/
rng_mrg.py
1229 lines (1042 loc) · 46.6 KB
/
rng_mrg.py
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"""
Implementation of MRG31k3p random number generator for Theano.
Generator code in SSJ package (L'Ecuyer & Simard).
http://www.iro.umontreal.ca/~simardr/ssj/indexe.html
The MRG31k3p algorithm was published in:
P. L'Ecuyer and R. Touzin, Fast Combined Multiple Recursive Generators with Multipliers of the form a = +/- 2^d +/- 2^e, Proceedings of the 2000 Winter Simulation Conference, Dec. 2000, 683-689.
The conception of the multi-stream from MRG31k3p was published in:
P. L'Ecuyer and R. Simard and E. Jack Chen and W. David Kelton, An Object-Oriented Random-Number Package with Many Long Streams and Substreams, Operations Research, volume 50, number 6, 2002, 1073-1075.
"""
from __future__ import absolute_import, print_function, division
import warnings
import numpy as np
from six import integer_types, string_types
from six.moves import xrange
import theano
from theano import Op, Apply, shared, config, Variable
from theano import gradient, function
from theano.gradient import undefined_grad
from theano import tensor
from theano.tensor import (TensorType, as_tensor_variable, get_vector_length,
cast, opt, scal)
from theano.compile import optdb
from theano.gof import local_optimizer, ParamsType
from theano.scalar import bool as bool_t, int32 as int_t
from . import multinomial
def matVecModM(A, s, m):
# TODO : need description for method, parameter and return
assert A.dtype == 'int64'
return np.int32(np.sum((A * s) % m, 1) % m)
def multMatVect(v, A, m1, B, m2):
# TODO : need description for parameter and return
"""
Multiply the first half of v by A with a modulo of m1 and the second half
by B with a modulo of m2.
Notes
-----
The parameters of dot_modulo are passed implicitly because passing them
explicitly takes more time than running the function's C-code.
"""
if multMatVect.dot_modulo is None:
A_sym = tensor.lmatrix('A')
s_sym = tensor.ivector('s')
m_sym = tensor.iscalar('m')
A2_sym = tensor.lmatrix('A2')
s2_sym = tensor.ivector('s2')
m2_sym = tensor.iscalar('m2')
o = DotModulo()(A_sym, s_sym, m_sym, A2_sym, s2_sym, m2_sym)
multMatVect.dot_modulo = function(
[A_sym, s_sym, m_sym, A2_sym, s2_sym, m2_sym], o, profile=False)
# This way of calling the Theano fct is done to bypass Theano overhead.
f = multMatVect.dot_modulo
f.input_storage[0].storage[0] = A
f.input_storage[1].storage[0] = v[:3]
f.input_storage[2].storage[0] = m1
f.input_storage[3].storage[0] = B
f.input_storage[4].storage[0] = v[3:]
f.input_storage[5].storage[0] = m2
f.fn()
r = f.output_storage[0].storage[0]
return r
multMatVect.dot_modulo = None
class DotModulo(Op):
"""
Efficient and numerically stable implementation of a dot product followed
by a modulo operation. This performs the same function as matVecModM.
We do this 2 times on 2 triple inputs and concatenating the output.
"""
__props__ = ()
def make_node(self, A, s, m, A2, s2, m2):
return Apply(self, [A, s, m, A2, s2, m2], [s.type()])
def perform(self, node, inputs, outputs):
(A, s, m, A2, s2, m2) = inputs
(out,) = outputs
o1 = matVecModM(A, s, m)
o2 = matVecModM(A2, s2, m2)
out[0] = np.concatenate((o1, o2))
def c_code_cache_version(self):
return (6,)
def c_code(self, node, name, inputs, outputs, sub):
(_A, _s, _m, _A2, _s2, _m2) = inputs
(_z,) = outputs
return """
int osize = -1;
if (PyArray_NDIM(%(_A)s) != 2) {PyErr_SetString(PyExc_NotImplementedError, "rank(A) != 2"); %(fail)s;}
if (PyArray_NDIM(%(_s)s) != 1) {PyErr_SetString(PyExc_NotImplementedError, "rank(v) != 1"); %(fail)s;}
if (PyArray_NDIM(%(_m)s) != 0) {PyErr_SetString(PyExc_NotImplementedError, "rank(m) != 0"); %(fail)s;}
if (PyArray_NDIM(%(_A2)s) != 2) {PyErr_SetString(PyExc_NotImplementedError, "rank(A2) != 2"); %(fail)s;}
if (PyArray_NDIM(%(_s2)s) != 1) {PyErr_SetString(PyExc_NotImplementedError, "rank(v2) != 1"); %(fail)s;}
if (PyArray_NDIM(%(_m2)s) != 0) {PyErr_SetString(PyExc_NotImplementedError, "rank(m2) != 0"); %(fail)s;}
if( PyArray_DIMS(%(_A)s)[1] != PyArray_DIMS(%(_s)s)[0])
{PyErr_SetString(PyExc_NotImplementedError, "A and s shapes don't agree."); %(fail)s;}
if( PyArray_DIMS(%(_A2)s)[1] != PyArray_DIMS(%(_s2)s)[0])
{PyErr_SetString(PyExc_NotImplementedError, "A2 and s2 shapes don't agree."); %(fail)s;}
osize = PyArray_DIMS(%(_A)s)[0] + PyArray_DIMS(%(_A2)s)[0];
if (!%(_z)s
|| (PyArray_DIMS(%(_z)s)[0] != osize))
{
{Py_XDECREF(%(_z)s);}
npy_intp dims[] = {0,};
dims[0] = osize;
%(_z)s = (PyArrayObject*) PyArray_SimpleNew(1, dims, PyArray_TYPE(%(_s)s));
}
if(!%(_z)s){%(fail)s;}
{ //makes it compile even though labels jump over variable definitions.
