New tests, and reference results generated from the original java code

theano/sandbox/samples_MRG31k3p_12_7_5.txt

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theano/sandbox/test_rng_mrg.py

 import sys, time
+
 import numpy
 import theano
+from theano import tensor, config
+from theano.sandbox import rng_mrg
 from theano.sandbox.rng_mrg import MRG_RandomStreams
+from theano.sandbox.cuda import cuda_available, cuda_enabled
+if cuda_available:
+    from theano.sandbox.cuda import float32_shared_constructor
+
+import unittest
+from theano.tests import unittest_tools as utt
+
 #TODO: test gpu
+# Done in test_consistency_GPU_{serial,parallel}
+
 #TODO: test MRG_RandomStreams
+# Partly done in test_consistency_randomstreams
+
 #TODO: test optimizer mrg_random_make_inplace
-#def test_rng_mrg_cpu():
 #TODO: make tests work when no flags gived. Now need: THEANO_FLAGS=device=gpu0,floatX=float32
+# Partly done, in test_consistency_GPU_{serial,parallel}
+
 #TODO: bug fix test_normal0, in normal() fct, n_samples currently need to be numpy.prod(size) not self.n_streams(size)
 
 mode = theano.config.mode
+utt.seed_rng()
+
+## Results generated by Java code using L'Ecuyer et al.'s code, with:
+# main seed: [12345]*6 (default)
+# 12 streams
+# 7 substreams for each stream
+# 5 samples drawn from each substream
+java_samples = numpy.loadtxt('samples_MRG31k3p_12_7_5.txt')
+
+
+def test_deterministic():
+    seed = utt.fetch_seed()
+    sample_size = (10, 20)
+
+    test_use_cuda = [False]
+    if cuda_enabled:
+        test_use_cuda.append(True)
+
+    for use_cuda in test_use_cuda:
+        print 'use_cuda =', use_cuda
+        R = MRG_RandomStreams(seed=seed, use_cuda=use_cuda)
+        u = R.uniform(size=sample_size)
+        f = theano.function([], u)
+
+        fsample1 = f()
+        fsample2 = f()
+        assert not numpy.allclose(fsample1, fsample2)
+
+        R2 = MRG_RandomStreams(seed=seed, use_cuda=use_cuda)
+        u2 = R2.uniform(size=sample_size)
+        g = theano.function([], u2)
+        gsample1 = g()
+        gsample2 = g()
+        assert numpy.allclose(fsample1, gsample1)
+        assert numpy.allclose(fsample2, gsample2)
+
+
+def test_consistency_randomstreams():
+    '''Verify that the random numbers generated by MRG_RandomStreams
+    are the same as the reference (Java) implementation by L'Ecuyer et al.
+    '''
+
+    seed = 12345
+    n_samples = 5
+    n_streams = 12
+    n_substreams = 7
+
+    test_use_cuda = [False]
+    if cuda_enabled:
+        test_use_cuda.append(True)
+
+    for use_cuda in test_use_cuda:
+        print 'use_cuda =', use_cuda
+        samples = []
+        rng = MRG_RandomStreams(seed = seed, use_cuda=False)
+        for i in range(n_streams):
+            stream_samples = []
+            u = rng.uniform(size=(n_substreams,), nstreams=n_substreams)
+            f = theano.function([], u)
+            for j in range(n_samples):
+                s = f()
+                stream_samples.append(s)
+            stream_samples = numpy.array(stream_samples)
+            stream_samples = stream_samples.T.flatten()
+            samples.append(stream_samples)
+
+        samples = numpy.array(samples).flatten()
+        assert(numpy.allclose(samples, java_samples))
+
+def test_consistency_cpu_serial():
+    '''Verify that the random numbers generated by mrg_uniform, serially,
+    are the same as the reference (Java) implementation by L'Ecuyer et al.
+    '''
+    seed = 12345
+    n_samples = 5
+    n_streams = 12
+    n_substreams = 7
+
+    samples = []
+    curr_rstate = numpy.array([seed] * 6, dtype='int32')
+
+    for i in range(n_streams):
+        stream_rstate = curr_rstate.copy()
+        for j in range(n_substreams):
+            rstate = tensor.shared(numpy.array([stream_rstate.copy()], dtype='int32'))
+            new_rstate, sample = rng_mrg.mrg_uniform.new(rstate, ndim=None, dtype=config.floatX, size=(1,))
+            # Not really necessary, just mimicking rng_mrg.MRG_RandomStreams' behavior
+            sample.rstate = rstate
+            sample.update = (rstate, new_rstate)
+
+            rstate.default_update = new_rstate
+            f = theano.function([], sample)
+            for k in range(n_samples):
+                s = f()
+                samples.append(s)
+
+            # next substream
+            stream_rstate = rng_mrg.ff_2p72(stream_rstate)
+
+        # next stream
+        curr_rstate = rng_mrg.ff_2p134(curr_rstate)
+
+    samples = numpy.array(samples).flatten()
+    assert(numpy.allclose(samples, java_samples))
+
+def test_consistency_cpu_parallel():
+    '''Verify that the random numbers generated by mrg_uniform, in parallel,
+    are the same as the reference (Java) implementation by L'Ecuyer et al.
+    '''
+    seed = 12345
+    n_samples = 5
+    n_streams = 12
+    n_substreams = 7 # 7 samples will be drawn in parallel
+
+    samples = []
