experiments/STT-tensorflow
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity
5396e7a3cd
STT-tensorflow/tensorflow/python/ops/stateful_random_ops.py
Srinivas Vasudevan 5396e7a3cd Allow RandomBinomial op to broadcast parameters. 
- Add multiple parameter broadcasting support for BCast. This will allow it to be used in multiparameter broadcasting contexts. This is specifically for ternary ops, but will be used to make other samplers like ParameterizedTruncatedNormal broadcast.

- Add batch index methods for generating a list of batch indices when the input vectors are flattened. This is used to get broadcasting on flattened inputs (which is used in the RandomBinomial sampler).

- Shard on the number of outputs. This allows us to scale better to Tensor inputs.

PiperOrigin-RevId: 281202841
Change-Id: I0b276e983bf31056677a67b4d5ce8ebc98d77930
2019-11-18 20:18:33 -08:00

773 lines
27 KiB
Python
Raw Blame History

# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Operations for generating random numbers."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
import numpy as np
from tensorflow.python.eager import context
from tensorflow.python.framework import composite_tensor
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_spec
from tensorflow.python.framework import type_spec
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gen_stateful_random_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import resource_variable_ops
from tensorflow.python.ops import variables
from tensorflow.python.training.tracking import tracking
from tensorflow.python.util.tf_export import tf_export
# A seed for random ops (stateful and stateless) will always be 1024
# bits, all of which will be sent to the C++ code. The actual C++
# implementation of some algorithms may only use a lower part of the bits.
MAX_INT64 = 2**63 - 1
MIN_INT64 = -(2**63)
UINT64_SPAN = 2**64
# 'Variable' doesn't support uint32 or uint64 yet (due to reasons explained in
# b/111604096 and cl/171681867), so I use signed int here. I choose int64
# instead of int32 here because `VarHandleOp` doesn't support int32 on GPU.
SEED_TYPE = "int64"
SEED_MIN = MIN_INT64
SEED_MAX = MAX_INT64
SEED_UINT_SPAN = UINT64_SPAN
SEED_TYPE_BITS = 64
SEED_BIT_MASK = 0xFFFFFFFFFFFFFFFF
SEED_SIZE = 16 # in units of SEED_TYPE
STATE_TYPE = SEED_TYPE
ALGORITHM_TYPE = STATE_TYPE
RNG_ALG_PHILOX = 1
RNG_ALG_THREEFRY = 2
DEFAULT_ALGORITHM = RNG_ALG_PHILOX
PHILOX_STATE_SIZE = 3
THREEFRY_STATE_SIZE = 2
def non_deterministic_ints(shape, dtype=dtypes.int64):
"""Non-deterministically generates some integers.
This op may use some OS-provided source of non-determinism (e.g. an RNG), so
each execution will give different results.
Args:
shape: the shape of the result.
dtype: (optional) the dtype of the result.
Returns:
a tensor whose element values are non-deterministically chosen.
"""
return gen_stateful_random_ops.non_deterministic_ints(
shape=shape, dtype=dtype)
def _uint_to_int(n):
if n > SEED_MAX:
n = n - SEED_UINT_SPAN
return n
def _make_1d_state(state_size, seed):
"""Makes a 1-D RNG state.
Args:
state_size: an integer.
seed: an integer or 1-D tensor.
Returns:
a 1-D tensor of shape [state_size] and dtype STATE_TYPE.
"""
int_types = (int,) if sys.version_info >= (3, 0) else (int, long)
if isinstance(seed, int_types):
# chop the Python integer (infinite precision) into chunks of SEED_TYPE
ls = []
for _ in range(state_size):
ls.append(seed & SEED_BIT_MASK)
seed >>= SEED_TYPE_BITS
seed = ls
# to avoid overflow error from np.asarray
seed = list(map(_uint_to_int, seed))
seed = np.asarray(seed, dtype=STATE_TYPE)
if len(seed.shape) != 1:
raise ValueError(
"seed should only have one dimension; got shape: %s" % seed.shape)
seed = seed[0:state_size]