// A has size MxN, s has N, output M
npy_intp M = PyArray_DIMS(%(_A)s)[0];
npy_intp N = PyArray_DIMS(%(_A)s)[1];
const dtype_%(_A)s* __restrict__ DA = (dtype_%(_A)s*)PyArray_DATA(%(_A)s);
dtype_%(_s)s* __restrict__ Ds = (dtype_%(_s)s*)PyArray_DATA(%(_s)s);
dtype_%(_z)s* __restrict__ Dz = (dtype_%(_z)s*)PyArray_DATA(%(_z)s);
const dtype_%(_m)s m = ((dtype_%(_m)s*)PyArray_DATA(%(_m)s))[0];
npy_intp SA = PyArray_STRIDES(%(_A)s)[1] / PyArray_DESCR(%(_A)s)->elsize;
npy_intp Ss = PyArray_STRIDES(%(_s)s)[0] / PyArray_DESCR(%(_s)s)->elsize;
npy_intp Sz = PyArray_STRIDES(%(_z)s)[0] / PyArray_DESCR(%(_z)s)->elsize;
for (npy_int32 i = 0; i < M; ++i)
{
const dtype_%(_A)s* __restrict__ Ak = (dtype_%(_A)s*)(PyArray_BYTES(%(_A)s) + PyArray_STRIDES(%(_A)s)[0] * i);
npy_int64 r = 0;
for (npy_int32 j = 0; j < N; ++j)
{
r += (npy_int64)(Ds[j * Ss] * (npy_int64)(Ak[j * SA])) %% m;
}
Dz[i * Sz] = r %% m;
}
}
//redo it with the second triple of inputs
{
// A has size MxN, s has N, output M
npy_intp M = PyArray_DIMS(%(_A2)s)[0];
npy_intp N = PyArray_DIMS(%(_A2)s)[1];
const dtype_%(_A2)s* __restrict__ DA = (dtype_%(_A2)s*)PyArray_DATA(%(_A2)s);
dtype_%(_s2)s* __restrict__ Ds = (dtype_%(_s2)s*)PyArray_DATA(%(_s2)s);
const dtype_%(_m2)s m = ((dtype_%(_m2)s*)PyArray_DATA(%(_m2)s))[0];
npy_intp SA = PyArray_STRIDES(%(_A2)s)[1] / PyArray_DESCR(%(_A2)s)->elsize;
npy_intp Ss = PyArray_STRIDES(%(_s2)s)[0] / PyArray_DESCR(%(_s2)s)->elsize;
npy_intp Sz = PyArray_STRIDES(%(_z)s)[0] / PyArray_DESCR(%(_z)s)->elsize;
dtype_%(_z)s* __restrict__ Dz = (dtype_%(_z)s*)PyArray_DATA(%(_z)s) + PyArray_DIMS(%(_A)s)[0] * Sz;
for (npy_int32 i = 0; i < M; ++i)
{
const dtype_%(_A2)s* __restrict__ Ak = (dtype_%(_A2)s*)(PyArray_BYTES(%(_A2)s) + PyArray_STRIDES(%(_A2)s)[0] * i);
npy_int64 r = 0;
for (npy_int32 j = 0; j < N; ++j)
{
r += (npy_int64)(Ds[j * Ss] * (npy_int64)(Ak[j * SA])) %% m;
}
Dz[i * Sz] = r %% m;
}
}
""" % dict(locals(), **sub)
# MRG31k3p
# generator constants :
M1 = np.asarray(np.int32(2147483647)) # 2^31 - 1
M2 = np.asarray(np.int32(2147462579)) # 2^31 - 21069
MASK12 = np.int32(511) # 2^9 - 1
MASK13 = np.int32(16777215) # 2^24 - 1
MASK2 = np.int32(65535) # 2^16 - 1
MULT2 = np.int32(21069)
NORM = 4.656612873077392578125e-10 # 1./2^31
# A1p0 = np.asarray([[0, 4194304, 129], [1, 0, 0], [0, 1, 0]],
# dtype='int64')
# A2p0 = np.asarray([[32768, 0, 32769], [1, 0, 0], [0, 1, 0]],
# dtype='int64')
A1p72 = np.asarray([[1516919229, 758510237, 499121365],
[1884998244, 1516919229, 335398200],
