Merged

theano/sandbox/rng_mrg.py

@@ -589,6 +589,35 @@ class GPU_mrg_uniform(mrg_uniform_base):
     def c_code_cache_version(self):
         return (4,)
 
+
+def guess_n_streams(size, warn=True):
+    """
+    Return a guess at a good number of streams.
+    
+    :param warn: If True, warn when a guess cannot be made (in which case
+    we return 30 * 256).
+    """
+    # TODO: a smart way of choosing the number of streams, see #612.
+    # Note that this code was moved out of `MRG_RandomStreams` so that it can
+    # be easily accessed from tests, where we want to disable the warning.
+    if (isinstance(size, (tuple, list)) and
+        all([isinstance(i, int) for i in size])):
+        # We can make a guess.
+        r = 1
+        for s in size:
+            r *= s
+        if r > 6:
+            r = r/6 # chosen as fastest for rbm_benchmark
+        return r
+    else:
+        if warn:
+            assert False
+            print >> sys.stderr, (
+                    "MRG_RandomStreams Can't determine #streams from "
+                    "size (%s), guessing 30*256") % str(size)
+        return 30 * 256
+
+
 class MRG_RandomStreams(object):
     """Module component with similar interface to numpy.random (numpy.random.RandomState)"""
 
@@ -654,18 +683,7 @@ class MRG_RandomStreams(object):
         return rval
 
     def n_streams(self, size):
-        # TODO: a smart way of choosing the number of streams, see #612.
-        if isinstance(size, (tuple, list)) and all([isinstance(i,int) for i in size]):
-            r = 1
-            for s in size:
-                r *= s
-            if r > 6:
-                r = r/6 # chosen as fastest for rbm_benchmark
-            return r
-
-        print >> sys.stderr, ("MRG_RandomStreams Can't determine #streams from "
-                "size (%s), guessing 30*256")%str(size)
-        return 30*256
+        return guess_n_streams(size, warn=True)
 
     def pretty_return(self, node_rstate, new_rstate, sample):
         sample.rstate = node_rstate
@@ -674,7 +692,8 @@ class MRG_RandomStreams(object):
         node_rstate.default_update = new_rstate
         return sample
 
-    def uniform(self, size=None, low=0.0, high=1.0, ndim=None, dtype=config.floatX, nstreams=None):
+    def uniform(self, size, low=0.0, high=1.0, ndim=None, dtype='floatX',
+                nstreams=None):
         """
         Sample a tensor of given size whose element from a uniform
         distribution between low and high.
@@ -683,10 +702,14 @@ class MRG_RandomStreams(object):
         ndim may be a plain integer to supplement the missing
         information.
 
-        :param: size: Can be a list of integer or Theano variable
+        :param size: Can be a list of integer or Theano variable
                 (ex: the shape of other Theano Variable)
-                TODO: can size be None?
+
+        :param dtype: The output data type.
         """
+        if dtype == 'floatX':
+            dtype = config.floatX
+
         if isinstance(size, tuple):
             msg = "size must be a tuple of int or a Theano variable"
             assert all([isinstance(i,int) or isinstance(i,Variable)
@@ -728,16 +751,19 @@ class MRG_RandomStreams(object):
             raise NotImplementedError( 'Increase the size to match the broadcasting pattern of `low` and `high` arguments')
         return  r
 
-    def binomial(self, size=None, n=1, p=0.5, ndim=None, dtype='int64'):
+    def binomial(self, size=None, n=1, p=0.5, ndim=None, dtype='int64',
+                 nstreams=None):
         if n == 1:
-            if dtype=='float32' and self.use_cuda:
-                return cast(self.uniform(size=size, dtype=dtype) < p, dtype)
+            if dtype == 'float32' and self.use_cuda:
+                x = self.uniform(size=size, dtype=dtype, nstreams=nstreams)
             else:
-                return cast(self.uniform(size=size) < p, dtype)
+                x = self.uniform(size=size, nstreams=nstreams)
+            return cast(x < p, dtype)
         else:
             raise NotImplementedError("MRG_RandomStreams.binomial with n > 1")
 
