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TensorFlow provides a set of pseudo-random number generators (RNG), in the tf.random module. This document describes how you can control the random number generators, and how these generators interact with other tensorflow sub-systems.
TensorFlow provides two approaches for controlling the random number generation process:
Through the explicit use of
tf.random.Generatorobjects. Each such object maintains a state (intf.Variable) that will be changed after each number generation.Through the purely-functional stateless random functions like
tf.random.stateless_uniform. Calling these functions with the same arguments (which include the seed) and on the same device will always produce the same results.
Setup
import tensorflow as tf
# Creates some virtual devices (cpu:0, cpu:1, etc.) for using distribution strategy
physical_devices = tf.config.list_physical_devices("CPU")
tf.config.experimental.set_virtual_device_configuration(
physical_devices[0], [
tf.config.experimental.VirtualDeviceConfiguration(),
tf.config.experimental.VirtualDeviceConfiguration(),
tf.config.experimental.VirtualDeviceConfiguration()
])
2024-08-15 01:43:41.157432: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered 2024-08-15 01:43:41.178819: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered 2024-08-15 01:43:41.185039: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered WARNING: All log messages before absl::InitializeLog() is called are written to STDERR I0000 00:00:1723686223.758551 55391 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723686223.762466 55391 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723686223.765643 55391 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723686223.769228 55391 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723686223.780976 55391 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723686223.784469 55391 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723686223.787369 55391 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723686223.790779 55391 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723686223.794250 55391 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723686223.797851 55391 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723686223.800627 55391 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723686223.804177 55391 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
The tf.random.Generator class
The tf.random.Generator class is used in cases where you want each RNG call to produce different results. It maintains an internal state (managed by a tf.Variable object) which will be updated every time random numbers are generated. Because the state is managed by tf.Variable, it enjoys all facilities provided by tf.Variable such as easy checkpointing, automatic control-dependency and thread safety.
You can get a tf.random.Generator by manually creating an object of the class or call tf.random.get_global_generator() to get the default global generator:
g1 = tf.random.Generator.from_seed(1)
print(g1.normal(shape=[2, 3]))
g2 = tf.random.get_global_generator()
print(g2.normal(shape=[2, 3]))
I0000 00:00:1723686225.040869 55391 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723686225.043016 55391 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723686225.045041 55391 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723686225.047115 55391 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723686225.049184 55391 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723686225.051188 55391 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723686225.053162 55391 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723686225.055160 55391 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723686225.057105 55391 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723686225.059113 55391 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723686225.061029 55391 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 tf.Tensor( [[ 0.43842277 -0.53439844 -0.07710262] [ 1.5658045 -0.1012345 -0.2744976 ]], shape=(2, 3), dtype=float32) tf.Tensor( [[ 1.3061213 0.6299361 0.52625704] [ 1.3733886 0.29277426 -0.7945693 ]], shape=(2, 3), dtype=float32) I0000 00:00:1723686225.063243 55391 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723686225.101904 55391 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723686225.103956 55391 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723686225.105919 55391 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723686225.107957 55391 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723686225.109922 55391 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723686225.111909 55391 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723686225.113815 55391 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723686225.115823 55391 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723686225.117783 55391 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723686225.120279 55391 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723686225.122678 55391 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723686225.125117 55391 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
There are multiple ways to create a generator object. The easiest is Generator.from_seed, as shown above, that creates a generator from a seed. A seed is any non-negative integer. from_seed also takes an optional argument alg which is the RNG algorithm that will be used by this generator:
g1 = tf.random.Generator.from_seed(1, alg='philox')
print(g1.normal(shape=[2, 3]))
tf.Tensor( [[ 0.43842277 -0.53439844 -0.07710262] [ 1.5658045 -0.1012345 -0.2744976 ]], shape=(2, 3), dtype=float32)
See the Algorithms section below for more information about it.
Another way to create a generator is with Generator.from_non_deterministic_state. A generator created this way will start from a non-deterministic state, depending on e.g., time and OS.
g = tf.random.Generator.from_non_deterministic_state()
print(g.normal(shape=[2, 3]))
tf.Tensor( [[ 0.9104948 -0.23143363 -0.09841432] [-0.91448975 0.1579936 1.3923475 ]], shape=(2, 3), dtype=float32)
There are yet other ways to create generators, such as from explicit states, which are not covered by this guide.
When using tf.random.get_global_generator to get the global generator, you need to be careful about device placement. The global generator is created (from a non-deterministic state) at the first time tf.random.get_global_generator is called, and placed on the default device at that call. So, for example, if the first site you call tf.random.get_global_generator is within a tf.device("gpu") scope, the global generator will be placed on the GPU, and using the global generator later on from the CPU will incur a GPU-to-CPU copy.
