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# Copyright 2018 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.
# ==============================================================================
"""Stateless random ops which take seed as a tensor input."""
import enum
import numpy as np
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import array_ops_stack
from tensorflow.python.ops import bitwise_ops
from tensorflow.python.ops import gen_random_index_shuffle_ops
from tensorflow.python.ops import gen_stateless_random_ops
from tensorflow.python.ops import gen_stateless_random_ops_v2
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import shape_util
from tensorflow.python.util import deprecation
from tensorflow.python.util import dispatch
from tensorflow.python.util.tf_export import tf_export
ops.NotDifferentiable("StatelessMultinomial")
ops.NotDifferentiable("StatelessRandomBinomial")
ops.NotDifferentiable("StatelessRandomNormal")
ops.NotDifferentiable("StatelessRandomPoisson")
ops.NotDifferentiable("StatelessRandomUniform")
ops.NotDifferentiable("StatelessRandomUniformInt")
ops.NotDifferentiable("StatelessRandomUniformFullInt")
ops.NotDifferentiable("StatelessTruncatedNormal")
ops.NotDifferentiable("StatelessRandomNormalV2")
ops.NotDifferentiable("StatelessRandomUniformV2")
ops.NotDifferentiable("StatelessRandomUniformIntV2")
ops.NotDifferentiable("StatelessRandomUniformFullIntV2")
ops.NotDifferentiable("StatelessTruncatedNormalV2")
ops.NotDifferentiable("StatelessRandomShuffle")
ops.NotDifferentiable("RandomIndexShuffle")
@tf_export("random.Algorithm", "random.experimental.Algorithm")
class Algorithm(enum.Enum):
"""A random-number-generation (RNG) algorithm.
Many random-number generators (e.g. the `alg` argument of
`tf.random.Generator` and `tf.random.stateless_uniform`) in TF allow
you to choose the algorithm used to generate the (pseudo-)random
numbers. You can set the algorithm to be one of the options below.
* `PHILOX`: The Philox algorithm introduced in the paper ["Parallel
Random Numbers: As Easy as 1, 2,
3"](https://www.thesalmons.org/john/random123/papers/random123sc11.pdf).
* `THREEFRY`: The ThreeFry algorithm introduced in the paper
["Parallel Random Numbers: As Easy as 1, 2,
3"](https://www.thesalmons.org/john/random123/papers/random123sc11.pdf).
* `AUTO_SELECT`: Allow TF to automatically select the algorithm
depending on the accelerator device. Note that with this option,
running the same TF program on different devices may result in
different random numbers. Also note that TF may select an
algorithm that is different from `PHILOX` and `THREEFRY`.
"""
# The numbers here must match framework/rng_alg.h
PHILOX = 1
THREEFRY = 2
AUTO_SELECT = 3
def unsupported_alg_error_msg(alg):
"""Produces the unsupported-algorithm error message."""
if isinstance(alg, int):
philox = Algorithm.PHILOX.value
threefry = Algorithm.THREEFRY.value
auto_select = Algorithm.AUTO_SELECT.value
elif isinstance(alg, str):
philox = "philox"
threefry = "threefry"
auto_select = "auto_select"
else:
philox = Algorithm.PHILOX
threefry = Algorithm.THREEFRY
auto_select = Algorithm.AUTO_SELECT
return (f"Argument `alg` got unsupported value {alg}. Supported values are "
f"{philox} for the Philox algorithm, "
f"{threefry} for the ThreeFry algorithm, and "
f"{auto_select} for auto-selection.")
def convert_alg_to_int(alg):
"""Converts algorithm to an integer.
Args:
alg: can be one of these types: integer, Algorithm, Tensor, string. Allowed
strings are "philox" and "threefry".
Returns:
An integer, unless the input is a Tensor in which case a Tensor is returned.
"""
if isinstance(alg, int):
return alg
if isinstance(alg, Algorithm):
return alg.value
if isinstance(alg, ops.Tensor):
return alg
if isinstance(alg, str):
# canonicalized alg
canon_alg = alg.strip().lower().replace("-", "").replace("_", "")
if canon_alg == "philox":
return Algorithm.PHILOX.value
elif canon_alg == "threefry":
return Algorithm.THREEFRY.value
elif canon_alg == "autoselect":
return Algorithm.AUTO_SELECT.value
else:
raise ValueError(unsupported_alg_error_msg(alg))
else:
raise TypeError(
f"Can't convert argument `alg` (of value {alg} and type {type(alg)}) "
f"to int.")
def _get_key_counter(seed, alg):
"""Calculates the key and counter to pass to raw RNG ops.
This function calculates the key and counter that will be passed to
the raw RNG ops like `StatelessRandomUniformV2`. Depending on the
input `alg`, the key and counter may be scrambled or copied from
`seed`. If `alg` is `"auto_select"`, the key and counter will be
determined at runtime based on device type.
Args:
seed: An integer tensor of shape [2]. The seed to calculate the
key and counter from.
alg: The RNG algorithm. See `tf.random.stateless_uniform` for an
explanation.