[601897748, 1884998244, 358115744]],
dtype='int64')
A2p72 = np.asarray([[1228857673, 1496414766, 954677935],
[1133297478, 1407477216, 1496414766],
[2002613992, 1639496704, 1407477216]],
dtype='int64')
A1p134 = np.asarray(
[[1702500920, 1849582496, 1656874625],
[828554832, 1702500920, 1512419905],
[1143731069, 828554832, 102237247]],
dtype='int64')
A2p134 = np.asarray(
[[796789021, 1464208080, 607337906],
[1241679051, 1431130166, 1464208080],
[1401213391, 1178684362, 1431130166]],
dtype='int64')
np_int32_vals = [np.int32(i) for i in (0, 7, 9, 15, 16, 22, 24)]
def ff_2p134(rstate):
# TODO : need description for method, parameter and return
return multMatVect(rstate, A1p134, M1, A2p134, M2)
def ff_2p72(rstate):
# TODO : need description for method, parameter and return
return multMatVect(rstate, A1p72, M1, A2p72, M2)
def mrg_next_value(rstate, new_rstate, NORM, mask, offset):
# TODO : need description for method, parameter and return
x11, x12, x13, x21, x22, x23 = rstate
assert type(x11) == np.int32
i0, i7, i9, i15, i16, i22, i24 = np_int32_vals
# first component
y1 = (((x12 & MASK12) << i22) + (x12 >> i9) +
((x13 & MASK13) << i7) + (x13 >> i24))
assert type(y1) == np.int32
if (y1 < 0 or y1 >= M1): # must also check overflow
y1 -= M1
y1 += x13
if (y1 < 0 or y1 >= M1):
y1 -= M1
x13 = x12
x12 = x11
x11 = y1
# second component
y1 = ((x21 & MASK2) << i15) + (MULT2 * (x21 >> i16))
assert type(y1) == np.int32
if (y1 < 0 or y1 >= M2):
y1 -= M2
y2 = ((x23 & MASK2) << i15) + (MULT2 * (x23 >> i16))
assert type(y2) == np.int32
if (y2 < 0 or y2 >= M2):
y2 -= M2
y2 += x23
if (y2 < 0 or y2 >= M2):
y2 -= M2
y2 += y1
if (y2 < 0 or y2 >= M2):
y2 -= M2
x23 = x22
x22 = x21
x21 = y2
# Must never return either 0 or M1+1
new_rstate[...] = [x11, x12, x13, x21, x22, x23]
assert new_rstate.dtype == np.int32
if (x11 <= x21):
return (((x11 - x21 + M1) & mask) + offset) * NORM
else:
return (((x11 - x21) & mask) + offset) * NORM
class mrg_uniform_base(Op):
# TODO : need description for class, parameter
__props__ = ("output_type", "inplace")
params_type = ParamsType(inplace=bool_t,
# following params will come from self.output_type.
# NB: As output object may not be allocated in C code,
# we can not be sure to get these properties from output.
# So, we should better get them as params from self.output_type.
ndim=int_t,
otypenum=int_t,
otype_is_float32=bool_t)
def __init__(self, output_type, inplace=False):
Op.__init__(self)
self.output_type = output_type
self.inplace = inplace
if inplace:
self.destroy_map = {0: [0]}
self.warned_numpy_version = False
# These attributes (used as params) are created as properties
# to make them available even for old pickled objects, e.g.
# when testing old interface or when using FAST_COMPILE mode.
ndim = property(lambda self: self.output_type.ndim)
otypenum = property(lambda self: np.dtype(self.output_type.dtype).num)
otype_is_float32 = property(lambda self: self.output_type.dtype == 'float32')
def __str__(self):
if self.inplace:
s = "inplace"
else:
s = "no_inplace"
return self.__class__.__name__ + "{%s,%s}" % (self.output_type, s)
def grad(self, inputs, ograd):
return [gradient.grad_undefined(self, k, inp,
'No gradient defined through '
'random sampling op')
for k, inp in enumerate(inputs)]
def R_op(self, inputs, eval_points):
return [None for i in eval_points]
class mrg_uniform(mrg_uniform_base):
# CPU VERSION
_f16_ok = True
def make_node(self, rstate, size):
# error checking slightly redundant here, since
# this op should not be called directly.
#
# call through MRG_RandomStreams instead.
broad = []
for i in range(self.output_type.ndim):
broad.append(tensor.extract_constant(size[i]) == 1)
output_type = self.output_type.clone(broadcastable=broad)()
rstate = as_tensor_variable(rstate)
size = as_tensor_variable(size)
return Apply(self,
[rstate, size],
[rstate.type(), output_type])
@classmethod
def new(cls, rstate, ndim, dtype, size):
v_size = as_tensor_variable(size)
if ndim is None:
ndim = get_vector_length(v_size)
op = cls(TensorType(dtype, (False,) * ndim))
return op(rstate, v_size)
def perform(self, node, inp, out, params):
rstate, size = inp
o_rstate, o_sample = out
n_elements = 1
for s in size:
n_elements *= s
if n_elements > M1:
# The limit is on the C and GPU code. This perform don't
# have this limit. But to have all of them behave the
# same (and have DebugMode don't use too much memory for
# some rng_mrg tests) I also add this limit here.
raise ValueError("rng_mrg does not support more then (2**31 -1) samples")
rstate = np.asarray(rstate) # bring state from GPU if necessary
if not self.inplace:
rstate = rstate.copy()
n_streams, _ = rstate.shape
rval = np.zeros(n_elements, dtype=self.output_type.dtype)
if rval.dtype == 'float16':
mask = 0x7fff
offset = 1
NORM = np.float16(3.0458e-05)
elif rval.dtype == 'float32':
mask = 0xffffffff
offset = 0
NORM = np.float32(4.6566126e-10)
elif rval.dtype == 'float64':
mask = 0xffffffff
offset = 0
NORM = 4.656612873077392578125e-10 # 1./2^31
err_orig = np.seterr(over='ignore')
try:
for i in xrange(n_elements):
sample = mrg_next_value(rstate[i % n_streams],
rstate[i % n_streams],
NORM=NORM, mask=mask, offset=offset)
rval[i] = sample
finally:
np.seterr(**err_orig)
# send to GPU if necessary
o_rstate[0] = node.outputs[0].type.filter(rstate)
o_sample[0] = node.outputs[1].type.filter(rval.reshape(size))
def c_support_code(self):
return "\n".join("""
void cpu_rng_mrg_uniform_%(dtype)s(PyArrayObject* o_sample, PyArrayObject* o_rstate,
npy_int64 n_elements, int n_streams) {
const npy_int32 i0 = 0;
const npy_int32 i7 = 7;
const npy_int32 i9 = 9;
const npy_int32 i15 = 15;
const npy_int32 i16 = 16;
const npy_int32 i22 = 22;
const npy_int32 i24 = 24;
const npy_int32 M1 = 2147483647; //2^31 - 1
const npy_int32 M2 = 2147462579; //2^31 - 21069
const npy_int32 MASK12 = 511; //2^9 - 1
const npy_int32 MASK13 = 16777215; //2^24 - 1
const npy_int32 MASK2 = 65535; //2^16 - 1
const npy_int32 MULT2 = 21069;
%(dtype)s* sample_data = (%(dtype)s *) PyArray_DATA(o_sample);
npy_int32* state_data = (npy_int32 *) PyArray_DATA(o_rstate);
for (int i = 0; i < n_elements; ++i)
{
npy_int32 * state_data_i = state_data + (i%%n_streams)*6;
npy_int32 y1, y2, x11, x12, x13, x21, x22, x23;
x11 = state_data_i[0];