-    def multinomial(self, size=None, n=1, pvals=None, ndim=None, dtype='int64'):
+    def multinomial(self, size=None, n=1, pvals=None, ndim=None, dtype='int64',
+                    nstreams=None):
         """
         Sample `n` (currently `n` needs to be 1) times from a multinomial
         distribution defined by probabilities pvals.
@@ -758,22 +784,31 @@ class MRG_RandomStreams(object):
                     ndim, size, pvals[:,0])
             assert ndim==1
             bcast = bcast+(pvals.type.broadcastable[-1],)
-            unis = self.uniform(size=size, ndim=1)
+            unis = self.uniform(size=size, ndim=1, nstreams=nstreams)
             op = multinomial.MultinomialFromUniform(dtype)
             return op(pvals, unis)
         else:
             raise NotImplementedError(("MRG_RandomStreams.multinomial only"
                 " implemented with n == 1 and pvals.ndim = 2"))
 
-    def normal(self, size=None, avg=0.0, std=1.0, ndim=None, dtype=config.floatX):
+    def normal(self, size=None, avg=0.0, std=1.0, ndim=None,
+               dtype='floatX', nstreams=None):
         """
-        :param: size: Can be a list of integer or Theano variable(ex: the shape of other Theano Variable)
+        :param size: Can be a list of integers or Theano variables (ex: the
+        shape of another Theano Variable)
+
+        :param dtype: The output data type.
+
+        :param nstreams: Number of streams.
         """
         # We need an even number of ]0,1[ samples. Then we split them
         # in two halves. First half becomes our U1's for Box-Muller,
         # second half our U2's. See Wikipedia page:
         # http://en.wikipedia.org/wiki/Box%E2%80%93Muller_transform
 
+        if dtype == 'floatX':
+            dtype = config.floatX
+
         evened = False
         constant = False
         if isinstance(size, tuple) and all([isinstance(i,int) for i in size]):
@@ -786,7 +821,8 @@ class MRG_RandomStreams(object):
         else:
             #if even, don't change, if odd, +1
             n_samples = prod(size)+(prod(size)%2)
-        flattened = self.uniform(size=(n_samples,), dtype=dtype)
+        flattened = self.uniform(size=(n_samples,), dtype=dtype,
+                                 nstreams=nstreams)
 
         if constant:
             U1 = flattened[:n_samples // 2]

theano/sandbox/test_rng_mrg.py

@@ -350,7 +350,9 @@ def test_uniform():
         print 'ON CPU with size=(%s):'%str(size)
         x = tensor.matrix()
         R = MRG_RandomStreams(234, use_cuda=False)
-        u = R.uniform(size=size)
+        # Note: we specify `nstreams` to avoid a warning.
+        u = R.uniform(size=size,
+                      nstreams=rng_mrg.guess_n_streams(size, warn=False))
         f = theano.function(var_input, u, mode=mode)
         assert any([isinstance(node.op,theano.sandbox.rng_mrg.mrg_uniform)
                     for node in f.maker.env.toposort()])
@@ -366,7 +368,8 @@ def test_uniform():
             print ''
             print 'ON GPU with size=(%s):'%str(size)
             R = MRG_RandomStreams(234, use_cuda=True)
-            u = R.uniform(size=size, dtype='float32')
+            u = R.uniform(size=size, dtype='float32',
+                          nstreams=rng_mrg.guess_n_streams(size, warn=False))
             assert u.dtype == 'float32' #well, it's really that this test w GPU doesn't make sense otw
             f = theano.function(var_input, theano.Out(
                     theano.sandbox.cuda.basic_ops.gpu_from_host(u),
@@ -421,7 +424,9 @@ def test_binomial():
             print ''
             print 'ON CPU with size=(%s) and mean(%d):'%(str(size),mean)
             R = MRG_RandomStreams(234, use_cuda=False)
-            u = R.binomial(size=size, p=mean)
+            # Note: we specify `nstreams` to avoid a warning.
+            u = R.binomial(size=size, p=mean,
+                           nstreams=rng_mrg.guess_n_streams(size, warn=False))
             f = theano.function(var_input, u, mode=mode)
             theano.printing.debugprint(f)
             out = f(*input)
@@ -433,7 +438,9 @@ def test_binomial():
                 print ''
                 print 'ON GPU with size=(%s) and mean(%d):'%(str(size),mean)
                 R = MRG_RandomStreams(234, use_cuda=True)
-                u = R.binomial(size=size, p=mean, dtype='float32')
+                u = R.binomial(size=size, p=mean, dtype='float32',
+                               nstreams=rng_mrg.guess_n_streams(size,
+                                                                warn=False))
                 assert u.dtype == 'float32' #well, it's really that this test w GPU doesn't make sense otw
                 f = theano.function(var_input, theano.Out(
                         theano.sandbox.cuda.basic_ops.gpu_from_host(u),
@@ -478,7 +485,9 @@ def test_normal0():
         print 'ON CPU:'
 