Returns:
A pair (key, counter) suitable for V2 stateless RNG ops like
`StatelessRandomUniformV2`.
"""
if alg == Algorithm.AUTO_SELECT.value:
key, counter = gen_stateless_random_ops_v2.stateless_random_get_key_counter(
seed)
elif alg == Algorithm.PHILOX.value:
key, counter = _philox_scramble_seed(seed)
elif alg == Algorithm.THREEFRY.value:
key = array_ops.reshape(
uint32s_to_uint64(math_ops.cast(seed, dtypes.uint32)), [1])
counter = array_ops.zeros([1], dtypes.uint64)
else:
raise ValueError(unsupported_alg_error_msg(alg))
return key, counter
def _get_key_counter_alg(seed, alg):
if alg is None:
alg = Algorithm.AUTO_SELECT.value
alg = convert_alg_to_int(alg)
key, counter = _get_key_counter(seed, alg)
return key, counter, alg
def _philox_scramble_seed(seed):
# the same scrambling procedure as core/kernels/stateless_random_ops.cc
key = constant_op.constant([0x02461e293ec8f720], dtypes.uint64)
counter = math_ops.cast(seed, dtypes.uint64)
mix = gen_stateless_random_ops_v2.stateless_random_uniform_full_int_v2(
[4], key=key, counter=counter, dtype=dtypes.uint32,
alg=Algorithm.PHILOX.value)
key = array_ops.reshape(uint32s_to_uint64(mix[:2]), [1])
counter = array_ops_stack.stack([0, uint32s_to_uint64(mix[2:])], axis=0)
return key, counter
def uint32s_to_uint64(x):
return bitwise_ops.bitwise_or(
math_ops.cast(x[0], dtypes.uint64),
bitwise_ops.left_shift(math_ops.cast(x[1], dtypes.uint64),
constant_op.constant(32, dtypes.uint64)))
@tf_export("random.split", "random.experimental.stateless_split")
@dispatch.add_dispatch_support
def split(seed, num=2, alg="auto_select"):
"""Splits an RNG seed into `num` new seeds by adding a leading axis.
Example:
>>> seed = [1, 2]
>>> new_seeds = tf.random.split(seed, num=3)
>>> print(new_seeds)
tf.Tensor(
[[1105988140 1738052849]
[-335576002 370444179]
[ 10670227 -246211131]], shape=(3, 2), dtype=int32)
>>> tf.random.stateless_normal(shape=[3], seed=new_seeds[0, :])
<tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.59835213, -0.9578608 ,
0.9002807 ], dtype=float32)>
Args:
seed: an RNG seed (a tensor with shape [2] and dtype `int32` or
`int64`). (When using XLA, only `int32` is allowed.)
num: optional, a positive integer or scalar tensor indicating the number of
seeds to produce (default 2).
alg: The RNG algorithm used to generate the random numbers. See
`tf.random.stateless_uniform` for a detailed explanation.
Returns:
A tensor with shape [num, 2] representing `num` new seeds. It will have the
same dtype as `seed` (if `seed` doesn't have an explict dtype, the dtype
will be determined by `tf.convert_to_tensor`).
"""
seed = ops.convert_to_tensor(seed)
return stateless_random_uniform(shape=[num, 2], seed=seed, dtype=seed.dtype,
minval=None, maxval=None, alg=alg)
@tf_export("random.fold_in", "random.experimental.stateless_fold_in")
@dispatch.add_dispatch_support
def fold_in(seed, data, alg="auto_select"):
"""Folds in data to an RNG seed to form a new RNG seed.
For example, in a distributed-training setting, suppose we have a master seed
and a replica ID. We want to fold the replica ID into the master seed to
form a "replica seed" to be used by that replica later on, so that different
replicas will generate different random numbers but the reproducibility of the
whole system can still be controlled by the master seed:
>>> master_seed = [1, 2]
>>> replica_id = 3
>>> replica_seed = tf.random.experimental.stateless_fold_in(
... master_seed, replica_id)
>>> print(replica_seed)
tf.Tensor([1105988140 3], shape=(2,), dtype=int32)
>>> tf.random.stateless_normal(shape=[3], seed=replica_seed)
<tf.Tensor: shape=(3,), dtype=float32, numpy=array([0.03197195, 0.8979765 ,
0.13253039], dtype=float32)>
Args:
seed: an RNG seed (a tensor with shape [2] and dtype `int32` or
`int64`). (When using XLA, only `int32` is allowed.)
data: an `int32` or `int64` scalar representing data to be folded in to the
seed.
alg: The RNG algorithm used to generate the random numbers. See
`tf.random.stateless_uniform` for a detailed explanation.
Returns:
A new RNG seed that is a deterministic function of the inputs and is
statistically safe for producing a stream of new pseudo-random values. It
will have the same dtype as `data` (if `data` doesn't have an explict dtype,
the dtype will be determined by `tf.convert_to_tensor`).
