Merge pull request #840 from lamblin/travis

theano/scan_module/tests/test_scan.py

@@ -841,7 +841,7 @@ class T_Scan(unittest.TestCase):
         (theano_x0, theano_x1) = f9(vu0, vu1, vu2, vx0, vx1)
         # assert that theano does what it should
         assert numpy.allclose(theano_x0, numpy_x0)
-        assert numpy.allclose(theano_x1, numpy_x1)
+        assert numpy.allclose(theano_x1, numpy_x1), (theano_x1, numpy_x1, theano_x1 - numpy_x1)
         # assert that it was done in place
 
         # !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

theano/tensor/extra_ops.py

@@ -3,7 +3,7 @@ import numpy
 
 import theano
 import basic
-from theano import gof, tensor, function, scalar
+from theano import gof, tensor, scalar
 from theano.sandbox.linalg.ops import diag
 
 
@@ -132,6 +132,21 @@ class BinCountOp(theano.Op):
 
         if x.dtype not in BinCountOp.compatible_type:
             raise TypeError("Inputs dtype must be an integer.")
+
+        # Some dtypes are not supported by numpy's implementation of bincount.
+        # Until another one is available, we should fail at graph construction
+        # time, not wait for execution.
+        int_bitwidth = theano.gof.cmodule.python_int_bitwidth()
+        if int_bitwidth == 64:
+            numpy_unsupported_dtypes = ('uint64',)
+        if int_bitwidth == 32:
+            numpy_unsupported_dtypes = ('uint32', 'int64', 'uint64')
+
+        if x.dtype in numpy_unsupported_dtypes:
+            raise TypeError(
+                    ("Input dtypes %s are not supported by numpy.bincount, "
+                    % numpy_unsupported_dtypes), x.dtype)
+
         if x.ndim != 1:
             raise TypeError("Inputs must be of dimension 1.")
 
@@ -286,6 +301,25 @@ class RepeatOp(theano.Op):
     def make_node(self, x, repeats):
         x = basic.as_tensor_variable(x)
         repeats = basic.as_tensor_variable(repeats)
+
+        if repeats.dtype not in tensor.discrete_dtypes:
+            raise TypeError("repeats.dtype must be an integer.")
+
+        # Some dtypes are not supported by numpy's implementation of repeat.
+        # Until another one is available, we should fail at graph construction
+        # time, not wait for execution.
+        int_bitwidth = theano.gof.cmodule.python_int_bitwidth()
+        if int_bitwidth == 64:
+            numpy_unsupported_dtypes = ('uint64',)
+        if int_bitwidth == 32:
+            numpy_unsupported_dtypes = ('uint32', 'int64', 'uint64')
+
+        if repeats.dtype in numpy_unsupported_dtypes:
+            raise TypeError(
+                    ("dtypes %s are not supported by numpy.repeat "
+                     "for the 'repeats' parameter, "
+                     % numpy_unsupported_dtypes), repeats.dtype)
+
         if self.axis is None:
             out_type = theano.tensor.TensorType(dtype=x.dtype,
                                                 broadcastable=[False])

theano/tensor/opt.py

@@ -970,7 +970,7 @@ class ShapeFeature(object):
                 # but this works with `local_useless_subtensor`, so for now we
                 # keep it this way. See #266 for a better long-term fix.
                 if getattr(d, 'dtype', 'int64') != 'int64':
-                    assert d.dtype in theano.tensor.int_dtypes
+                    assert d.dtype in theano.tensor.discrete_dtypes, d.dtype
                     new_shape += sh[len(new_shape):i + 1]
                     new_shape[i] = theano.tensor.cast(d, 'int64')
             if new_shape:

theano/tensor/tests/test_extra_ops.py

@@ -3,9 +3,11 @@ import numpy
 
 import theano
 from theano.tests import unittest_tools as utt
-from theano.tensor.extra_ops import *
+from theano.tensor.extra_ops import (BinCountOp, bincount, DiffOp, diff,
+        SqueezeOp, squeeze, RepeatOp, repeat, Bartlett, bartlett,
+        FillDiagonal, fill_diagonal)
 from theano import tensor as T
-from theano import config, tensor, function, scalar
+from theano import config, tensor, function
 
 
 class TestBinCountOp(utt.InferShapeTester):
@@ -15,9 +17,24 @@ class TestBinCountOp(utt.InferShapeTester):
         self.op = BinCountOp()
 