# Padding with zeros on the *left* if too short. Padding on the right would
# cause a small seed to be used as the "counter" while the "key" is always
# zero (for counter-based RNG algorithms), because in the current memory
# layout counter is stored before key. In such a situation two RNGs with
# two different small seeds may generate overlapping outputs.
seed_size = seed.shape[0]
if seed_size < state_size:
seed = np.pad(
seed, [(state_size - seed_size, 0)],
mode="constant",
constant_values=0)
assert seed.shape == (state_size,), "Wrong seed.shape: %s" % seed.shape
return seed
def _get_state_size(alg):
if alg == RNG_ALG_PHILOX:
return PHILOX_STATE_SIZE
elif alg == RNG_ALG_THREEFRY:
return THREEFRY_STATE_SIZE
else:
raise ValueError("Unsupported algorithm id: %s" % alg)
def _check_state_shape(shape, alg):
if isinstance(alg, ops.Tensor) and not context.executing_eagerly():
return
shape.assert_is_compatible_with([_get_state_size(int(alg))])
def _make_state_from_seed(seed, alg):
return _make_1d_state(_get_state_size(alg), seed)
@tf_export("random.experimental.create_rng_state")
def create_rng_state(seed, algorithm):
"""Creates a RNG state.
Args:
seed: an integer or 1-D tensor.
algorithm: an integer representing the RNG algorithm.
Returns:
a 1-D tensor whose size depends on the algorithm.
"""
return _make_state_from_seed(seed, algorithm)
def _shape_tensor(shape):
"""Convert to an int32 or int64 tensor, defaulting to int64 if empty."""
if isinstance(shape, (tuple, list)) and not shape:
dtype = dtypes.int64
else:
dtype = None
return ops.convert_to_tensor(shape, dtype=dtype, name="shape")
def _convert_to_state_tensor(t):
if isinstance(t, list):
# to avoid out-of-range error from ops.convert_to_tensor
t = list(map(_uint_to_int, t))
return ops.convert_to_tensor(t, dtype=STATE_TYPE)
class GeneratorSpec(type_spec.TypeSpec):
"""TypeSpec for Generator."""
def __init__(self, shape=None, dtype=None):
self.shape = shape
self.dtype = dtype
@property
def _component_specs(self):
return (tensor_spec.TensorSpec(shape=(), dtype=dtypes.resource),
tensor_spec.TensorSpec(shape=(), dtype=ALGORITHM_TYPE))
def _to_components(self, value):
return (value.state.handle, ops.convert_to_tensor(value.algorithm,
dtype=ALGORITHM_TYPE))
def _from_components(self, components):
assert isinstance(components, (list, tuple))
assert len(components) == 2
handle = components[0]
alg = components[1]
state_var = resource_variable_ops.BaseResourceVariable(
handle=handle, shape=self.shape, dtype=self.dtype,
trainable=False, handle_deleter=object(), handle_name="RNGVar")
return Generator(state=state_var, alg=alg)
@property
def value_type(self):
return Generator
def _serialize(self):
return (self.shape, self.dtype)
@tf_export("random.experimental.Generator")
class Generator(tracking.AutoTrackable, composite_tensor.CompositeTensor):
"""Random-number generator.
It uses Variable to manage its internal state, and allows choosing an
Random-Number-Generation (RNG) algorithm.
CPU, GPU and TPU with the same algorithm and seed will generate the same
integer random numbers. Float-point results (such as the output of `normal`)
may have small numerical discrepancies between CPU and GPU.
"""
def __init__(self, copy_from=None, state=None, alg=None):
"""Creates a generator.
The new generator will be initialized by one of the following ways, with
decreasing precedence:
(1) If `copy_from` is not None, the new generator is initialized by copying
information from another generator.
(3) If `state` and `alg` are not None (they must be set together), the new
generator is initialized by a state.
Args:
copy_from: a generator to be copied from.
state: a vector of dtype STATE_TYPE representing the initial state of the
RNG, whose length and semantics are algorithm-specific. If it's a
variable, the generator will reuse it instead of creating a new
variable.
alg: the RNG algorithm. Possible values are `RNG_ALG_PHILOX` for the
Philox algorithm and `RNG_ALG_THREEFRY` for the ThreeFry
algorithm (see paper 'Parallel Random Numbers: As Easy as 1, 2, 3'
[https://www.thesalmons.org/john/random123/papers/random123sc11.pdf]).
Note `RNG_ALG_PHILOX` guarantees the same numbers are produced (given
the same random state) across all architextures (CPU, GPU, XLA etc).