x12 = state_data_i[1];
x13 = state_data_i[2];
x21 = state_data_i[3];
x22 = state_data_i[4];
x23 = state_data_i[5];
y1 = ((x12 & MASK12) << i22) + (x12 >> i9) + ((x13 & MASK13) << i7) + (x13 >> i24);
if ((y1 < 0 || y1 >= M1)) //must also check overflow
y1 -= M1;
y1 += x13;
if ((y1 < 0 or y1 >= M1))
y1 -= M1;
x13 = x12;
x12 = x11;
x11 = y1;
y1 = ((x21 & MASK2) << i15) + (MULT2 * (x21 >> i16));
if (y1 < 0 || y1 >= M2)
y1 -= M2;
y2 = ((x23 & MASK2) << i15) + (MULT2 * (x23 >> i16));
if (y2 < 0 || y2 >= M2)
y2 -= M2;
y2 += x23;
if (y2 < 0 || y2 >= M2)
y2 -= M2;
y2 += y1;
if (y2 < 0 or y2 >= M2)
y2 -= M2;
x23 = x22;
x22 = x21;
x21 = y2;
if (x11 <= x21) {
assert((x11 - x21 + M1) <= M1);
sample_data[i] = (x11 - x21 + M1) * %(NORM)s;
}
else
{
assert(x11 - x21 <= M1);
sample_data[i] = (x11 - x21) * %(NORM)s;
}
state_data_i[0]= x11;
state_data_i[1]= x12;
state_data_i[2]= x13;
state_data_i[3]= x21;
state_data_i[4]= x22;
state_data_i[5]= x23;
}
}
""" % dict(dtype=dtype, NORM=NORM) for dtype, NORM in (
('npy_float32', '4.6566126e-10f'),
('npy_float64', '4.656612873077392578125e-10')
))
def c_code(self, node, name, inp, out, sub):
# If we try to use the C code here with something else than a
# TensorType, something is wrong (likely one of the GPU ops
# not defining C code correctly).
assert isinstance(node.inputs[0].type, TensorType)
if self.output_type.dtype == 'float16':
# C code is not tested, fall back to Python
super(mrg_uniform, self).c_code(node, name, inp, out, sub)
return """
//////// <code generated by mrg_uniform>
npy_int64 odims_i;
npy_int64 n_elements = 1;
int n_streams = 0;
int must_alloc_sample = ((NULL == %(o_sample)s)
|| (PyArray_NDIM(%(o_sample)s) != %(params)s->ndim)
|| !(PyArray_ISCONTIGUOUS(%(o_sample)s)));
int o_rstate_requirement = %(params)s->inplace ?
(NPY_ARRAY_C_CONTIGUOUS|NPY_ARRAY_ALIGNED) :
(NPY_ARRAY_ENSURECOPY|NPY_ARRAY_C_CONTIGUOUS|NPY_ARRAY_ALIGNED);
const npy_int32 i0 = 0;
const npy_int32 i7 = 7;
const npy_int32 i9 = 9;
const npy_int32 i15 = 15;
const npy_int32 i16 = 16;
const npy_int32 i22 = 22;
const npy_int32 i24 = 24;
const npy_int32 M1 = 2147483647; //2^31 - 1
const npy_int32 M2 = 2147462579; //2^31 - 21069
const npy_int32 MASK12 = 511; //2^9 - 1
const npy_int32 MASK13 = 16777215; //2^24 - 1
const npy_int32 MASK2 = 65535; //2^16 - 1
const npy_int32 MULT2 = 21069;
// We have to read size[i] as an int64, but odims has to be intp*
// for NumPy on 32-bit platforms.
npy_intp* odims = (npy_intp*)malloc(%(params)s->ndim * sizeof(npy_intp));
if (odims == NULL) {
PyErr_NoMemory();
%(just_fail)s
}
if (PyArray_NDIM(%(size)s) != 1)
{
PyErr_SetString(PyExc_ValueError, "size must be vector");
%(fail)s
}
if (PyArray_DIMS(%(size)s)[0] != %(params)s->ndim)
{
PyErr_Format(PyExc_ValueError, "size must have length %%i (not %%i)",
%(params)s->ndim, int(PyArray_DIMS(%(size)s)[0]));
%(fail)s
}
for (int i = 0; i < %(params)s->ndim; ++i)
{
odims_i = *(dtype_%(size)s *)PyArray_GETPTR1(%(size)s, i);
odims[i] = odims_i;
n_elements *= odims_i;
must_alloc_sample = must_alloc_sample || (PyArray_DIMS(%(o_sample)s)[i] != odims[i]);
//fprintf(stderr, "size %%i %%i\\n", i, (int)odims[i]);
//printf("%%li", n_elements);
}
//fprintf(stderr, "n_elements %%lld\\n", (long long)n_elements);
if (n_elements > M1)
{
PyErr_SetString(
PyExc_ValueError,
"rng_mrg cpu-implementation does not support more than (2**31 -1) samples");
%(fail)s
}
if (must_alloc_sample)
{
Py_XDECREF(%(o_sample)s);
%(o_sample)s = (PyArrayObject*)PyArray_SimpleNew(%(params)s->ndim, odims, %(params)s->otypenum);
if(!%(o_sample)s) {
PyErr_SetString(PyExc_MemoryError, "failed to alloc mrg_uniform output");
%(fail)s
}
}
Py_XDECREF(%(o_rstate)s);
%(o_rstate)s = (PyArrayObject*)PyArray_FromAny(
(PyObject*)%(rstate)s,
