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')
# Transfer to device
rstate = float32_shared_constructor(tmp_float_buf)
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, mode=mode)
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_overflow_gpu_new_backend():
# run with THEANO_FLAGS=mode=FAST_RUN,init_gpu_device=cuda1,device=cpu
from theano.sandbox.gpuarray.tests.test_basic_ops import \
mode_with_gpu as mode
from theano.sandbox.gpuarray.type import gpuarray_shared_constructor
seed = 12345
n_substreams = 7
curr_rstate = numpy.array([seed] * 6, dtype='int32')
rstate = [curr_rstate.copy()]
for j in range(1, n_substreams):
rstate.append(rng_mrg.ff_2p72(rstate[-1]))
rstate = numpy.asarray(rstate)
rstate = gpuarray_shared_constructor(rstate)
fct = functools.partial(rng_mrg.GPUA_mrg_uniform.new, rstate,
ndim=None, dtype='float32')
# should raise error as the size overflows
sizes = [(2**31, ), (2**32, ), (2**15, 2**16,), (2, 2**15, 2**15)]
rng_mrg_overflow(sizes, fct, mode, should_raise_error=True)
# should not raise error
sizes = [(2**5, ), (2**5, 2**5), (2**5, 2**5, 2**5)]
rng_mrg_overflow(sizes, fct, mode, should_raise_error=False)
# should support int32 sizes
sizes = [(numpy.int32(2**10), ),
(numpy.int32(2), numpy.int32(2**10), numpy.int32(2**10))]
rng_mrg_overflow(sizes, fct, mode, should_raise_error=False)
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 = theano.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_overflow_gpu_new_backend():
# run with THEANO_FLAGS=mode=FAST_RUN,init_gpu_device=cuda1,device=cpu
from theano.sandbox.gpuarray.tests.test_basic_ops import \
mode_with_gpu as mode
from theano.sandbox.gpuarray.type import gpuarray_shared_constructor
seed = 12345
n_substreams = 7
curr_rstate = numpy.array([seed] * 6, dtype='int32')
rstate = [curr_rstate.copy()]
for j in range(1, n_substreams):
rstate.append(rng_mrg.ff_2p72(rstate[-1]))
rstate = numpy.asarray(rstate)
rstate = gpuarray_shared_constructor(rstate)
fct = functools.partial(rng_mrg.GPUA_mrg_uniform.new,
rstate,
ndim=None,
dtype='float32')
# should raise error as the size overflows
sizes = [(2**31, ), (2**32, ), (
2**15,
2**16,
), (2, 2**15, 2**15)]
rng_mrg_overflow(sizes, fct, mode, should_raise_error=True)
# should not raise error
sizes = [(2**5, ), (2**5, 2**5), (2**5, 2**5, 2**5)]
rng_mrg_overflow(sizes, fct, mode, should_raise_error=False)
# should support int32 sizes
sizes = [(numpy.int32(2**10), ),
(numpy.int32(2), numpy.int32(2**10), numpy.int32(2**10))]
rng_mrg_overflow(sizes, fct, mode, should_raise_error=False)
def test_overflow_gpu_new_backend():
seed = 12345
n_substreams = 7
curr_rstate = np.array([seed] * 6, dtype='int32')
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)
fct = functools.partial(GPUA_mrg_uniform.new,
rstate,
ndim=None,
dtype='float32')
# should raise error as the size overflows
sizes = [(2**31, ), (2**32, ), (
2**15,
2**16,
), (2, 2**15, 2**15)]
rng_mrg_overflow(sizes, fct, mode, should_raise_error=True)
# should not raise error
sizes = [(2**5, ), (2**5, 2**5), (2**5, 2**5, 2**5)]
rng_mrg_overflow(sizes, fct, mode, should_raise_error=False)
# should support int32 sizes
sizes = [(np.int32(2**10), ),
(np.int32(2), np.int32(2**10), np.int32(2**10))]
rng_mrg_overflow(sizes, fct, mode, should_raise_error=False)
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')
# Transfer to device
rstate = float32_shared_constructor(tmp_float_buf)
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, mode=mode)
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_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.
"""
from theano.sandbox.gpuarray.tests.test_basic_ops import \
mode_with_gpu as mode
from theano.sandbox.gpuarray.type import gpuarray_shared_constructor
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')
# Transfer to device
rstate = gpuarray_shared_constructor(substream_rstate)
new_rstate, sample = rng_mrg.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 = 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_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.
"""
from theano.sandbox.gpuarray.tests.test_basic_ops import \
mode_with_gpu as mode
from theano.sandbox.gpuarray.type import gpuarray_shared_constructor
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 = gpuarray_shared_constructor(rstate)
new_rstate, sample = rng_mrg.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_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()
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_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.
from theano.gpuarray.tests.config import mode_with_gpu as mode
from theano.gpuarray.type import gpuarray_shared_constructor
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 = gpuarray_shared_constructor(rstate)
new_rstate, sample = rng_mrg.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_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()
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_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.
from theano.gpuarray.tests.config import mode_with_gpu as mode
from theano.gpuarray.type import gpuarray_shared_constructor
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')
# Transfer to device
rstate = gpuarray_shared_constructor(substream_rstate)
new_rstate, sample = rng_mrg.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 = 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_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 = theano.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_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 = theano.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(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))
def test_overflow_gpu_new_backend():
seed = 12345
n_substreams = 7
curr_rstate = np.array([seed] * 6, dtype='int32')
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)
fct = functools.partial(GPUA_mrg_uniform.new, rstate,
ndim=None, dtype='float32')
# should raise error as the size overflows
sizes = [(2**31, ), (2**32, ), (2**15, 2**16,), (2, 2**15, 2**15)]
rng_mrg_overflow(sizes, fct, mode, should_raise_error=True)
# should not raise error
sizes = [(2**5, ), (2**5, 2**5), (2**5, 2**5, 2**5)]
rng_mrg_overflow(sizes, fct, mode, should_raise_error=False)
# should support int32 sizes
sizes = [(np.int32(2**10), ),
(np.int32(2), np.int32(2**10), np.int32(2**10))]
rng_mrg_overflow(sizes, fct, mode, should_raise_error=False)
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 = np.array([seed] * 6, dtype='int32')
for i in range(n_streams):
stream_rstate = curr_rstate.copy()
for j in range(n_substreams):
rstate = theano.shared(
np.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 = np.array(samples).flatten()
assert (np.allclose(samples, java_samples))