"""
data = ops.convert_to_tensor(data)
seed1 = stateless_random_uniform(shape=[], seed=seed, dtype=data.dtype,
minval=None, maxval=None, alg=alg)
return array_ops_stack.stack([seed1, data])
@tf_export("random.experimental.index_shuffle")
@dispatch.add_dispatch_support
def index_shuffle(index, seed, max_index):
"""Outputs the position of `index` in a permutation of `[0, ..., max_index]`.
For each possible `seed` and `max_index` there is one pseudorandom
permutation of the sequence `S=[0, ..., max_index]`. Instead of
materializing the full array we can compute the new position of any
integer `i` (`0 <= i <= max_index`) in `S`. This can be useful for
very large `max_index`s by avoiding allocating large chunks of
memory.
In the simplest case, `index` and `max_index` are scalars, and
`seed` is a length-2 vector (as typical for stateless RNGs). But
you can add a leading batch dimension to all of them. If some of
them don't have the batch dimension while others do, `index_shuffle`
will add a batch dimension to the former by broadcasting.
The input `index` and output can be used as indices to shuffle a
vector. For example:
>>> vector = tf.constant(['e0', 'e1', 'e2', 'e3'])
>>> indices = tf.random.experimental.index_shuffle(
... index=tf.range(4), seed=[5, 9], max_index=3)
>>> print(indices)
tf.Tensor([2 0 1 3], shape=(4,), dtype=int32)
>>> shuffled_vector = tf.gather(vector, indices)
>>> print(shuffled_vector)
tf.Tensor([b'e2' b'e0' b'e1' b'e3'], shape=(4,), dtype=string)
More usefully, it can be used in a streaming (aka online) scenario such as
`tf.data`, where each element of `vector` is processed individually and the
whole `vector` is never materialized in memory.
>>> dataset = tf.data.Dataset.range(10)
>>> dataset = dataset.map(
... lambda idx: tf.random.experimental.index_shuffle(idx, [5, 8], 9))
>>> print(list(dataset.as_numpy_iterator()))
[3, 8, 0, 1, 2, 7, 6, 9, 4, 5]
This operation is stateless (like the `tf.random.stateless_*`
functions), meaning the output is fully determined by the `seed`
(other inputs being equal). Each `seed` choice corresponds to one
permutation, so when calling this function multiple times for the
same shuffling, please make sure to use the same `seed`. For
example:
>>> seed = [5, 9]
>>> idx0 = tf.random.experimental.index_shuffle(0, seed, 3)
>>> idx1 = tf.random.experimental.index_shuffle(1, seed, 3)
>>> idx2 = tf.random.experimental.index_shuffle(2, seed, 3)
>>> idx3 = tf.random.experimental.index_shuffle(3, seed, 3)
>>> shuffled_vector = tf.gather(vector, [idx0, idx1, idx2, idx3])
>>> print(shuffled_vector)
tf.Tensor([b'e2' b'e0' b'e1' b'e3'], shape=(4,), dtype=string)
Args:
index: An integer scalar tensor or vector with values in `[0,
max_index]`. It can be seen as either a value `v` in the
sequence `S=[0, ..., max_index]` to be permutated, or as an
index of an element `e` in a shuffled vector.
seed: A tensor of shape [2] or [n, 2] with dtype `int32`,
`uint32`, `int64` or `uint64`. The RNG seed. If the rank is
unknown during graph-building time it must be 1 at runtime.
max_index: A non-negative tensor with the same shape and dtype as
`index`. The upper bound (inclusive).
Returns:
If all inputs were scalar (shape [2] for `seed`), the output will
be a scalar with the same dtype as `index`. The output can be seen
as the new position of `v` in `S`, or as the index of `e` in the
vector before shuffling. If one or multiple inputs were vectors
(shape [n, 2] for `seed`), then the output will be a vector of the
same size which each element shuffled independently. Scalar values
are broadcasted in this case.
"""
# We expect users to pass a seed with shape [2] to be consistent with other
# stateless_* ops, but the raw op expects shape [3].
seed = ops.convert_to_tensor(seed)
# Pad the first dimension with an arbitrary number since our raw op expects
# shape [3].
if seed.shape.rank is None:
paddings = [[1, 0]]
else:
paddings = [[1, 0]] + (seed.shape.rank - 1) * [[0, 0]]
seed = array_ops.pad(seed, paddings, constant_values=498247692)
return gen_random_index_shuffle_ops.random_index_shuffle(
index, seed=seed, max_index=max_index, rounds=4)
@tf_export("random.experimental.stateless_shuffle")
@dispatch.add_dispatch_support
def stateless_shuffle(value, seed, alg="auto_select", name=None):
"""Randomly and deterministically shuffles a tensor along its first dimension.
The tensor is shuffled along dimension 0, such that each `value[j]` is mapped
to one and only one `output[i]`. For example, a mapping that might occur for a
3x2 tensor is:
```python
[[1, 2], [[5, 6],
[3, 4], ==> [1, 2],
[5, 6]] [3, 4]]
```
>>> v = tf.constant([[1, 2], [3, 4], [5, 6]])
>>> shuffled = tf.random.experimental.stateless_shuffle(v, seed=[8, 9])
>>> print(shuffled)
tf.Tensor(
[[5 6]
[1 2]
[3 4]], shape=(3, 2), dtype=int32)
This is a stateless version of `tf.random.shuffle`: if run twice with the
same `value` and `seed`, it will produce the same result. The
output is consistent across multiple runs on the same hardware (and between
CPU and GPU), but may change between versions of TensorFlow or on non-CPU/GPU
hardware.