NULL, 0, 0, o_rstate_requirement,NULL);
if (PyArray_NDIM(%(o_rstate)s) != 2)
{
PyErr_SetString(PyExc_ValueError, "rstate must be matrix");
%(fail)s
}
if (PyArray_DIMS(%(o_rstate)s)[1] != 6)
{
PyErr_Format(PyExc_ValueError, "rstate must have 6 columns");
%(fail)s
}
if (PyArray_DESCR(%(o_rstate)s)->type_num != NPY_INT32)
{
PyErr_SetString(PyExc_ValueError, "rstate must be int32");
%(fail)s
}
n_streams = PyArray_DIMS(%(o_rstate)s)[0];
if (%(params)s->otype_is_float32) {
cpu_rng_mrg_uniform_npy_float32(%(o_sample)s, %(o_rstate)s, n_elements, n_streams);
} else {
cpu_rng_mrg_uniform_npy_float64(%(o_sample)s, %(o_rstate)s, n_elements, n_streams);
}
free(odims);
//////// </ code generated by mrg_uniform>
""" % dict(rstate=inp[0], size=inp[1],
o_rstate=out[0], o_sample=out[1],
params=sub['params'],
just_fail=sub['fail'],
fail="""
{
free(odims);
%(fail)s
}
""" % dict(fail=sub['fail']))
def c_code_cache_version(self):
return (10,)
def guess_n_streams(size, warn=False):
# TODO : need description for parameter 'size'
"""
Return a guess at a good number of streams.
Parameters
----------
warn : bool, optional
If True, warn when a guess cannot be made (in which case we
return 60 * 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, integer_types) 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
# The purpose of sampling from many streams is to be able to use
# the GPU to its full capacity. It just wastes RAM and
# stream-initialization time to allocate more streams than necessary
# for the GPU.
# XXX: This number is chosen to be good for 280 and 480 architectures,
# Better would be to use pycuda to query the number of
# processors on the GPU device,
# rather than guessing 60.
return min(r, 60 * 256)
else:
if warn:
warnings.warn(
("MRG_RandomStreams Can't determine #streams "
"from size (%s), guessing 60*256") % str(size),
stacklevel=3)
return 60 * 256
class MRG_RandomStreams(object):
"""
Module component with similar interface to numpy.random
(numpy.random.RandomState).
Parameters
----------
seed : int or list of 6 int
A default seed to initialize the random state.
If a single int is given, it will be replicated 6 times.
The first 3 values of the seed must all be less than M1 = 2147483647,
and not all 0; and the last 3 values must all be less than
M2 = 2147462579, and not all 0.
"""
def updates(self):
# TODO : need description for method and return
return list(self.state_updates)
def __init__(self, seed=12345):
# A list of pairs of the form (input_r, output_r), representing the
# update rules of all the random states generated
# by this RandomStreams.
self.state_updates = []
super(MRG_RandomStreams, self).__init__()
# Needed to reset the streams.
self.default_instance_seed = seed
self.set_rstate(seed)
def set_rstate(self, seed):
# TODO : need description for method, parameter
if isinstance(seed, integer_types):
if seed == 0:
raise ValueError('seed should not be 0', seed)
elif seed >= M2:
raise ValueError('seed should be less than %i' % M2, seed)
self.rstate = np.asarray([seed] * 6, dtype='int32')
elif len(seed) == 6:
if seed[0] == 0 and seed[1] == 0 and seed[2] == 0:
raise ValueError(
'The first 3 values of seed should not be all 0', seed)
if seed[3] == 0 and seed[4] == 0 and seed[5] == 0:
raise ValueError(
'The last 3 values of seed should not be all 0', seed)
if seed[0] >= M1 or seed[1] >= M1 or seed[2] >= M1:
raise ValueError(
'The first 3 values of seed should be less than %i' % M1,
seed)
if seed[3] >= M2 or seed[4] >= M2 or seed[5] >= M2:
raise ValueError(
'The last 3 values of seed should be less than %i' % M2,
seed)
self.rstate = np.asarray(seed, dtype='int32')
else:
raise TypeError("seed should be 1 integer or 6 integers")
def seed(self, seed=None):
"""
Re-initialize each random stream.