+    curr_rstate = numpy.array([seed]*6, dtype='int32')
+
+    for i in range(n_streams):
+        stream_samples = []
+        rstate = [curr_rstate.copy()]
+        for j in range(1, n_substreams):
+            rstate.append(rng_mrg.ff_2p72(rstate[-1]))
+        rstate = numpy.asarray(rstate)
+        rstate = tensor.shared(rstate)
+
+        new_rstate, sample = rng_mrg.mrg_uniform.new(rstate, ndim=None,
+                dtype=config.floatX, size=(n_substreams,))
+        # Not really necessary, just mimicking rng_mrg.MRG_RandomStreams' behavior
+        sample.rstate = rstate
+        sample.update = (rstate, new_rstate)
+
+        rstate.default_update = new_rstate
+        f = theano.function([], sample)
+
+        for k in range(n_samples):
+            s = f()
+            stream_samples.append(s)
+
+        samples.append(numpy.array(stream_samples).T.flatten())
+
+        # next stream
+        curr_rstate = rng_mrg.ff_2p134(curr_rstate)
+
+    samples = numpy.array(samples).flatten()
+    assert(numpy.allclose(samples, java_samples))
+
+def test_consistency_GPU_serial():
+    '''Verify that the random numbers generated by GPU_mrg_uniform, serially,
+    are the same as the reference (Java) implementation by L'Ecuyer et al.
+    '''
+    if not cuda_available:
+        raise SkipTest('Optional package cuda not available')
+    if config.mode == 'FAST_COMPILE':
+        mode = 'FAST_RUN'
+    else:
+        mode = config.mode
+
+    seed = 12345
+    n_samples = 5
+    n_streams = 12
+    n_substreams = 7
+
+    samples = []
+    curr_rstate = numpy.array([seed] * 6, dtype='int32')
+
+    for i in range(n_streams):
+        stream_rstate = curr_rstate.copy()
+        for j in range(n_substreams):
+            substream_rstate = numpy.array(stream_rstate.copy(), dtype='int32')
+            # HACK - we transfer these int32 to the GPU memory as float32
+            # (reinterpret_cast)
+            tmp_float_buf = numpy.frombuffer(substream_rstate.data, dtype='float32')
+            rstate = float32_shared_constructor(tmp_float_buf) # Transfer to device
+
+            new_rstate, sample = rng_mrg.GPU_mrg_uniform.new(rstate, ndim=None,
+                    dtype='float32', size=(1,))
+            rstate.default_update = new_rstate
+
+            # Not really necessary, just mimicking rng_mrg.MRG_RandomStreams' behavior
+            sample.rstate = rstate
+            sample.update = (rstate, new_rstate)
+
+            # We need the sample back in the main memory
+            cpu_sample = tensor.as_tensor_variable(sample)
+            f = theano.function([], cpu_sample, mode=mode)
+            for k in range(n_samples):
+                s = f()
+                samples.append(s)
+
+            # next substream
+            stream_rstate = rng_mrg.ff_2p72(stream_rstate)
+
+        # next stream
+        curr_rstate = rng_mrg.ff_2p134(curr_rstate)
+
+    samples = numpy.array(samples).flatten()
+    assert(numpy.allclose(samples, java_samples))
+
+def test_consistency_GPU_parallel():
+    '''Verify that the random numbers generated by GPU_mrg_uniform, in parallel,
+    are the same as the reference (Java) implementation by L'Ecuyer et al.
+    '''
+    if not cuda_available:
+        raise SkipTest('Optional package cuda not available')
+    if config.mode == 'FAST_COMPILE':
+        mode = 'FAST_RUN'
+    else:
+        mode = config.mode
+
+    seed = 12345
+    n_samples = 5
+    n_streams = 12
+    n_substreams = 7 # 7 samples will be drawn in parallel
+
+    samples = []
+    curr_rstate = numpy.array([seed]*6, dtype='int32')
+
+    for i in range(n_streams):
+        stream_samples = []
+        rstate = [curr_rstate.copy()]
+        for j in range(1, n_substreams):
+            rstate.append(rng_mrg.ff_2p72(rstate[-1]))
+        rstate = numpy.asarray(rstate).flatten()
+        # HACK - transfer these int32 to the GPU memory as float32
+        # (reinterpret_cast)
+        tmp_float_buf = numpy.frombuffer(rstate.data, dtype='float32')
+        rstate = float32_shared_constructor(tmp_float_buf) # Transfer to device
+
+        new_rstate, sample = rng_mrg.GPU_mrg_uniform.new(rstate, ndim=None,
+                dtype='float32', size=(n_substreams,))
+        rstate.default_update = new_rstate
+
+        # Not really necessary, just mimicking rng_mrg.MRG_RandomStreams' behavior
+        sample.rstate = rstate
+        sample.update = (rstate, new_rstate)
+
+        # We need the sample back in the main memory
+        cpu_sample = tensor.as_tensor_variable(sample)
+        f = theano.function([], cpu_sample)
+
+        for k in range(n_samples):
+            s = f()
+            stream_samples.append(s)
+
+        samples.append(numpy.array(stream_samples).T.flatten())
+
+        # next stream
+        curr_rstate = rng_mrg.ff_2p134(curr_rstate)
+
+    samples = numpy.array(samples).flatten()
+    assert(numpy.allclose(samples, java_samples))
 
 def test_rng0():