     def test_bincountOp(self):
-        x = T.lvector('x')
         w = T.vector('w')
-        a = np.random.random_integers(50, size=(25))
+        for dtype in ('int8', 'int16', 'int32', 'int64',
+                      'uint8', 'uint16', 'uint32', 'uint64'):
+            # uint64 always fails
+            # int64 and uint32 also fail if python int are 32-bit
+            int_bitwidth = theano.gof.cmodule.python_int_bitwidth()
+            if int_bitwidth == 64:
+                numpy_unsupported_dtypes = ('uint64',)
+            if int_bitwidth == 32:
+                numpy_unsupported_dtypes = ('uint32', 'int64', 'uint64')
+
+            x = T.vector('x', dtype=dtype)
+
+            if dtype in numpy_unsupported_dtypes:
+                self.assertRaises(TypeError, bincount, x)
+
+            else:
+                a = np.random.random_integers(50, size=(25)).astype(dtype)
                 weights = np.random.random((25,)).astype(config.floatX)
 
                 f1 = theano.function([x], bincount(x))
@@ -26,32 +43,54 @@ class TestBinCountOp(utt.InferShapeTester):
                 f4 = theano.function([x], bincount(x, minlength=5))
 
                 assert (np.bincount(a) == f1(a)).all()
-        assert np.allclose(np.bincount(a, weights=weights), f2(a, weights))
+                assert np.allclose(np.bincount(a, weights=weights),
+                                   f2(a, weights))
                 assert (np.bincount(a, minlength=23) == f3(a)).all()
-        assert (np.bincount(a, minlength=5) == f3(a)).all()
+                assert (np.bincount(a, minlength=5) == f4(a)).all()
 
     def test_infer_shape(self):
-        x = T.lvector('x')
+        for dtype in tensor.discrete_dtypes:
+            # uint64 always fails
+            # int64 and uint32 also fail if python int are 32-bit
+            int_bitwidth = theano.gof.cmodule.python_int_bitwidth()
+            if int_bitwidth == 64:
+                numpy_unsupported_dtypes = ('uint64',)
+            if int_bitwidth == 32:
+                numpy_unsupported_dtypes = ('uint32', 'int64', 'uint64')
 
-        self._compile_and_check([x],
+            x = T.vector('x', dtype=dtype)
+
+            if dtype in numpy_unsupported_dtypes:
+                self.assertRaises(TypeError, bincount, x)
+
+            else:
+                self._compile_and_check(
+                        [x],
                         [bincount(x)],
-                                [np.random.random_integers(50, size=(25,))],
+                        [np.random.random_integers(
+                            50, size=(25,)).astype(dtype)],
                         self.op_class)
 
                 weights = np.random.random((25,)).astype(config.floatX)
-        self._compile_and_check([x],
+                self._compile_and_check(
+                        [x],
                         [bincount(x, weights=weights)],
-                                [np.random.random_integers(50, size=(25,))],
+                        [np.random.random_integers(
+                            50, size=(25,)).astype(dtype)],
                         self.op_class)
 
-        self._compile_and_check([x],
+                self._compile_and_check(
+                        [x],
                         [bincount(x, minlength=60)],
-                                [np.random.random_integers(50, size=(25,))],
+                        [np.random.random_integers(
+                            50, size=(25,)).astype(dtype)],
                         self.op_class)
 
-        self._compile_and_check([x],
+                self._compile_and_check(
+                        [x],
                         [bincount(x, minlength=5)],
-                                [np.random.random_integers(50, size=(25,))],
+                        [np.random.random_integers(
+                            50, size=(25,)).astype(dtype)],
                         self.op_class)
 
 
@@ -95,11 +134,11 @@ class TestDiffOp(utt.InferShapeTester):
         x = T.vector('x')
         a = np.random.random(50).astype(config.floatX)
 
-        gf = theano.function([x], T.grad(T.sum(diff(x)), x))
+        theano.function([x], T.grad(T.sum(diff(x)), x))
         utt.verify_grad(self.op, [a])
 
         for k in range(TestDiffOp.nb):
-            dg = theano.function([x], T.grad(T.sum(diff(x, n=k)), x))
+            theano.function([x], T.grad(T.sum(diff(x, n=k)), x))
             utt.verify_grad(DiffOp(n=k), [a], eps=7e-3)
 