Parameters
----------
seed : None or integer in range 0 to 2**30
Each random stream will be assigned a unique state that depends
deterministically on this value.
Returns
-------
None
"""
if seed is None:
seed = self.default_instance_seed
self.set_rstate(seed)
for old_r, new_r, size, nstreams in self.state_updates:
if nstreams is None:
nstreams = self.n_streams(size)
rstates = self.get_substream_rstates(nstreams,
new_r.owner.outputs[1].dtype)
assert (old_r.get_value(borrow=True,
return_internal_type=True).shape ==
rstates.shape)
assert rstates.dtype == old_r.dtype
old_r.set_value(rstates, borrow=True)
def inc_rstate(self):
"""
Update self.rstate to be skipped 2^134 steps forward to the next stream
start.
"""
# self.rstate = ff_2p134(self.rstate)
self.rstate = multMatVect(self.rstate, A1p134, M1, A2p134, M2)
assert self.rstate.dtype == np.int32
@theano.change_flags(compute_test_value='off')
def get_substream_rstates(self, n_streams, dtype, inc_rstate=True):
# TODO : need description for parameter and return
"""
Initialize a matrix in which each row is a MRG stream state,
and they are spaced by 2**72 samples.
"""
assert isinstance(dtype, string_types)
assert n_streams < 2**72
assert n_streams > 0
rval = np.zeros((n_streams, 6), dtype='int32')
rval[0] = self.rstate
# If multMatVect.dot_modulo isn't compiled, compile it.
if multMatVect.dot_modulo is None:
multMatVect(rval[0], A1p72, M1, A2p72, M2)
# This way of calling the Theano fct is done to bypass Theano overhead.
f = multMatVect.dot_modulo
f.input_storage[0].storage[0] = A1p72
f.input_storage[2].storage[0] = M1
f.input_storage[3].storage[0] = A2p72
f.input_storage[5].storage[0] = M2
for i in xrange(1, n_streams):
# Inline the following call to bypass Python overhead
# rval[i] = ff_2p72(rval[i - 1])
v = rval[i - 1]
f.input_storage[1].storage[0] = v[:3]
f.input_storage[4].storage[0] = v[3:]
f.fn()
rval[i] = f.output_storage[0].storage[0]
if inc_rstate:
self.inc_rstate()
return rval
def n_streams(self, size):
# TODO : need description for method, parameter and return
return guess_n_streams(size)
def pretty_return(self, node_rstate, new_rstate, sample, size, nstreams):
# TODO : need description for method, parameter and return
sample.rstate = node_rstate
sample.update = (node_rstate, new_rstate)
self.state_updates.append((node_rstate, new_rstate, size, nstreams))
node_rstate.default_update = new_rstate
return sample
def uniform(self, size, low=0.0, high=1.0, ndim=None, dtype=None,
nstreams=None, **kwargs):
# TODO : need description for parameter 'size', 'ndim', 'nstreams'
"""
Sample a tensor of given size whose element from a uniform
distribution between low and high.
If the size argument is ambiguous on the number of dimensions,
ndim may be a plain integer to supplement the missing information.
Parameters
----------
low
Lower bound of the interval on which values are sampled.
If the ``dtype`` arg is provided, ``low`` will be cast into
dtype. This bound is excluded.
high
Higher bound of the interval on which values are sampled.
If the ``dtype`` arg is provided, ``high`` will be cast into
dtype. This bound is excluded.
size
Can be a list of integer or Theano variable (ex: the shape
of other Theano Variable).
dtype
The output data type. If dtype is not specified, it will be
inferred from the dtype of low and high, but will be at
least as precise as floatX.