"""
if copy_from is not None:
# All other arguments should be None
assert (alg or state) is None
self._state_var = variables.Variable(copy_from.state, dtype=STATE_TYPE,
trainable=False)
self._alg = copy_from.algorithm
else:
assert alg is not None and state is not None
if isinstance(state, variables.Variable):
_check_state_shape(state.shape, alg)
self._state_var = state
else:
state = _convert_to_state_tensor(state)
_check_state_shape(state.shape, alg)
self._state_var = variables.Variable(state, dtype=STATE_TYPE,
trainable=False)
self._alg = alg
@classmethod
def from_state(cls, state, alg):
"""Creates a generator from a state.
See `__init__` for description of `state` and `alg`.
Args:
state: the new state.
alg: the RNG algorithm.
Returns:
The new generator.
"""
return cls(alg=alg, state=state)
@classmethod
def from_seed(cls, seed, alg=None):
"""Creates a generator from a seed.
A seed is a 1024-bit unsigned integer represented either as a Python
integer or a vector of integers. Seeds shorter than 1024-bit will be
padded. The padding, the internal structure of a seed and the way a seed
is converted to a state are all opaque (unspecified). The only semantics
specification of seeds is that two different seeds are likely to produce
two independent generators (but no guarantee).
Args:
seed: the seed for the RNG.
alg: (optional) the RNG algorithm. If None, it will be auto-selected. See
`__init__` for its possible values.
Returns:
The new generator.
"""
if alg is None:
# TODO(wangpeng): more sophisticated algorithm selection
alg = DEFAULT_ALGORITHM
state = create_rng_state(seed, alg)
return cls(state=state, alg=alg)
@classmethod
def from_non_deterministic_state(cls, alg=None):
"""Creates a generator by non-deterministically initializing its state.
The source of the non-determinism will be platform- and time-dependent.
Args:
alg: (optional) the RNG algorithm. If None, it will be auto-selected. See
`__init__` for its possible values.
Returns:
The new generator.
"""
if alg is None:
# TODO(wangpeng): more sophisticated algorithm selection
alg = DEFAULT_ALGORITHM
state = non_deterministic_ints(shape=[_get_state_size(alg)],
dtype=SEED_TYPE)
return cls(state=state, alg=alg)
@classmethod
def from_key_counter(cls, key, counter, alg):
"""Creates a generator from a key and a counter.
This constructor only applies if the algorithm is a counter-based algorithm.
See method `key` for the meaning of "key" and "counter".
Args:
key: the key for the RNG, a scalar of type STATE_TYPE.
counter: a vector of dtype STATE_TYPE representing the initial counter for
the RNG, whose length is algorithm-specific.,
alg: the RNG algorithm. If None, it will be auto-selected. See
`__init__` for its possible values.
Returns:
The new generator.
"""
counter = _convert_to_state_tensor(counter)
key = _convert_to_state_tensor(key)
counter.shape.assert_is_compatible_with([_get_state_size(alg) - 1])
key.shape.assert_is_compatible_with([])
key = array_ops.reshape(key, [1])
state = array_ops.concat([counter, key], 0)
return cls(state=state, alg=alg)
def reset(self, state):
"""Resets the generator by a new state.
See `__init__` for the meaning of "state".
Args:
state: the new state.
"""
state = _convert_to_state_tensor(state)
state.shape.assert_is_compatible_with([_get_state_size(self.algorithm)])
self._state_var.assign(state)
def reset_from_seed(self, seed):
"""Resets the generator by a new seed.
See `from_seed` for the meaning of "seed".
Args:
seed: the new seed.
"""
state = create_rng_state(seed, self.algorithm)
self._state_var.assign(state)
def reset_from_key_counter(self, key, counter):
"""Resets the generator by a new key-counter pair.
See `from_key_counter` for the meaning of "key" and "counter".
Args:
key: the new key.
counter: the new counter.
"""
counter = _convert_to_state_tensor(counter)
key = _convert_to_state_tensor(key)
counter.shape.assert_is_compatible_with(
[_get_state_size(self.algorithm) - 1])
key.shape.assert_is_compatible_with([])
key = array_ops.reshape(key, [1])
state = array_ops.concat([counter, key], 0)
self._state_var.assign(state)
@property
def _type_spec(self):
return GeneratorSpec(shape=self.state.shape, dtype=self.state.dtype)
@property
def state(self):
"""The internal state of the RNG."""
return self._state_var
@property
def algorithm(self):
"""The RNG algorithm."""
return self._alg
def _standard_normal(self, shape, dtype):
return gen_stateful_random_ops.stateful_standard_normal_v2(
self.state.handle, self.algorithm, shape, dtype=dtype)
@property
def key(self):
"""The 'key' part of the state of a counter-based RNG.
