Merge pull request #3936 from nouiz/32bit

theano/tensor/extra_ops.py

@@ -504,18 +504,6 @@ def bincount(x, weights=None, minlength=None, assert_nonneg=False):
     .. versionadded:: 0.6
 
     """
-    compatible_type = ('int8', 'int16', 'int32', 'int64',
-                       'uint8', 'uint16', 'uint32')
-    unsupported_dtypes = ('uint64',)
-
-    if x.dtype in unsupported_dtypes:
-            raise TypeError(
-                ("Input dtype %s is not supported, "
-                 % unsupported_dtypes), x.dtype)
-
-    if x.dtype not in compatible_type:
-        raise TypeError("Inputs dtype must be an integer.")
-
     if x.ndim != 1:
         raise TypeError("Inputs must be of dimension 1.")
 
@@ -738,6 +726,9 @@ def repeat(x, repeats, axis=None):
         if repeats.ndim == 1:
             repeats = repeats[0]
 
+        if x.dtype == 'uint64':
+            raise TypeError("theano.tensor.repeat don't support dtype uint64")
+
         if axis is None:
             axis = 0
             x = x.flatten()

theano/tensor/signal/tests/test_pool.py

@@ -663,9 +663,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
                 output = pool_2d(images, maxpoolshp, ignore_border,
                                  mode=mode)
                 output_val = function([images], output)(imval)
-                assert numpy.all(output_val == numpy_output_val), (
-                    "output_val is %s, numpy_output_val is %s"
-                    % (output_val, numpy_output_val))
+                utt.assert_allclose(output_val, numpy_output_val)
 
                 def mp(input):
                     return pool_2d(input, maxpoolshp, ignore_border,
@@ -686,12 +684,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
         patch_size = (2, 2)
         op = max_pool_2d_same_size(input, patch_size)
         op_output = function([input], op)(test_input_array)
-        assert numpy.all(op_output == test_answer_array), (
-            "op_output is %s, test_answer_array is %s" % (
-
-                op_output, test_answer_array
-            )
-        )
+        utt.assert_allclose(op_output, test_answer_array)
 
         def mp(input):
             return max_pool_2d_same_size(input, patch_size)
@@ -716,9 +709,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
                 output = pool_2d(images, maxpoolshp, ignore_border,
                                  mode=mode)
                 output_val = function([images], output)(imval)
-                assert numpy.all(output_val == numpy_output_val), (
-                    "output_val is %s, numpy_output_val is %s"
-                    % (output_val, numpy_output_val))
+                utt.assert_allclose(output_val, numpy_output_val)
 
 # removed as already tested in test_max_pool_2d_2D
 # This make test in debug mode too slow.
@@ -745,7 +736,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
                 output = pool_2d(images, maxpoolshp, ignore_border,
                                  mode=mode)
                 output_val = function([images], output)(imval)
-                assert numpy.all(output_val == numpy_output_val)
+                utt.assert_allclose(output_val, numpy_output_val)
 
 # removed as already tested in test_max_pool_2d_2D
 # This make test in debug mode too slow.

theano/tensor/subtensor.py

@@ -1779,7 +1779,8 @@ class AdvancedSubtensor1(Op):
             if (i_type != NPY_INTP) {
                 // Cast %(i_name)s to NPY_INTP (expected by PyArray_TakeFrom),
                 // if all values fit.
-                if (!PyArray_CanCastSafely(i_type, NPY_INTP)) {
+                if (!PyArray_CanCastSafely(i_type, NPY_INTP) &&
+                    PyArray_SIZE(%(i_name)s) > 0) {
                     npy_int64 min_val, max_val;
                     PyObject* py_min_val = PyArray_Min(%(i_name)s, NPY_MAXDIMS,
                                                        NULL);
@@ -1850,7 +1851,7 @@ class AdvancedSubtensor1(Op):
         """ % locals()
 
     def c_code_cache_version(self):
-        return (0, 1, 1)
+        return (0, 1, 2)
 
 advanced_subtensor1 = AdvancedSubtensor1()
 

theano/tensor/tests/test_basic.py

@@ -6672,10 +6672,11 @@ class test_arithmetic_cast(unittest.TestCase):
 
