Merge pull request #46 from jaberg/curand

theano/sandbox/cuda/__init__.py

@@ -170,6 +170,7 @@ if cuda_available:
     from basic_ops import host_from_gpu, gpu_from_host, as_cuda_array
     import opt
     import cuda_ndarray
+    from rng_curand import CURAND_RandomStreams
 
 
 def use(device,

theano/sandbox/cuda/tests/test_rng_curand.py

+import numpy
+import theano
+from theano.sandbox.cuda.rng_curand import CURAND_RandomStreams
+from theano.sandbox.rng_mrg import MRG_RandomStreams
+
+def test_uniform_basic():
+    rng = CURAND_RandomStreams(234)
+
+    u0 = rng.uniform((10,10))
+    u1 = rng.uniform((10,10))
+
+    f0 = theano.function([], u0)
+    f1 = theano.function([], u1)
+
+    v0list = [f0() for i in range(3)]
+    v1list = [f1() for i in range(3)]
+
+    #print v0list
+    #print v1list
+    # assert that elements are different in a few ways
+    assert numpy.all(v0list[0] != v0list[1])
+    assert numpy.all(v1list[0] != v1list[1])
+    assert numpy.all(v0list[0] != v1list[0])
+
+    for v in v0list:
+        assert v.shape == (10,10)
+        assert v.min() >= 0
+        assert v.max() <= 1
+        assert v.min() < v.max()
+        assert .25 <= v.mean() <= .75
+
+
+def test_normal_basic():
+    rng = CURAND_RandomStreams(234)
+
+    u0 = rng.normal((10,10))
+    u1 = rng.normal((10,10))
+
+    f0 = theano.function([], u0)
+    f1 = theano.function([], u1)
+
+    v0list = [f0() for i in range(3)]
+    v1list = [f1() for i in range(3)]
+
+    #print v0list
+    #print v1list
+    # assert that elements are different in a few ways
+    assert numpy.all(v0list[0] != v0list[1])
+    assert numpy.all(v1list[0] != v1list[1])
+    assert numpy.all(v0list[0] != v1list[0])
+
+    for v in v0list:
+        assert v.shape == (10,10)
+        assert v.min() < v.max()
+        assert -.5 <= v.mean() <= .5
+
+
+def compare_speed():
+    # To run this speed comparison
+    # cd <directory of this file>
+    # THEANO_FLAGS=device=gpu \
+    #   python -c 'import test_rng_curand; test_rng_curand.compare_speed()'
+
+    mrg = MRG_RandomStreams()
+    crn = CURAND_RandomStreams(234)
+
+    N=1000*100
+
+    dest = theano.shared(numpy.zeros(N,dtype=theano.config.floatX))
+
+    mrg_u = theano.function([], [], updates={dest:mrg.uniform((N,))},
+            profile='mrg uniform')
+    crn_u = theano.function([], [], updates={dest:crn.uniform((N,))},
+            profile='crn uniform')
+    mrg_n = theano.function([], [], updates={dest:mrg.normal((N,))},
+            profile='mrg normal')
+    crn_n = theano.function([], [], updates={dest:crn.normal((N,))},
+            profile='crn normal')
+
+    for f in mrg_u, crn_u, mrg_n, crn_n:
+        # don't time the first call, it has some startup cost
+        print 'DEBUGPRINT'
+        print '----------'
+        theano.printing.debugprint(f)
+
+    for i in range(100):
+        for f in mrg_u, crn_u, mrg_n, crn_n:
+            # don't time the first call, it has some startup cost
+            f.fn.time_thunks = (i>0)
+            f()
+