         R = MRG_RandomStreams(234, use_cuda=False)
-        n = R.normal(size=size, avg=avg, std=std)
+        # Note: we specify `nstreams` to avoid a warning.
+        n = R.normal(size=size, avg=avg, std=std,
+                     nstreams=rng_mrg.guess_n_streams(size, warn=False))
         f = theano.function(var_input, n, mode=mode)
         theano.printing.debugprint(f)
         out  = f(*input)
@@ -491,7 +500,8 @@ def test_normal0():
             print ''
             print 'ON GPU:'
             R = MRG_RandomStreams(234, use_cuda=True)
-            n = R.normal(size=size, avg=avg, std=std, dtype='float32')
+            n = R.normal(size=size, avg=avg, std=std, dtype='float32',
+                         nstreams=rng_mrg.guess_n_streams(size, warn=False))
             assert n.dtype == 'float32' #well, it's really that this test w GPU doesn't make sense otw
             f = theano.function(var_input, theano.Out(
                 theano.sandbox.cuda.basic_ops.gpu_from_host(n),
@@ -557,7 +567,8 @@ def test_multinomial():
     pvals = numpy.asarray(numpy.random.uniform(size=sample_size))
     pvals = numpy.apply_along_axis(lambda row : row/numpy.sum(row), 1, pvals)
     R = MRG_RandomStreams(234, use_cuda=False)
-    m = R.multinomial(pvals=pvals, dtype=config.floatX)
+    # Note: we specify `nstreams` to avoid a warning.
+    m = R.multinomial(pvals=pvals, dtype=config.floatX, nstreams=30 * 256)
     f = theano.function([], m, mode=mode_)
     theano.printing.debugprint(f)
     out = f()

theano/scalar/basic.py

@@ -1034,11 +1034,11 @@ def div_proxy(x, y):
         # division"), we will change the semantics of "/" on integer types in
         # Theano 0.4. Until then, it is forbidden to use "/" on integers.
         raise IntegerDivisionError(
-                "Dividing two integers with '/' is forbidden until Theano v0.4"
-                " is released (where the result will be a floating point "
-                "number). In the meantime, please either use '//' for integer "
-                "division, or cast one of the arguments to a floating point "
-                "type for float division.")
+                "Dividing two integers with '/' is currently forbidden "
+                "to avoid confusion between integer and floating point "
+                "divisions. Please either use '//' for integer division, or "
+                "cast one of the arguments to a floating point type for float "
+                "division.")
     else:
         return true_div(x, y)
 

theano/tensor/basic.py

@@ -2589,11 +2589,11 @@ def div_proxy(x, y):
         as_tensor_variable(y).dtype in discrete_dtypes):
         # See the same in scalar/basic.py
         raise IntegerDivisionError(
-                "Dividing two integer arrays with '/' is forbidden until "
-                "Theano v0.4 is released (where the result will be a floating "
-                "point number). In the meantime, please either use '//' for "
-                "integer division, or cast one of the arguments to a floating "
-                "point type for float division.")
+                "Dividing two integer arrays with '/' is currently forbidden "
+                "to avoid confusion between integer and floating point "
+                "divisions. Please either use '//' for integer division, or "
+                "cast one of the arguments to a floating point type for float "
+                "division.")
     else:
         return true_div(x, y)
 