Args:
value: A Tensor to be shuffled.
seed: A shape [2] Tensor. The seed to the random number generator. Must have
dtype `int32` or `int64`.
alg: The RNG algorithm used to generate the random numbers. See
`tf.random.stateless_uniform` for a detailed explanation.
name: A name for the operation.
Returns:
A tensor of same shape and type as `value`, shuffled along its first
dimension.
"""
with ops.name_scope(name, "stateless_shuffle", [value, seed]) as name:
key, counter, alg = _get_key_counter_alg(seed, alg)
return gen_stateless_random_ops_v2.stateless_shuffle(
value, key=key, counter=counter, alg=alg)
@tf_export("random.stateless_uniform")
@dispatch.add_dispatch_support
def stateless_random_uniform(shape,
seed,
minval=0,
maxval=None,
dtype=dtypes.float32,
name=None,
alg="auto_select"):
"""Outputs deterministic pseudorandom values from a uniform distribution.
This is a stateless version of `tf.random.uniform`: if run twice with the
same seeds and shapes, it will produce the same pseudorandom numbers. The
output is consistent across multiple runs on the same hardware (and between
CPU and GPU), but may change between versions of TensorFlow or on non-CPU/GPU
hardware.
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 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`).
For full-range (i.e. inclusive of both max and min) random integers, pass
`minval=None` and `maxval=None` with an integer `dtype`. For an integer dtype
either both `minval` and `maxval` must be `None` or neither may be `None`. For
example:
```python
ints = tf.random.stateless_uniform(
[10], seed=(2, 3), minval=None, maxval=None, dtype=tf.int32)
```
Args:
shape: A 1-D integer Tensor or Python array. The shape of the output tensor.
seed: A shape [2] Tensor, the seed to the random number generator. Must have
dtype `int32` or `int64`. (When using XLA, only `int32` is allowed.)
minval: A Tensor or Python value of type `dtype`, broadcastable with
`shape` (for integer types, broadcasting is not supported, so it needs to
be a scalar). The lower bound on the range of random values to
generate. Pass `None` for full-range integers. Defaults to 0.
maxval: A Tensor or Python value of type `dtype`, broadcastable with
`shape` (for integer types, broadcasting is not supported, so it needs to
be a scalar). The upper bound on the range of random values to generate.
Defaults to 1 if `dtype` is floating point. Pass `None` for full-range
integers.
dtype: The type of the output: `float16`, `bfloat16`, `float32`, `float64`,
`int32`, or `int64`. For unbounded uniform ints (`minval`, `maxval` both
`None`), `uint32` and `uint64` may be used. Defaults to `float32`.
name: A name for the operation (optional).
alg: The RNG algorithm used to generate the random numbers. Valid
choices are `"philox"` for [the Philox
algorithm](https://www.thesalmons.org/john/random123/papers/random123sc11.pdf),
`"threefry"` for [the ThreeFry
algorithm](https://www.thesalmons.org/john/random123/papers/random123sc11.pdf),
and `"auto_select"` (default) for the system to automatically
select an algorithm based the device type. Values of
`tf.random.Algorithm` can also be used. Note that with
`"auto_select"`, the outputs of this function may change when
it is running on a different device.
Returns:
A tensor of the specified shape filled with random uniform values.
Raises:
ValueError: If `dtype` is integral and only one of `minval` or `maxval` is
specified.
"""
dtype = dtypes.as_dtype(dtype)
accepted_dtypes = (dtypes.float16, dtypes.bfloat16, dtypes.float32,
dtypes.float64, dtypes.int32, dtypes.int64, dtypes.uint32,
dtypes.uint64)
if dtype not in accepted_dtypes:
raise ValueError(
f"Argument `dtype` got invalid value {dtype}. Accepted dtypes are "
f"{accepted_dtypes}.")
if dtype.is_integer:
if (minval is None) != (maxval is None):
raise ValueError(
f"For integer `dtype` argument {dtype}, argument `minval` and "
f"`maxval` must be both None or not None. Got `minval`={minval} and "
f"`maxval`={maxval}.")
if minval is not None and dtype in (dtypes.uint32, dtypes.uint64):
raise ValueError(
f"Argument `dtype` got invalid value {dtype} when argument `minval` "
f"is not None. Please don't use unsigned integers in this case.")