 class T_long_tensor(unittest.TestCase):
     def test_fit_int64(self):
-        for exp in xrange(64):
+        for exp in xrange(gof.python_int_bitwidth()):
             val = L(2 ** exp - 1)
             scalar_ct = constant(val)
-            assert scalar_ct.dtype.startswith('int')
+
+            assert scalar_ct.dtype.startswith('int'), (exp, val, scalar_ct.dtype)
             assert scalar_ct.value == val
 
             vector_ct = constant([val, val])

theano/tensor/tests/test_extra_ops.py

@@ -134,35 +134,35 @@ class TestBinCountOp(utt.InferShapeTester):
 
     def test_bincountFn(self):
         w = T.vector('w')
+        def ref(data, w=None, minlength=None):
+            size = data.max() + 1
+            if minlength:
+                size = max(size, minlength)
+            if w is not None:
+                out = np.zeros(size, dtype=w.dtype)
+                for i in range(data.shape[0]):
+                    out[data[i]] += w[i]
+            else:
+                out = np.zeros(size, dtype=a.dtype)
+                for i in range(data.shape[0]):
+                    out[data[i]] += 1
+            return out
         for dtype in ('int8', 'int16', 'int32', 'int64',
                       'uint8', 'uint16', 'uint32', 'uint64'):
             x = T.vector('x', dtype=dtype)
 
-            # uint64 always fails
-            # int64 and uint32 also fail if python int are 32-bit
-            int_bitwidth = theano.gof.python_int_bitwidth()
-            if int_bitwidth == 64:
-                numpy_unsupported_dtypes = ('uint64',)
-            if int_bitwidth == 32:
-                numpy_unsupported_dtypes = ('uint32', 'int64', 'uint64')
-            # uint64 always fails
-            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))
             f2 = theano.function([x, w], bincount(x, weights=w))
 
-                assert (np.bincount(a) == f1(a)).all()
-                assert np.allclose(np.bincount(a, weights=weights),
-                                   f2(a, weights))
-                f3 = theano.function([x], bincount(x, minlength=23))
+            assert (ref(a) == f1(a)).all()
+            assert np.allclose(ref(a, weights), f2(a, weights))
+            f3 = theano.function([x], bincount(x, minlength=55))
             f4 = theano.function([x], bincount(x, minlength=5))
-                assert (np.bincount(a, minlength=23) == f3(a)).all()
-                assert (np.bincount(a, minlength=5) == f4(a)).all()
+            assert (ref(a, minlength=55) == f3(a)).all()
+            assert (ref(a, minlength=5) == f4(a)).all()
             # skip the following test when using unsigned ints
             if not dtype.startswith('u'):
                 a[0] = -1
@@ -423,7 +423,8 @@ class TestRepeatOp(utt.InferShapeTester):
                 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:
+                    if (dtype == 'uint64' or
+                        (dtype in self.numpy_unsupported_dtypes and r_var.ndim == 1)):
                         self.assertRaises(TypeError,
                                 repeat, x, r_var, axis=axis)
                     else:
@@ -440,6 +441,10 @@ class TestRepeatOp(utt.InferShapeTester):
                             r = np.random.random_integers(
                                     5, size=(10,)).astype(dtype)
 
+                        if dtype in self.numpy_unsupported_dtypes and r_var.ndim == 1:
+                            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),
@@ -477,6 +482,7 @@ class TestRepeatOp(utt.InferShapeTester):
                     r_var = T.scalar(dtype=dtype)
                     r = numpy.asarray(3, dtype=dtype)
                     if dtype in self.numpy_unsupported_dtypes:
+                        r_var = T.vector(dtype=dtype)
                         self.assertRaises(TypeError, repeat, x, r_var)
                     else:
                         self._compile_and_check(

theano/tests/test_printing.py

@@ -514,8 +514,8 @@ def test_scan_debugprint4():
     def fn(a_m2, a_m1, b_m2, b_m1):
         return a_m1 + a_m2, b_m1 + b_m2
 
-    a0 = theano.shared(numpy.arange(2))
-    b0 = theano.shared(numpy.arange(2))
+    a0 = theano.shared(numpy.arange(2, dtype='int64'))
+    b0 = theano.shared(numpy.arange(2, dtype='int64'))
 
     (a, b), _ = theano.scan(
         fn, outputs_info=[{'initial': a0, 'taps': [-2, -1]},