예제 #1
0
def test_consistency_GPUA_parallel():
    # Verify that the random numbers generated by GPUA_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 = np.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 = np.asarray(rstate)
        rstate = gpuarray_shared_constructor(rstate)

        new_rstate, sample = GPUA_mrg_uniform.new(rstate,
                                                  ndim=None,
                                                  dtype="float32",
                                                  size=(n_substreams, ))
        rstate.default_update = new_rstate

        # Not really necessary, just mimicking
        # rng_mrg.MRG_RandomStream' behavior
        sample.rstate = rstate
        sample.update = (rstate, new_rstate)

        # We need the sample back in the main memory
        cpu_sample = at.as_tensor_variable(sample)
        f = aesara.function([], cpu_sample, mode=mode)

        for k in range(n_samples):
            s = f()
            stream_samples.append(s)

        samples.append(np.array(stream_samples).T.flatten())

        # next stream
        curr_rstate = rng_mrg.ff_2p134(curr_rstate)

    samples = np.array(samples).flatten()
    assert np.allclose(samples, java_samples)
예제 #2
0
def test_consistency_GPUA_serial():
    # Verify that the random numbers generated by GPUA_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 = np.array([seed] * 6, dtype="int32")

    for i in range(n_streams):
        stream_rstate = curr_rstate.copy()
        for j in range(n_substreams):
            substream_rstate = np.array([stream_rstate.copy()], dtype="int32")
            # Transfer to device
            rstate = gpuarray_shared_constructor(substream_rstate)

            new_rstate, sample = GPUA_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 = aesara.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 = np.array(samples).flatten()
    assert np.allclose(samples, java_samples)
예제 #3
0
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 = np.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 = np.asarray(rstate)
        rstate = 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_RandomStream' behavior
        sample.rstate = rstate
        sample.update = (rstate, new_rstate)

        rstate.default_update = new_rstate
        f = function([], sample)

        for k in range(n_samples):
            s = f()
            stream_samples.append(s)

        samples.append(np.array(stream_samples).T.flatten())

        # next stream
        curr_rstate = rng_mrg.ff_2p134(curr_rstate)

    samples = np.array(samples).flatten()
    assert np.allclose(samples, java_samples)