For a counter-base RNG algorithm such as Philox and ThreeFry (as
described in paper 'Parallel Random Numbers: As Easy as 1, 2, 3'
[https://www.thesalmons.org/john/random123/papers/random123sc11.pdf]),
the RNG state consists of two parts: counter and key. The output is
generated via the formula: output=hash(key, counter), i.e. a hashing of
the counter parametrized by the key. Two RNGs with two different keys can
be thought as generating two independent random-number streams (a stream
is formed by increasing the counter).
Returns:
A scalar which is the 'key' part of the state, if the RNG algorithm is
counter-based; otherwise it raises a ValueError.
"""
alg = self.algorithm
if alg == RNG_ALG_PHILOX or alg == RNG_ALG_THREEFRY:
return self._state_var[-1]
else:
raise ValueError("Unsupported algorithm id: %s" % alg)
def skip(self, delta):
"""Advance the counter of a counter-based RNG.
Args:
delta: the amount of advancement. The state of the RNG after
`skip(n)` will be the same as that after `normal([n])`
(or any other distribution). The actual increment added to the
counter is an unspecified implementation detail.
"""
gen_stateful_random_ops.rng_skip(self.state.handle, self.algorithm, delta)
# The following functions return a tensor and as a side effect update
# self._state_var.
def normal(self, shape, mean=0.0, stddev=1.0, dtype=dtypes.float32,
name=None):
"""Outputs random values from a normal distribution.
Args:
shape: A 1-D integer Tensor or Python array. The shape of the output
tensor.
mean: A 0-D Tensor or Python value of type `dtype`. The mean of the normal
distribution.
stddev: A 0-D Tensor or Python value of type `dtype`. The standard
deviation of the normal distribution.
dtype: The type of the output.
name: A name for the operation (optional).
Returns:
A tensor of the specified shape filled with random normal values.
"""
with ops.name_scope(name, "stateful_normal", [shape, mean, stddev]) as name:
shape = _shape_tensor(shape)
mean = ops.convert_to_tensor(mean, dtype=dtype, name="mean")
stddev = ops.convert_to_tensor(stddev, dtype=dtype, name="stddev")
rnd = self._standard_normal(shape, dtype=dtype)
return math_ops.add(rnd * stddev, mean, name=name)
def _truncated_normal(self, shape, dtype):
return gen_stateful_random_ops.stateful_truncated_normal(
self.state.handle, self.algorithm, shape, dtype=dtype)
def truncated_normal(self, shape,
mean=0.0,
stddev=1.0,
dtype=dtypes.float32,
name=None):
"""Outputs random values from a truncated normal distribution.
The generated values follow a normal distribution with specified mean and
standard deviation, except that values whose magnitude is more than
2 standard deviations from the mean are dropped and re-picked.
Args:
shape: A 1-D integer Tensor or Python array. The shape of the output
tensor.
mean: A 0-D Tensor or Python value of type `dtype`. The mean of the
truncated normal distribution.
stddev: A 0-D Tensor or Python value of type `dtype`. The standard
deviation of the normal distribution, before truncation.
dtype: The type of the output.
name: A name for the operation (optional).
Returns:
A tensor of the specified shape filled with random truncated normal
values.
"""
with ops.name_scope(
name, "truncated_normal", [shape, mean, stddev]) as name:
shape_tensor = _shape_tensor(shape)
mean_tensor = ops.convert_to_tensor(mean, dtype=dtype, name="mean")
stddev_tensor = ops.convert_to_tensor(stddev, dtype=dtype, name="stddev")
rnd = self._truncated_normal(shape_tensor, dtype=dtype)
mul = rnd * stddev_tensor
return math_ops.add(mul, mean_tensor, name=name)
def _uniform(self, shape, dtype):
return gen_stateful_random_ops.stateful_uniform(
self.state.handle, self.algorithm, shape=shape, dtype=dtype)
def uniform(self, shape, minval=0, maxval=None,
dtype=dtypes.float32, name=None):
"""Outputs random values from a uniform distribution.
