinstance(n.op, theano.scan_module.scan_op.Scan), topo)
@@ -2410,7 +2412,8 @@ class T_Scan(unittest.TestCase):
         sx, upx = theano.scan(sum, sequences=[x])
         sy, upy = theano.scan(sum, sequences=[x], truncate_gradient=1)
 
-        f = theano.function([x], [sx, sy], mode=mode_with_opt)
+        f = theano.function([x], [sx, sy],
+                            mode=mode_with_opt.excluding('scanOp_pushout_seqs_ops'))
         topo = f.maker.fgraph.toposort()
         scans = filter(lambda n:
                        isinstance(n.op, theano.scan_module.scan_op.Scan), topo)
@@ -2820,12 +2823,12 @@ class T_Scan(unittest.TestCase):
         vx = numpy.zeros((50,), dtype=theano.config.floatX)
         vx[23] = 4
         out, out2 = f(vx)
-        print 'len_out', len(out)
         assert len(out) == 24
         assert numpy.all(out2 == vx + 2)
         lssc = [x for x in f.maker.fgraph.toposort()
                 if isinstance(x.op, theano.scan_module.scan_op.Scan)]
-        assert len(lssc) == 2
+        # One scan node gets optimnized out
+        assert len(lssc) == 1
 
     @dec.knownfailureif(True,
                         ("This test fails because not typed outputs_info "
@@ -3303,6 +3306,70 @@ class T_Scan(unittest.TestCase):
                                     theano.scan_module.scan_op.Scan)]
         assert len(scan_nodes) == 1
 
+    def test_eliminate_seqs(self):
+        U = tensor.vector('U')
+        sh = theano.shared(asarrayX(2.))
+        x1 = tensor.vector('x1')
+        x2 = tensor.scalar('x2')
+
+        def rec_fn(*args):
+            u_t = args[0]
+            return [(u_t + 1,  # mitsot
+                     u_t + 2,  # sitsot
+                     u_t + 3),  # nitsot
+                    {sh: u_t + 4}]  # shared
+
+        [X1, X2, X3], updates = theano.scan(
+            rec_fn,
+            U,
+            [dict(initial=x1, taps=[-1, -3]), x2, None],
+            n_steps=None,
+            truncate_gradient=-1,
+            go_backwards=False)
+        f = theano.function([U, x1, x2], [X1, X2, X3],
+                            updates=updates,
+                            mode=theano.Mode(linker='py'),
+                            allow_input_downcast=True)
+        rng = numpy.random.RandomState(utt.fetch_seed())
+        v_u = asarrayX(rng.uniform(size=(5,)))
+        outs = f(v_u, [0, 0, 0], 0)
+        assert numpy.allclose(outs[0], v_u + 1)
+        assert numpy.allclose(outs[1], v_u + 2)
+        assert numpy.allclose(outs[2], v_u + 3)
+        assert numpy.allclose(sh.get_value(), v_u[-1] + 4)
+
+    def test_eliminate_nonseqs(self):
+        W = tensor.scalar('W')
+        sh = theano.shared(asarrayX(2.))
+        x1 = tensor.vector('x1')
+        x2 = tensor.scalar('x2')
+
+        def rec_fn(*args):
+            w = args[-1]
+            return [(w + 1.,  # mitsot
+                     w + 2.,  # sitsot
+                     w + 3.),  # nitsot
+                    {sh: w + 4.}]  # shared
+
+        [X1, X2, X3], updates = theano.scan(
+            rec_fn,
+            [],
+            [dict(initial=x1, taps=[-1, -3]), x2, None],
+            W,
+            n_steps=5,
+            truncate_gradient=-1,
+            go_backwards=False)
+        f = theano.function([W, x1, x2], [X1, X2, X3],
+                            updates=updates,
+                            mode=theano.Mode(linker='py'),
+                            allow_input_downcast=True)
+        rng = numpy.random.RandomState(utt.fetch_seed())
+        v_w = asarrayX(rng.uniform())
+        outs = f(v_w, [0, 0, 0], 0)
+        assert numpy.allclose(outs[0], v_w + 1)
+        assert numpy.allclose(outs[1], v_w + 2)
+        assert numpy.allclose(outs[2], v_w + 3)
+        assert numpy.allclose(sh.get_value(), v_w + 4)
 
 
 def test_speed():