""" 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 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 """ //////// 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); //////// """ % 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, str) 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( "MRG_RandomStreams.choice only works without replacement " "for now.") if a is not None: raise TypeError("For now, a has to be None in " "MRG_RandomStreams.choice. Sampled values are " "between 0 and p.shape[1]-1") if p is None: raise TypeError("For now, p has to be specified in " "MRG_RandomStreams.choice.") p = as_tensor_variable(p) p = undefined_grad(p) if ndim is not None: raise ValueError("ndim argument to " "MRG_RandomStreams.choice " "is not used.") if p.ndim != 2: raise NotImplementedError( "MRG_RandomStreams.choice is only implemented for p.ndim = 2") shape = p[:, 0].shape * size unis = self.uniform(size=shape, ndim=1, nstreams=nstreams, **kwargs) op = multinomial.ChoiceFromUniform(odtype=dtype) return op(p, unis, as_tensor_variable(size)) def multinomial_wo_replacement(self, size=None, n=1, pvals=None, ndim=None, dtype='int64', nstreams=None, **kwargs): warnings.warn('MRG_RandomStreams.multinomial_wo_replacement() is ' 'deprecated and will be removed in the next release of ' 'Theano. Please use MRG_RandomStreams.choice() instead.') assert size is None return self.choice(size=n, a=None, replace=False, p=pvals, dtype=dtype, nstreams=nstreams, ndim=ndim, **kwargs) def normal(self, size, avg=0.0, std=1.0, ndim=None, dtype=None, nstreams=None, truncate=False, **kwargs): """ Sample a tensor of values from a normal distribution. Parameters ---------- size : int_vector_like Array dimensions for the output tensor. avg : float_like, optional The mean value for the truncated normal to sample from (defaults to 0.0). std : float_like, optional The standard deviation for the truncated normal to sample from (defaults to 1.0). truncate : bool, optional Truncates the normal distribution at 2 standard deviations if True (defaults to False). When this flag is set, the standard deviation of the result will be less than the one specified. ndim : int, optional The number of dimensions for the output tensor (defaults to None). This argument is necessary if the size argument is ambiguous on the number of dimensions. dtype : str, optional The data-type for the output tensor. If not specified, the dtype is inferred from avg and std, but it is at least as precise as floatX. kwargs Other keyword arguments for random number generation (see uniform). Returns ------- samples : TensorVariable A Theano tensor of samples randomly drawn from a normal distribution. """ size = _check_size(size) avg = undefined_grad(as_tensor_variable(avg)) std = undefined_grad(as_tensor_variable(std)) if dtype is None: dtype = scal.upcast(config.floatX, avg.dtype, std.dtype) avg = tensor.cast(avg, dtype=dtype) std = tensor.cast(std, dtype=dtype) # generate even number of uniform samples # Do manual constant folding to lower optiimizer work. if isinstance(size, theano.Constant): n_odd_samples = size.prod(dtype='int64') else: n_odd_samples = tensor.prod(size, dtype='int64') n_even_samples = n_odd_samples + n_odd_samples % 2 uniform = self.uniform((n_even_samples, ), low=0., high=1., ndim=1, dtype=dtype, nstreams=nstreams, **kwargs) # box-muller transform u1 = uniform[:n_even_samples // 2] u2 = uniform[n_even_samples // 2:] r = tensor.sqrt(-2.0 * tensor.log(u1)) theta = np.array(2.0 * np.pi, dtype=dtype) * u2 cos_theta, sin_theta = tensor.cos(theta), tensor.sin(theta) z0 = r * cos_theta z1 = r * sin_theta if truncate: # use valid samples to_fix0 = (z0 < -2.) | (z0 > 2.) to_fix1 = (z1 < -2.) | (z1 > 2.) z0_valid = z0[tensor.nonzero(~to_fix0)] z1_valid = z1[tensor.nonzero(~to_fix1)] # re-sample invalid samples to_fix0 = tensor.nonzero(to_fix0)[0] to_fix1 = tensor.nonzero(to_fix1)[0] n_fix_samples = to_fix0.size + to_fix1.size lower = tensor.constant(1. / np.e**2, dtype=dtype) u_fix = self.uniform((n_fix_samples, ), low=lower, high=1., ndim=1, dtype=dtype, nstreams=nstreams, **kwargs) r_fix = tensor.sqrt(-2. * tensor.log(u_fix)) z0_fixed = r_fix[:to_fix0.size] * cos_theta[to_fix0] z1_fixed = r_fix[to_fix0.size:] * sin_theta[to_fix1] # pack everything together to a useful result norm_samples = tensor.join(0, z0_valid, z0_fixed, z1_valid, z1_fixed) else: norm_samples = tensor.join(0, z0, z1) if isinstance(n_odd_samples, theano.Variable): samples = norm_samples[:n_odd_samples] elif n_odd_samples % 2 == 1: samples = norm_samples[:-1] else: samples = norm_samples samples = tensor.reshape(samples, newshape=size, ndim=ndim) samples *= std samples += avg return samples def truncated_normal(self, size, avg=0.0, std=1.0, ndim=None, dtype=None, nstreams=None, **kwargs): """ Sample a tensor of values from a symmetrically truncated normal distribution. Parameters ---------- size : int_vector_like Array dimensions for the output tensor. avg : float_like, optional The mean value for the truncated normal to sample from (defaults to 0.0). std : float_like, optional The standard deviation for the truncated normal to sample from (defaults to 1.0). ndim : int, optional The number of dimensions for the output tensor (defaults to None). This argument is necessary if the size argument is ambiguous on the number of dimensions. dtype : str, optional The data-type for the output tensor. If not specified, the dtype is inferred from avg and std, but it is at least as precise as floatX. kwargs Other keyword arguments for random number generation (see uniform). Returns ------- samples : TensorVariable A Theano tensor of samples randomly drawn from a truncated normal distribution. See Also -------- normal """ # constant taken from scipy.stats.truncnorm.std(a=-2, b=2, loc=0., scale=1.) std = std / tensor.constant(.87962566103423978) return self.normal(size=size, avg=avg, std=std, truncate=True, ndim=ndim, dtype=dtype, nstreams=nstreams, **kwargs) def _check_size(size): """ Canonicalise inputs to get valid output sizes for Theano tensors. Parameters ---------- size : int_vector_like Some variable that could serve as the shape for a Theano tensor. This can be an int, a tuple of ints, a list of ints or a Theano Variable with similar properties. Returns ------- size_var : int_vector A one-dimensional Theano variable encapsulating the given size. Raises ------ ValueError If this method can not build a valid size from the input. """ # non-tuple checks and scalar-to-tuple transform if isinstance(size, theano.Variable): if size.ndim == 1: return size elif size.ndim == 0: return tensor.stack([size], ndim=1) else: raise ValueError("Theano variable must have 1 dimension to be a valid size.", size) elif isinstance(size, (np.integer, integer_types)): return tensor.constant([size], ndim=1) elif not isinstance(size, (tuple, list)): raise ValueError("Size must be a int, tuple, list or Theano variable.", size) # check entries of list or tuple for i in size: if isinstance(i, theano.Variable): if i.ndim != 0: raise ValueError("Non-scalar Theano variable in size", size, i) elif isinstance(i, (np.integer, integer_types)): if i <= 0: raise ValueError("Non-positive dimensions not allowed in size.", size, i) else: raise ValueError("Only Theano variables and integers are allowed in a size-tuple.", size, i) return tensor.as_tensor_variable(size, ndim=1) @local_optimizer((mrg_uniform_base,)) def mrg_random_make_inplace(node): op = node.op if isinstance(op, mrg_uniform_base) and not op.inplace: # op might be gpu version new_op = op.__class__(op.output_type, inplace=True) return new_op.make_node(*node.inputs).outputs return False optdb.register('random_make_inplace_mrg', opt.in2out(mrg_random_make_inplace, ignore_newtrees=True), 99, 'fast_run', 'inplace')