Merge pull request #2734 from hojonathanho/tile_symbolic_reps

theano/tensor/basic.py

@@ -4255,18 +4255,21 @@ def tile(x, reps, ndim=None):
     Tile input array `x` according to `reps`. See the docstring of `numpy.tile`
     for details.
 
-    Currently, `reps` must be a constant, x.ndim and len(reps) must be equal
-    and, if specified, 'ndim' must be equal to both.
+    Currently, x.ndim and len(reps) must be equal, and, if specified, 'ndim'
+    must be equal to both.
 
     TODO: expand this.
     """
 
     try:
-        assert python_all([int(i) == i for i in iter(reps)])
-    except (TypeError, AssertionError):
-        raise ValueError("reps argument to tile must be a constant (e.g. "
-                         "tuple, list of integers)")
-    if len(reps) != x.ndim:
+        iter(reps)
+    except TypeError:
+        raise ValueError("reps must be iterable")
+    if not numpy.all([isinstance(r, (int, long)) or
+        (isinstance(r, TensorVariable) and
+            r.dtype in ["int8", "int16", "int32", "int64"]) for r in reps]):
+        raise ValueError("elements of reps must be scalars of integer dtype")
+    elif len(reps) != x.ndim:
         raise ValueError("len(reps) != x.ndim not currently supported")
     elif (ndim is not None) and ndim != x.ndim:
         raise ValueError("if specified, ndim must be equal to both x.ndim and "

theano/tensor/tests/test_basic.py

@@ -4838,31 +4838,41 @@ def test_flatten_outdim_invalid():
 
 
 def test_tile():
-    # Test the one-dimensional case.
+    def run_tile(x, x_, reps, use_symbolic_reps):
+        if use_symbolic_reps:
+            rep_symbols = [iscalar() for _ in range(len(reps))]
+            f = function([x] + rep_symbols, tile(x, rep_symbols))
+            return f(*([x_] + list(reps)))
+        else:
+            f = function([x], tile(x, reps))
+            return f(x_)
+
     rng = numpy.random.RandomState(utt.fetch_seed())
-    x = vector()
-    f = function([x], tile(x, (2,)))
-    x_ = rng.randn(5).astype(config.floatX)
-    assert numpy.all(f(x_) == numpy.tile(x_, (2,)))
-
-    # Test the two-dimensional case.
-    x = matrix()
-    f = function([x], tile(x, (2, 3)))
-    x_ = rng.randn(2, 4).astype(config.floatX)
-    assert numpy.all(f(x_) == numpy.tile(x_, (2, 3)))
-
-    # Test the three-dimensional case.
-    x = tensor3()
-    f = function([x], tile(x, (2, 3, 4)))
-    x_ = rng.randn(2, 4, 3).astype(config.floatX)
-    assert numpy.all(f(x_) == numpy.tile(x_, (2, 3, 4)))
-
-    # Test the four-dimensional case.
-    x = tensor4()
-    f = function([x], tile(x, (2, 3, 4, 6)))
-    x_ = rng.randn(2, 4, 3, 5).astype(config.floatX)
-    assert numpy.all(f(x_) == numpy.tile(x_, (2, 3, 4, 6)))
 
+    for use_symbolic_reps in [False, True]:
+        # Test the one-dimensional case.
+        x = vector()
+        x_ = rng.randn(5).astype(config.floatX)
+        assert numpy.all(run_tile(x, x_, (2,), use_symbolic_reps) ==
+            numpy.tile(x_, (2,)))
+
+        # Test the two-dimensional case.
+        x = matrix()
+        x_ = rng.randn(2, 4).astype(config.floatX)
+        assert numpy.all(run_tile(x, x_, (2, 3), use_symbolic_reps) ==
+            numpy.tile(x_, (2, 3)))
+
+        # Test the three-dimensional case.
+        x = tensor3()
+        x_ = rng.randn(2, 4, 3).astype(config.floatX)
+        assert numpy.all(run_tile(x, x_, (2, 3, 4), use_symbolic_reps) ==
+            numpy.tile(x_, (2, 3, 4)))
+
+        # Test the four-dimensional case.
+        x = tensor4()
+        x_ = rng.randn(2, 4, 3, 5).astype(config.floatX)
+        assert numpy.all(run_tile(x, x_, (2, 3, 4, 6), use_symbolic_reps) ==
+            numpy.tile(x_, (2, 3, 4, 6)))
 
 def test_tile_grad():