elif maxval is None:
maxval = 1
with ops.name_scope(name, "stateless_random_uniform",
[shape, seed, minval, maxval]) as name:
shape = shape_util.shape_tensor(shape)
if dtype.is_integer and minval is None:
key, counter, alg = _get_key_counter_alg(seed, alg)
result = (
gen_stateless_random_ops_v2.stateless_random_uniform_full_int_v2(
shape, key=key, counter=counter, dtype=dtype, alg=alg, name=name))
else:
minval = ops.convert_to_tensor(minval, dtype=dtype, name="min")
maxval = ops.convert_to_tensor(maxval, dtype=dtype, name="max")
if dtype.is_integer:
key, counter, alg = _get_key_counter_alg(seed, alg)
result = gen_stateless_random_ops_v2.stateless_random_uniform_int_v2(
shape,
key=key,
counter=counter,
minval=minval,
maxval=maxval,
alg=alg,
name=name)
else:
key, counter, alg = _get_key_counter_alg(seed, alg)
rnd = gen_stateless_random_ops_v2.stateless_random_uniform_v2(
shape, key=key, counter=counter, dtype=dtype, alg=alg)
result = math_ops.add(rnd * (maxval - minval), minval, name=name)
shape_util.maybe_set_static_shape(result, shape)
return result
@tf_export("random.stateless_binomial")
@dispatch.add_dispatch_support
def stateless_random_binomial(shape,
seed,
counts,
probs,
output_dtype=dtypes.int32,
name=None):
"""Outputs deterministic pseudorandom values from a binomial distribution.
The generated values follow a binomial distribution with specified count and
probability of success parameters.
This is a stateless version of `tf.random.Generator.binomial`: if run twice
with the same seeds and shapes, it will produce the same pseudorandom numbers.
The output is consistent across multiple runs on the same hardware (and
between CPU and GPU), but may change between versions of TensorFlow or on
non-CPU/GPU hardware.
Example:
```python
counts = [10., 20.]
# Probability of success.
probs = [0.8]
binomial_samples = tf.random.stateless_binomial(
shape=[2], seed=[123, 456], counts=counts, probs=probs)
counts = ... # Shape [3, 1, 2]
probs = ... # Shape [1, 4, 2]
shape = [3, 4, 3, 4, 2]
# Sample shape will be [3, 4, 3, 4, 2]
binomial_samples = tf.random.stateless_binomial(
shape=shape, seed=[123, 456], counts=counts, probs=probs)
```
Args:
shape: A 1-D integer Tensor or Python array. The shape of the output tensor.
seed: A shape [2] Tensor, the seed to the random number generator. Must have
dtype `int32` or `int64`. (When using XLA, only `int32` is allowed.)
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`.
output_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].
"""
with ops.name_scope(name, "stateless_random_binomial",
[shape, seed, counts, probs]) as name:
shape = shape_util.shape_tensor(shape)
probs = ops.convert_to_tensor(
probs, dtype_hint=dtypes.float32, name="probs")
counts = ops.convert_to_tensor(
counts, dtype_hint=probs.dtype, name="counts")
result = gen_stateless_random_ops.stateless_random_binomial(
shape=shape, seed=seed, counts=counts, probs=probs, dtype=output_dtype)
shape_util.maybe_set_static_shape(result, shape)
return result
@tf_export("random.stateless_gamma")
@dispatch.add_dispatch_support
def stateless_random_gamma(shape,
seed,
alpha,
beta=None,
dtype=dtypes.float32,
name=None):
"""Outputs deterministic pseudorandom values from a gamma distribution.
The generated values follow a gamma distribution with specified concentration
(`alpha`) and inverse scale (`beta`) parameters.
This is a stateless version of `tf.random.gamma`: if run twice with the same
seeds and shapes, it will produce the same pseudorandom numbers. The output is
consistent across multiple runs on the same hardware (and between CPU and
GPU),
but may change between versions of TensorFlow or on non-CPU/GPU hardware.
A slight difference exists in the interpretation of the `shape` parameter
between `stateless_gamma` and `gamma`: in `gamma`, the `shape` is always
prepended to the shape of the broadcast of `alpha` with `beta`; whereas in
`stateless_gamma` the `shape` parameter must always encompass the shapes of
each of `alpha` and `beta` (which must broadcast together to match the
trailing dimensions of `shape`).
Note: Because internal calculations are done using `float64` and casting has
`floor` semantics, we must manually map zero outcomes to the smallest
possible positive floating-point value, i.e., `np.finfo(dtype).tiny`. This
means that `np.finfo(dtype).tiny` occurs more frequently than it otherwise
should. This bias can only happen for small values of `alpha`, i.e.,
`alpha << 1` or large values of `beta`, i.e., `beta >> 1`.
The samples are differentiable w.r.t. alpha and beta.
The derivatives are computed using the approach described in
(Figurnov et al., 2018).