"""
low = as_tensor_variable(low)
high = as_tensor_variable(high)
if dtype is None:
dtype = scal.upcast(config.floatX, low.dtype, high.dtype)
low = cast(low, dtype=dtype)
high = cast(high, dtype=dtype)
low = undefined_grad(low)
high = undefined_grad(high)
if isinstance(size, tuple):
msg = "size must be a tuple of int or a Theano variable"
assert all([isinstance(i, (np.integer, integer_types, Variable))
for i in size]), msg
if any([isinstance(i, (np.integer, integer_types)) and i <= 0
for i in size]):
raise ValueError(
"The specified size contains a dimension with value <= 0",
size)
else:
if not (isinstance(size, Variable) and size.ndim == 1):
raise TypeError("size must be a tuple of int or a Theano "
"Variable with 1 dimension, got " + str(size) +
" of type " + str(type(size)))
orig_nstreams = nstreams
if nstreams is None:
nstreams = self.n_streams(size)
rstates = self.get_substream_rstates(nstreams, dtype)
d = {}
if 'target' in kwargs:
d = dict(target=kwargs.pop('target'))
if len(kwargs) > 0:
raise TypeError("uniform() got unexpected keyword arguments %s" % (str(kwargs.keys())))
node_rstate = shared(rstates, **d)
u = self.pretty_return(node_rstate,
*mrg_uniform.new(node_rstate,
ndim, dtype, size),
size=size, nstreams=orig_nstreams)
# Add a reference to distinguish from other shared variables
node_rstate.tag.is_rng = True
r = u * (high - low) + low
if u.type.broadcastable != r.type.broadcastable:
raise NotImplementedError(
'Increase the size to match the broadcasting pattern of '
'`low` and `high` arguments')
assert r.dtype == dtype
return r
def binomial(self, size=None, n=1, p=0.5, ndim=None, dtype='int64',
nstreams=None, **kwargs):
# TODO : need description for method, parameter and return
if n == 1:
p = undefined_grad(as_tensor_variable(p))
x = self.uniform(size=size, nstreams=nstreams, **kwargs)
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',
nstreams=None, **kwargs):
# TODO : need description for parameter and return
"""
Sample `n` (`n` needs to be >= 1, default 1) times from a multinomial
distribution defined by probabilities pvals.
Example : pvals = [[.98, .01, .01], [.01, .49, .50]] and n=1 will
probably result in [[1,0,0],[0,0,1]]. When setting n=2, this
will probably result in [[2,0,0],[0,1,1]].
Notes
-----
-`size` and `ndim` are only there keep the same signature as other
uniform, binomial, normal, etc.
TODO : adapt multinomial to take that into account
-Does not do any value checking on pvals, i.e. there is no
check that the elements are non-negative, less than 1, or
sum to 1. passing pvals = [[-2., 2.]] will result in
sampling [[0, 0]]
"""
if pvals is None:
raise TypeError("You have to specify pvals")
pvals = as_tensor_variable(pvals)
pvals = undefined_grad(pvals)
if size is not None:
if any([isinstance(i, integer_types) and i <= 0 for i in size]):
raise ValueError(
"The specified size contains a dimension with value <= 0",
size)
if size is not None:
raise ValueError(
"Provided a size argument to MRG_RandomStreams.multinomial, "
"which does not use the size argument.")
if ndim is not None:
raise ValueError(
"Provided an ndim argument to MRG_RandomStreams.multinomial, "
"which does not use the ndim argument.")
if pvals.ndim == 2:
size = pvals[:, 0].shape * n
unis = self.uniform(size=size, ndim=1, nstreams=nstreams, **kwargs)
op = multinomial.MultinomialFromUniform(dtype)
n_samples = as_tensor_variable(n)
return op(pvals, unis, n_samples)
else:
raise NotImplementedError(("MRG_RandomStreams.multinomial only"
" implemented for pvals.ndim = 2"))
def choice(self, size=1, a=None, replace=True, p=None, ndim=None,
dtype='int64', nstreams=None, **kwargs):
"""
Sample `size` times from a multinomial distribution defined by
probabilities `p`, and returns the indices of the sampled elements.
Sampled values are between 0 and `p.shape[1]-1`.
Only sampling without replacement is implemented for now.
Parameters
----------
size: integer or integer tensor (default 1)
The number of samples. It should be between 1 and `p.shape[1]-1`.
a: int or None (default None)
For now, a should be None. This function will sample
values between 0 and `p.shape[1]-1`. When a != None will be
implemented, if `a` is a scalar, the samples are drawn from the
range 0,...,a-1. We default to 2 as to have the same interface as
RandomStream.
replace: bool (default True)
Whether the sample is with or without replacement.
Only replace=False is implemented for now.
p: 2d numpy array or theano tensor
the probabilities of the distribution, corresponding to values
0 to `p.shape[1]-1`.
Example : p = [[.98, .01, .01], [.01, .49, .50]] and size=1 will
probably result in [[0],[2]]. When setting size=2, this
will probably result in [[0,1],[2,1]].
Notes
-----
-`ndim` is only there keep the same signature as other
uniform, binomial, normal, etc.
-Does not do any value checking on pvals, i.e. there is no
check that the elements are non-negative, less than 1, or
sum to 1. passing pvals = [[-2., 2.]] will result in
sampling [[0, 0]]
-Only replace=False is implemented for now.
"""
if replace:
raise NotImplementedError(