The generated values follow a uniform distribution in the range
`[minval, maxval)`. The lower bound `minval` is included in the range, while
the upper bound `maxval` is excluded. (For float numbers especially
low-precision types like bfloat16, because of
rounding, the result may sometimes include `maxval`.)
For floats, the default range is `[0, 1)`. For ints, at least `maxval` must
be specified explicitly.
In the integer case, the random integers are slightly biased unless
`maxval - minval` is an exact power of two. The bias is small for values of
`maxval - minval` significantly smaller than the range of the output (either
`2**32` or `2**64`).
Args:
shape: A 1-D integer Tensor or Python array. The shape of the output
tensor.
minval: A 0-D Tensor or Python value of type `dtype`. The lower bound on
the range of random values to generate. Defaults to 0.
maxval: A 0-D Tensor or Python value of type `dtype`. The upper bound on
the range of random values to generate. Defaults to 1 if `dtype` is
floating point.
dtype: The type of the output.
name: A name for the operation (optional).
Returns:
A tensor of the specified shape filled with random uniform values.
Raises:
ValueError: If `dtype` is integral and `maxval` is not specified.
"""
dtype = dtypes.as_dtype(dtype)
if maxval is None:
if dtype.is_integer:
raise ValueError("Must specify maxval for integer dtype %r" % dtype)
maxval = 1
with ops.name_scope(name, "stateful_uniform",
[shape, minval, maxval]) as name:
shape = _shape_tensor(shape)
minval = ops.convert_to_tensor(minval, dtype=dtype, name="min")
maxval = ops.convert_to_tensor(maxval, dtype=dtype, name="max")
if dtype.is_integer:
return gen_stateful_random_ops.stateful_uniform_int(
self.state.handle, self.algorithm, shape=shape,
minval=minval, maxval=maxval, name=name)
else:
rnd = self._uniform(shape=shape, dtype=dtype)
return math_ops.add(rnd * (maxval - minval), minval, name=name)
def uniform_full_int(self, shape, dtype=dtypes.uint64, name=None):
"""Uniform distribution on an integer type's entire range.
The other method `uniform` only covers the range [minval, maxval), which
cannot be `dtype`'s full range because `maxval` is of type `dtype`.
Args:
shape: the shape of the output.
dtype: (optional) the integer type, default to uint64.
name: (optional) the name of the node.
Returns:
A tensor of random numbers of the required shape.
"""
dtype = dtypes.as_dtype(dtype)
with ops.name_scope(name, "stateful_uniform_full_int",
[shape]) as name:
shape = _shape_tensor(shape)
return gen_stateful_random_ops.stateful_uniform_full_int(
self.state.handle, self.algorithm, shape=shape,
dtype=dtype, name=name)
def binomial(self, shape, counts, probs, dtype=dtypes.int32, name=None):
"""Outputs random values from a binomial distribution.
The generated values follow a binomial distribution with specified count and
probability of success parameters.
Example:
```python
counts = [10., 20.]
# Probability of success.
probs = [0.8]
rng = tf.random.experimental.Generator.from_seed(seed=234)
binomial_samples = rng.binomial(shape=[2], counts=counts, probs=probs)
counts = ... # Shape [3, 1, 2]
probs = ... # Shape [1, 4, 2]
shape = [3, 4, 3, 4, 2]
rng = tf.random.experimental.Generator.from_seed(seed=1717)
# Sample shape will be [3, 4, 3, 4, 2]
binomial_samples = rng.binomial(shape=shape, counts=counts, probs=probs)
```
Args:
shape: A 1-D integer Tensor or Python array. The shape of the output
tensor.
counts: Tensor. The counts of the binomial distribution. Must be
broadcastable with `probs`, and broadcastable with the rightmost
dimensions of `shape`.
probs: Tensor. The probability of success for the
binomial distribution. Must be broadcastable with `counts` and
broadcastable with the rightmost dimensions of `shape`.
dtype: The type of the output. Default: tf.int32
name: A name for the operation (optional).
Returns:
samples: A Tensor of the specified shape filled with random binomial
values. For each i, each samples[i, ...] is an independent draw from
the binomial distribution on counts[i] trials with probability of
success probs[i].