Example:
```python
samples = tf.random.stateless_gamma([10, 2], seed=[12, 34], alpha=[0.5, 1.5])
# samples has shape [10, 2], where each slice [:, 0] and [:, 1] represents
# the samples drawn from each distribution
samples = tf.random.stateless_gamma([7, 5, 2], seed=[12, 34], alpha=[.5, 1.5])
# samples has shape [7, 5, 2], where each slice [:, :, 0] and [:, :, 1]
# represents the 7x5 samples drawn from each of the two distributions
alpha = tf.constant([[1.], [3.], [5.]])
beta = tf.constant([[3., 4.]])
samples = tf.random.stateless_gamma(
[30, 3, 2], seed=[12, 34], alpha=alpha, beta=beta)
# samples has shape [30, 3, 2], with 30 samples each of 3x2 distributions.
with tf.GradientTape() as tape:
tape.watch([alpha, beta])
loss = tf.reduce_mean(tf.square(tf.random.stateless_gamma(
[30, 3, 2], seed=[12, 34], alpha=alpha, beta=beta)))
dloss_dalpha, dloss_dbeta = tape.gradient(loss, [alpha, beta])
# unbiased stochastic derivatives of the loss function
alpha.shape == dloss_dalpha.shape # True
beta.shape == dloss_dbeta.shape # True
```
Args:
shape: A 1-D integer Tensor or Python array. The shape of the output tensor.
seed: A shape [2] Tensor, the seed to the random number generator. Must have
dtype `int32` or `int64`. (When using XLA, only `int32` is allowed.)
alpha: Tensor. The concentration parameter of the gamma distribution. Must
be broadcastable with `beta`, and broadcastable with the rightmost
dimensions of `shape`.
beta: Tensor. The inverse scale parameter of the gamma distribution. Must be
broadcastable with `alpha` and broadcastable with the rightmost dimensions
of `shape`.
dtype: Floating point dtype of `alpha`, `beta`, and the output.
name: A name for the operation (optional).
Returns:
samples: A Tensor of the specified shape filled with random gamma values.
For each i, each `samples[..., i] is an independent draw from the gamma
distribution with concentration alpha[i] and scale beta[i].
"""
with ops.name_scope(name, "stateless_random_gamma",
[shape, seed, alpha, beta]) as name:
shape = shape_util.shape_tensor(shape)
alpha = ops.convert_to_tensor(alpha, dtype=dtype, name="alpha")
beta = ops.convert_to_tensor(
beta if beta is not None else 1, name="beta", dtype=dtype)
broadcast_shape = array_ops.broadcast_dynamic_shape(
array_ops.shape(alpha), array_ops.shape(beta))
alpha_broadcast = array_ops.broadcast_to(alpha, broadcast_shape)
alg = "auto_select"
key, counter, alg = _get_key_counter_alg(seed, alg)
rnd = gen_stateless_random_ops_v2.stateless_random_gamma_v3(
shape, key=key, counter=counter, alg=alg, alpha=alpha_broadcast)
result = math_ops.maximum(
np.finfo(alpha.dtype.as_numpy_dtype).tiny, rnd / beta)
shape_util.maybe_set_static_shape(result, shape)
return result
@tf_export("random.stateless_poisson")
@dispatch.add_dispatch_support
def stateless_random_poisson(shape,
seed,
lam,
dtype=dtypes.int32,
name=None):
"""Outputs deterministic pseudorandom values from a Poisson distribution.
The generated values follow a Poisson distribution with specified rate
parameter.
This is a stateless version of `tf.random.poisson`: if run twice with the same
seeds and shapes, it will produce the same pseudorandom numbers. The output is
consistent across multiple runs on the same hardware, but may change between
versions of TensorFlow or on non-CPU/GPU hardware.
A slight difference exists in the interpretation of the `shape` parameter
between `stateless_poisson` and `poisson`: in `poisson`, the `shape` is always
prepended to the shape of `lam`; whereas in `stateless_poisson` the shape of
`lam` must match the trailing dimensions of `shape`.
Example:
```python
samples = tf.random.stateless_poisson([10, 2], seed=[12, 34], lam=[5, 15])
# samples has shape [10, 2], where each slice [:, 0] and [:, 1] represents
# the samples drawn from each distribution
samples = tf.random.stateless_poisson([7, 5, 2], seed=[12, 34], lam=[5, 15])
# samples has shape [7, 5, 2], where each slice [:, :, 0] and [:, :, 1]
# represents the 7x5 samples drawn from each of the two distributions
rate = tf.constant([[1.], [3.], [5.]])
samples = tf.random.stateless_poisson([30, 3, 1], seed=[12, 34], lam=rate)
# samples has shape [30, 3, 1], with 30 samples each of 3x1 distributions.
```
Args:
shape: A 1-D integer Tensor or Python array. The shape of the output tensor.
seed: A shape [2] Tensor, the seed to the random number generator. Must have
dtype `int32` or `int64`. (When using XLA, only `int32` is allowed.)
lam: Tensor. The rate parameter "lambda" of the Poisson distribution. Shape
must match the rightmost dimensions of `shape`.
dtype: Dtype of the samples (int or float dtypes are permissible, as samples
are discrete). Default: int32.
name: A name for the operation (optional).
Returns:
samples: A Tensor of the specified shape filled with random Poisson values.
For each i, each `samples[..., i]` is an independent draw from the Poisson
distribution with rate `lam[i]`.