@@ -3044,8 +3044,11 @@ def setsubtensor(x, y, idx_list, inplace=False):
     print >> sys.stderr, "tensor.setsubtensor is deprecated - please use set_subtensor"
     the_op = IncSubtensor(idx_list, inplace, set_instead_of_inc=True)
     return the_op(x, y, *Subtensor.collapse(idx_list, lambda entry: isinstance(entry, Variable)))
-def incsubtensor(x, y, idx_list, inplace=False):
-    print >> sys.stderr, "tensor.incsubtensor is deprecated - please use inc_subtensor"
+def incsubtensor(x, y, idx_list, inplace=False, show_warning=True):
+    # Note that `show_warning` should only be set to False by tests, in order
+    # to make sure this old code is still working.
+    if show_warning:
+        print >> sys.stderr, "tensor.incsubtensor is deprecated - please use inc_subtensor"
     the_op = IncSubtensor(idx_list, inplace, set_instead_of_inc=False)
     return the_op(x, y, *Subtensor.collapse(idx_list, lambda entry: isinstance(entry, Variable)))
 

theano/tensor/tests/test_basic.py

@@ -458,8 +458,9 @@ _grad_broadcast_div_mod_normal = dict(same_shapes = (rand(2, 3), rand(2, 3)),
 
 div_grad_rtol=None
 if config.floatX=='float32':
-    #We raise the relative tolerence for the grad as their is error in float32
-    #This is probably caused by our way of computing the gradient error.
+    # We raise the relative tolerance for the grad as there can be errors in
+    # float32.
+    # This is probably caused by our way of computing the gradient error.
     div_grad_rtol=0.025
 DivTester = makeBroadcastTester(op = true_div,
                                   expected = lambda x, y: x / y,

theano/tensor/tests/test_incsubtensor.py

@@ -32,7 +32,8 @@ class Test_incsubtensor(unittest.TestCase):
             if do_set:
                 resut = T.setsubtensor(a, increment, [sl1, sl2])
             else:
-                resut = T.incsubtensor(a, increment, [sl1, sl2])
+                resut = T.incsubtensor(a, increment, [sl1, sl2],
+                                       show_warning=False)
 
             f = theano.function([a, increment, sl2_end], resut)
 
@@ -59,7 +60,7 @@ class Test_incsubtensor(unittest.TestCase):
 
         def inc_slice(*s):
             def just_numeric_args(a,b):
-                return T.incsubtensor(a, b, s)
+                return T.incsubtensor(a, b, s, show_warning=False)
             return just_numeric_args
 
         # vector

theano/tensor/tests/test_opt.py

@@ -2490,6 +2490,7 @@ class T_local_sum(unittest.TestCase):
         assert numpy.allclose(f(input),input.sum())
 
 
+        config.warn.sum_sum_bug = False
         f = theano.function([a],a.sum(0).sum(0).sum(0),mode=self.mode)
         assert len(f.maker.env.nodes)==1
         assert numpy.allclose(f(input),input.sum())
@@ -2499,6 +2500,7 @@ class T_local_sum(unittest.TestCase):
         input=numpy.arange(3*3*3, dtype=config.floatX).reshape(3,3,3)
         dims=[(0,0),(1,0),(2,0),(0,1),(1,1),(2,1)]
 
+        config.warn.sum_sum_bug = False
         for d,dd in dims:
             f = theano.function([a],a.sum(d).sum(dd),mode=self.mode)
             assert numpy.allclose(f(input),input.sum(d).sum(dd))
@@ -2544,6 +2546,7 @@ class T_local_sum(unittest.TestCase):
                 assert len(f.maker.env.nodes)==nb_nodes[2]
                 assert f.maker.env.toposort()[-1].op==T.alloc
 
+            config.warn.sum_sum_bug = False
             for d, dd in [(0,0),(1,0),(2,0),(0,1),(1,1),(2,1)]:
                 f = theano.function([a],t_like(a).sum(d).sum(dd),mode=mode)
                 print f.maker.env.toposort()
@@ -2603,6 +2606,8 @@ class T_local_sum_dimshuffle(unittest.TestCase):
         c_val = rng.randn(2,2,2).astype(config.floatX)
         d_val = numpy.asarray(rng.randn(), config.floatX)
 
+        config.warn.sum_sum_bug = False
+        config.warn.sum_div_dimshuffle_bug = False
         for i,s in enumerate(sums):
             print i
             f = theano.function([a,b,c,d], s, mode=self.mode)