"""
dtype = dtypes.as_dtype(dtype)
with ops.name_scope(name, "binomial", [shape, counts, probs]) as name:
counts = ops.convert_to_tensor(counts, name="counts")
probs = ops.convert_to_tensor(probs, name="probs")
shape_tensor = _shape_tensor(shape)
return gen_stateful_random_ops.stateful_random_binomial(
self.state.handle,
self.algorithm,
shape=shape_tensor,
counts=counts,
probs=probs,
dtype=dtype,
name=name)
# TODO(wangpeng): implement other distributions
def _make_int64_keys(self, shape=()):
# New independent keys are generated via
# `new_key[i] = hash(old_key, counter+i)`, which is exactly what
# `uniform_full_int(dtype=int64)` does for PhiloxRandom_64_128_128 and
# ThreeFry_64_64_64.
return self.uniform_full_int(shape=shape, dtype=dtypes.int64)
def make_seeds(self, count=1):
"""Generates seeds for stateless random ops.
For example:
```python
seeds = get_global_generator().make_seeds(count=10)
for i in range(10):
seed = seeds[:, i]
numbers = stateless_random_normal(shape=[2, 3], seed=seed)
...
```
Args:
count: the number of seed pairs (note that stateless random ops need a
pair of seeds to invoke).
Returns:
A tensor of shape [2, count] and dtype int64.
"""
alg = self.algorithm
if alg == RNG_ALG_PHILOX or alg == RNG_ALG_THREEFRY:
keys = self._make_int64_keys(shape=[count])
# The two seeds for stateless random ops don't have individual semantics
# and are scrambled together, so setting one to zero is fine.
zeros = array_ops.zeros_like(keys)
return array_ops.stack([keys, zeros])
else:
raise ValueError("Unsupported algorithm id: %s" % alg)
def split(self, count=1):
"""Returns a list of independent `Generator` objects.
Two generators are independent of each other in the sense that the
random-number streams they generate don't have statistically detectable
correlations. The new generators are also independent of the old one.
The old generator's state will be changed (like other random-number
generating methods), so two calls of `split` will return different
new generators.
For example:
```python
gens = get_global_generator().split(count=10)
for gen in gens:
numbers = gen.normal(shape=[2, 3])
# ...
gens2 = get_global_generator().split(count=10)
# gens2 will be different from gens
```
The new generators will be put on the current device (possible different
from the old generator's), for example:
```python
with tf.device("/device:CPU:0"):
gen = Generator(seed=1234) # gen is on CPU
with tf.device("/device:GPU:0"):
gens = gen.split(count=10) # gens are on GPU
```
Args:
count: the number of generators to return.
Returns:
A list (length `count`) of `Generator` objects independent of each other.
The new generators have the same RNG algorithm as the old one.
"""
def _key_to_state(alg, key):
# Padding with zeros on the left. The zeros will be the counter.
return [0] * (_get_state_size(alg) - 1) + [key]
alg = self.algorithm
if alg == RNG_ALG_PHILOX or alg == RNG_ALG_THREEFRY:
keys = self._make_int64_keys(shape=[count])
return [Generator(state=_key_to_state(alg, key), alg=alg)
for key in keys.numpy()]
else:
raise ValueError("Unsupported algorithm id: %s" % alg)
# It's not safe to create TF ops before `init_google` is called, so this is
# initialized to None and get a value the first time `get_global_generator` is
# called.
global_generator = None
@tf_export("random.experimental.get_global_generator")
def get_global_generator():
global global_generator
if global_generator is None:
with ops.init_scope():
global_generator = Generator.from_non_deterministic_state()
return global_generator
@tf_export("random.experimental.set_global_generator")
def set_global_generator(generator):
"""Replaces the global generator with another `Generator` object.
This function creates a new Generator object (and the Variable object within),
which does not work well with tf.function because (1) tf.function puts
restrictions on Variable creation thus reset_global_generator can't be freely
used inside tf.function; (2) redirecting a global variable to
a new object is problematic with tf.function because the old object may be
captured by a 'tf.function'ed function and still be used by it.
A 'tf.function'ed function only keeps weak references to variables,
so deleting a variable and then calling that function again may raise an
error, as demonstrated by
random_test.py/RandomTest.testResetGlobalGeneratorBadWithDefun .
Args:
generator: the new `Generator` object.
"""
global global_generator
global_generator = generator
Reference in New Issue View Git Blame Copy Permalink