"""
with ops.name_scope(name, "stateless_random_poisson",
[shape, seed, lam]) as name:
shape = shape_util.shape_tensor(shape)
result = gen_stateless_random_ops.stateless_random_poisson(
shape, seed=seed, lam=lam, dtype=dtype)
shape_util.maybe_set_static_shape(result, shape)
return result
@tf_export("random.stateless_normal")
@dispatch.add_dispatch_support
def stateless_random_normal(shape,
seed,
mean=0.0,
stddev=1.0,
dtype=dtypes.float32,
name=None,
alg="auto_select"):
"""Outputs deterministic pseudorandom values from a normal distribution.
This is a stateless version of `tf.random.normal`: if run twice with the
same seeds and shapes, it will produce the same pseudorandom numbers. The
output is consistent across multiple runs on the same hardware (and between
CPU and GPU), but may change between versions of TensorFlow or on non-CPU/GPU
hardware.
Args:
shape: A 1-D integer Tensor or Python array. The shape of the output tensor.
seed: A shape [2] Tensor, the seed to the random number generator. Must have
dtype `int32` or `int64`. (When using XLA, only `int32` is allowed.)
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 float type of the output: `float16`, `bfloat16`, `float32`,
`float64`. Defaults to `float32`.
name: A name for the operation (optional).
alg: The RNG algorithm used to generate the random numbers. See
`tf.random.stateless_uniform` for a detailed explanation.
Returns:
A tensor of the specified shape filled with random normal values.
"""
with ops.name_scope(name, "stateless_random_normal",
[shape, seed, mean, stddev]) as name:
shape = shape_util.shape_tensor(shape)
mean = ops.convert_to_tensor(mean, dtype=dtype, name="mean")
stddev = ops.convert_to_tensor(stddev, dtype=dtype, name="stddev")
key, counter, alg = _get_key_counter_alg(seed, alg)
rnd = gen_stateless_random_ops_v2.stateless_random_normal_v2(
shape, key=key, counter=counter, dtype=dtype, alg=alg)
result = math_ops.add(rnd * stddev, mean, name=name)
shape_util.maybe_set_static_shape(result, shape)
return result
@tf_export("random.stateless_truncated_normal")
@dispatch.add_dispatch_support
def stateless_truncated_normal(shape,
seed,
mean=0.0,
stddev=1.0,
dtype=dtypes.float32,
name=None,
alg="auto_select"):
"""Outputs deterministic pseudorandom values, truncated normally distributed.
This is a stateless version of `tf.random.truncated_normal`: if run twice with
the same seeds and shapes, it will produce the same pseudorandom numbers. The
output is consistent across multiple runs on the same hardware (and between
CPU and GPU), but may change between versions of TensorFlow or on non-CPU/GPU
hardware.
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.
seed: A shape [2] Tensor, the seed to the random number generator. Must have
dtype `int32` or `int64`. (When using XLA, only `int32` is allowed.)
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).
alg: The RNG algorithm used to generate the random numbers. See
`tf.random.stateless_uniform` for a detailed explanation.
Returns:
A tensor of the specified shape filled with random truncated normal values.
"""
with ops.name_scope(name, "stateless_truncated_normal",
[shape, seed, mean, stddev]) as name:
shape = shape_util.shape_tensor(shape)
mean = ops.convert_to_tensor(mean, dtype=dtype, name="mean")
stddev = ops.convert_to_tensor(stddev, dtype=dtype, name="stddev")
key, counter, alg = _get_key_counter_alg(seed, alg)
rnd = gen_stateless_random_ops_v2.stateless_truncated_normal_v2(
shape, key=key, counter=counter, dtype=dtype, alg=alg)
result = math_ops.add(rnd * stddev, mean, name=name)
shape_util.maybe_set_static_shape(result, shape)
return result
@tf_export(v1=["random.stateless_multinomial"])
@dispatch.add_dispatch_support
@deprecation.deprecated(
date=None, instructions="Use `tf.random.stateless_categorical` instead.")
def stateless_multinomial(logits,
num_samples,
seed,
output_dtype=dtypes.int64,
name=None):
"""Draws deterministic pseudorandom samples from a multinomial distribution.
This is a stateless version of `tf.random.categorical`: if run twice with the
same seeds and shapes, it will produce the same pseudorandom numbers. The
output is consistent across multiple runs on the same hardware (and between
CPU and GPU), but may change between versions of TensorFlow or on non-CPU/GPU
hardware.
Example:
```python
# samples has shape [1, 5], where each value is either 0 or 1 with equal
# probability.
samples = tf.random.stateless_categorical(
tf.math.log([[0.5, 0.5]]), 5, seed=[7, 17])
```
Args:
logits: 2-D Tensor with shape `[batch_size, num_classes]`. Each slice
`[i, :]` represents the unnormalized log-probabilities for all classes.
num_samples: 0-D. Number of independent samples to draw for each row slice.
seed: A shape [2] Tensor, the seed to the random number generator. Must have
dtype `int32` or `int64`. (When using XLA, only `int32` is allowed.)
output_dtype: The integer type of the output: `int32` or `int64`. Defaults
to `int64`.
name: Optional name for the operation.
Returns:
The drawn samples of shape `[batch_size, num_samples]`.