 
@@ -132,7 +171,7 @@ class TestSqueezeOp(utt.InferShapeTester):
         x = T.tensor4('x')
         a = np.random.random((1, 1, 3, 4)).astype(config.floatX)
 
-        gf = theano.function([x], T.grad(T.sum(squeeze(x, out_nd=1)), x))
+        theano.function([x], T.grad(T.sum(squeeze(x, out_nd=1)), x))
         utt.verify_grad(SqueezeOp(out_nd=2), [a])
 
 
@@ -144,6 +183,13 @@ class TestRepeatOp(utt.InferShapeTester):
         super(TestRepeatOp, self).setUp()
         self.op_class = RepeatOp
         self.op = RepeatOp()
+        # uint64 always fails
+        # int64 and uint32 also fail if python int are 32-bit
+        int_bitwidth = theano.gof.cmodule.python_int_bitwidth()
+        if int_bitwidth == 64:
+            self.numpy_unsupported_dtypes = ('uint64',)
+        if int_bitwidth == 32:
+            self.numpy_unsupported_dtypes = ('uint32', 'int64', 'uint64')
 
     def test_repeatOp(self):
         for ndim in range(3):
@@ -151,19 +197,30 @@ class TestRepeatOp(utt.InferShapeTester):
             a = np.random.random((10, ) * ndim).astype(config.floatX)
 
             for axis in self._possible_axis(ndim):
-                r_var = T.lscalar()
-                r = 3
-                f = theano.function([x, r_var], repeat(x, r_var, axis=axis))
-                assert np.allclose(np.repeat(a, r, axis=axis), f(a, r))
+                for dtype in tensor.discrete_dtypes:
+                    r_var = T.scalar(dtype=dtype)
+                    r = numpy.asarray(3, dtype=dtype)
+                    if dtype in self.numpy_unsupported_dtypes:
+                        self.assertRaises(TypeError,
+                                repeat, x, r_var, axis=axis)
+                    else:
+                        f = theano.function([x, r_var],
+                                            repeat(x, r_var, axis=axis))
+                        assert np.allclose(np.repeat(a, r, axis=axis),
+                                           f(a, r))
 
-                r_var = T.lvector()
+                        r_var = T.vector(dtype=dtype)
                         if axis is None:
-                    r = np.random.random_integers(5, size=a.size)
+                            r = np.random.random_integers(
+                                    5, size=a.size).astype(dtype)
                         else:
-                    r = np.random.random_integers(5, size=(10,))
+                            r = np.random.random_integers(
+                                    5, size=(10,)).astype(dtype)
 
-                f = theano.function([x, r_var], repeat(x, r_var, axis=axis))
-                assert np.allclose(np.repeat(a, r, axis=axis), f(a, r))
+                        f = theano.function([x, r_var],
+                                            repeat(x, r_var, axis=axis))
+                        assert np.allclose(np.repeat(a, r, axis=axis),
+                                           f(a, r))
 
     def test_infer_shape(self):
         for ndim in range(4):
@@ -171,20 +228,28 @@ class TestRepeatOp(utt.InferShapeTester):
             a = np.random.random((10, ) * ndim).astype(config.floatX)
 
             for axis in self._possible_axis(ndim):
-                r_var = T.lscalar()
-                r = 3
-                self._compile_and_check([x, r_var],
+                for dtype in tensor.discrete_dtypes:
+                    r_var = T.scalar(dtype=dtype)
+                    r = numpy.asarray(3, dtype=dtype)
+                    if dtype in self.numpy_unsupported_dtypes:
+                        self.assertRaises(TypeError, repeat, x, r_var)
+                    else:
+                        self._compile_and_check(
+                                [x, r_var],
                                 [RepeatOp(axis=axis)(x, r_var)],
                                 [a, r],
                                 self.op_class)
 
-                r_var = T.lvector()
+                        r_var = T.vector(dtype=dtype)
                         if axis is None:
-                    r = np.random.random_integers(5, size=a.size)
+                            r = np.random.random_integers(
+                                    5, size=a.size).astype(dtype)
                         else:
-                    r = np.random.random_integers(5, size=(10,))
+                            r = np.random.random_integers(
+                                    5, size=(10,)).astype(dtype)
 
-                self._compile_and_check([x, r_var],
+                        self._compile_and_check(
+                                [x, r_var],
                                 [RepeatOp(axis=axis)(x, r_var)],
                                 [a, r],
                                 self.op_class)