"""
with ops.name_scope(name, "stateless_multinomial", [logits, seed]):
return stateless_multinomial_categorical_impl(logits, num_samples,
output_dtype, seed)
@tf_export("random.stateless_categorical")
@dispatch.add_dispatch_support
def stateless_categorical(logits,
num_samples,
seed,
dtype=dtypes.int64,
name=None):
"""Draws deterministic pseudorandom samples from a categorical distribution.
This is a stateless version of `tf.categorical`: if run twice with the
same seeds and shapes, it will produce the same pseudorandom numbers. The
output is consistent across multiple runs on the same hardware (and between
CPU and GPU), but may change between versions of TensorFlow or on non-CPU/GPU
hardware.
Example:
```python
# samples has shape [1, 5], where each value is either 0 or 1 with equal
# probability.
samples = tf.random.stateless_categorical(
tf.math.log([[0.5, 0.5]]), 5, seed=[7, 17])
```
Args:
logits: 2-D Tensor with shape `[batch_size, num_classes]`. Each slice
`[i, :]` represents the unnormalized log-probabilities for all classes.
num_samples: 0-D. Number of independent samples to draw for each row slice.
seed: A shape [2] Tensor, the seed to the random number generator. Must have
dtype `int32` or `int64`. (When using XLA, only `int32` is allowed.)
dtype: The integer type of the output: `int32` or `int64`. Defaults to
`int64`.
name: Optional name for the operation.
Returns:
The drawn samples of shape `[batch_size, num_samples]`.
"""
with ops.name_scope(name, "stateless_categorical", [logits, seed]):
return stateless_multinomial_categorical_impl(logits, num_samples, dtype,
seed)
def stateless_multinomial_categorical_impl(logits, num_samples, dtype, seed):
"""Implementation for stateless multinomial/categorical ops (v1/v2)."""
logits = ops.convert_to_tensor(logits, name="logits")
dtype = dtypes.as_dtype(dtype) if dtype else dtypes.int64
accepted_dtypes = (dtypes.int32, dtypes.int64)
if dtype not in accepted_dtypes:
raise ValueError(
f"Argument `dtype` got invalid value {dtype}. Accepted dtypes are "
f"{accepted_dtypes}.")
return gen_stateless_random_ops.stateless_multinomial(
logits, num_samples, seed, output_dtype=dtype)
@dispatch.add_dispatch_support
@tf_export("random.stateless_parameterized_truncated_normal")
def stateless_parameterized_truncated_normal(shape,
seed,
means=0.0,
stddevs=1.0,
minvals=-2.0,
maxvals=2.0,
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.
Examples:
Sample from a Truncated normal, with deferring shape parameters that
broadcast.
>>> means = 0.
>>> stddevs = tf.math.exp(tf.random.uniform(shape=[2, 3]))
>>> minvals = [-1., -2., -1000.]
>>> maxvals = [[10000.], [1.]]
>>> y = tf.random.stateless_parameterized_truncated_normal(
... shape=[10, 2, 3], seed=[7, 17],
... means=means, stddevs=stddevs, minvals=minvals, maxvals=maxvals)
>>> y.shape
TensorShape([10, 2, 3])
Args:
shape: A 1-D integer `Tensor` or Python array. The shape of the output
tensor.
seed: A shape [2] Tensor, the seed to the random number generator. Must have
dtype `int32` or `int64`. (When using XLA, only `int32` is allowed.)
means: A `Tensor` or Python value of type `dtype`. The mean of the truncated
normal distribution. This must broadcast with `stddevs`, `minvals` and
`maxvals`, and the broadcasted shape must be dominated by `shape`.
stddevs: A `Tensor` or Python value of type `dtype`. The standard deviation
of the truncated normal distribution. This must broadcast with `means`,
`minvals` and `maxvals`, and the broadcasted shape must be dominated by
`shape`.
minvals: A `Tensor` or Python value of type `dtype`. The minimum value of
the truncated normal distribution. This must broadcast with `means`,
`stddevs` and `maxvals`, and the broadcasted shape must be dominated by
`shape`.
maxvals: A `Tensor` or Python value of type `dtype`. The maximum value of
the truncated normal distribution. This must broadcast with `means`,
`stddevs` and `minvals`, and the broadcasted shape must be dominated by
`shape`.
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, "stateless_parameterized_truncated_normal",
[shape, means, stddevs, minvals, maxvals]) as name:
shape_tensor = shape_util.shape_tensor(shape)
means_tensor = ops.convert_to_tensor(means, name="means")
stddevs_tensor = ops.convert_to_tensor(stddevs, name="stddevs")
minvals_tensor = ops.convert_to_tensor(minvals, name="minvals")
maxvals_tensor = ops.convert_to_tensor(maxvals, name="maxvals")
rnd = gen_stateless_random_ops.stateless_parameterized_truncated_normal(
shape_tensor, seed, means_tensor, stddevs_tensor, minvals_tensor,
maxvals_tensor)
shape_util.maybe_set_static_shape(rnd, shape)
return rnd