There is also a function tf.random.set_global_generator for replacing the global generator with another generator object. This function should be used with caution though, because the old global generator may have been captured by a tf.function (as a weak reference), and replacing it will cause it to be garbage collected, breaking the tf.function. A better way to reset the global generator is to use one of the "reset" functions such as Generator.reset_from_seed, which won't create new generator objects.
g = tf.random.Generator.from_seed(1)
print(g.normal([]))
print(g.normal([]))
g.reset_from_seed(1)
print(g.normal([]))
tf.Tensor(0.43842277, shape=(), dtype=float32) tf.Tensor(1.6272374, shape=(), dtype=float32) tf.Tensor(0.43842277, shape=(), dtype=float32)
Creating independent random-number streams
In many applications one needs multiple independent random-number streams, independent in the sense that they won't overlap and won't have any statistically detectable correlations. This is achieved by using Generator.split to create multiple generators that are guaranteed to be independent of each other (i.e. generating independent streams).
g = tf.random.Generator.from_seed(1)
print(g.normal([]))
new_gs = g.split(3)
for new_g in new_gs:
print(new_g.normal([]))
print(g.normal([]))
tf.Tensor(0.43842277, shape=(), dtype=float32) tf.Tensor(2.536413, shape=(), dtype=float32) tf.Tensor(0.33186463, shape=(), dtype=float32) tf.Tensor(-0.07144657, shape=(), dtype=float32) tf.Tensor(-0.79253083, shape=(), dtype=float32)
split will change the state of the generator on which it is called (g in the above example), similar to an RNG method such as normal. In addition to being independent of each other, the new generators (new_gs) are also guaranteed to be independent of the old one (g).
Spawning new generators is also useful when you want to make sure the generator you use is on the same device as other computations, to avoid the overhead of cross-device copy. For example:
with tf.device("cpu"): # change "cpu" to the device you want
g = tf.random.get_global_generator().split(1)[0]
print(g.normal([])) # use of g won't cause cross-device copy, unlike the global generator
tf.Tensor(-0.66787744, shape=(), dtype=float32)
You can do splitting recursively, calling split on split generators. There are no limits (barring integer overflow) on the depth of recursions.
Interaction with tf.function
tf.random.Generator obeys the same rules as tf.Variable when used with tf.function. This includes three aspects.
Creating generators outside tf.function
tf.function can use a generator created outside of it.
g = tf.random.Generator.from_seed(1)
@tf.function
def foo():
return g.normal([])
print(foo())
tf.Tensor(0.43842277, shape=(), dtype=float32)
The user needs to make sure that the generator object is still alive (not garbage-collected) when the function is called.
Creating generators inside tf.function
Creation of generators inside a tf.function can only happened during the first run of the function.
g = None
@tf.function
def foo():
global g
if g is None:
g = tf.random.Generator.from_seed(1)
return g.normal([])
print(foo())
print(foo())
tf.Tensor(0.43842277, shape=(), dtype=float32) tf.Tensor(1.6272374, shape=(), dtype=float32)
Passing generators as arguments to tf.function
When used as an argument to a tf.function, different generator objects will cause retracing of the tf.function.
num_traces = 0
@tf.function
def foo(g):
global num_traces
num_traces += 1
return g.normal([])
foo(tf.random.Generator.from_seed(1))
foo(tf.random.Generator.from_seed(2))
print(num_traces)
2
Note that this retracing behavior is consistent with tf.Variable:
num_traces = 0
@tf.function
def foo(v):
global num_traces
num_traces += 1
return v.read_value()
foo(tf.Variable(1))
foo(tf.Variable(2))
print(num_traces)
1
Interaction with distribution strategies
There are two ways in which Generator interacts with distribution strategies.
Creating generators outside distribution strategies
If a generator is created outside strategy scopes, all replicas’ access to the generator will be serialized, and hence the replicas will get different random numbers.
g = tf.random.Generator.from_seed(1)
strat = tf.distribute.MirroredStrategy(devices=["cpu:0", "cpu:1"])
with strat.scope():
def f():
print(g.normal([]))
results = strat.run(f)
INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:CPU:0', '/job:localhost/replica:0/task:0/device:CPU:1')
WARNING:tensorflow:Using MirroredStrategy eagerly has significant overhead currently. We will be working on improving this in the future, but for now please wrap `call_for_each_replica` or `experimental_run` or `run` inside a tf.function to get the best performance.
tf.Tensor(0.43842274, shape=(), dtype=float32)
tf.Tensor(1.6272374, shape=(), dtype=float32)
Note that this usage may have performance issues because the generator's device is different from the replicas.
Creating generators inside distribution strategies
If a generator is created inside a strategy scope, each replica will get a different and independent stream of random numbers.
strat = tf.distribute.MirroredStrategy(devices=["cpu:0", "cpu:1"])
with strat.scope():
g = tf.random.Generator.from_seed(1)
print(strat.run(lambda: g.normal([])))
print(strat.run(lambda: g.normal([])))
INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:CPU:0', '/job:localhost/replica:0/task:0/device:CPU:1')
WARNING:tensorflow:Using MirroredStrategy eagerly has significant overhead currently. We will be working on improving this in the future, but for now please wrap `call_for_each_replica` or `experimental_run` or `run` inside a tf.function to get the best performance.
PerReplica:{
0: tf.Tensor(-0.87930447, shape=(), dtype=float32),
1: tf.Tensor(0.020661574, shape=(), dtype=float32)
}
WARNING:tensorflow:Using MirroredStrategy eagerly has significant overhead currently. We will be working on improving this in the future, but for now please wrap `call_for_each_replica` or `experimental_run` or `run` inside a tf.function to get the best performance.
PerReplica:{
0: tf.Tensor(-1.5822568, shape=(), dtype=float32),
1: tf.Tensor(0.77539235, shape=(), dtype=float32)
}
If the generator is seeded (e.g. created by Generator.from_seed), the random numbers are determined by the seed, even though different replicas get different and uncorrelated numbers. One can think of a random number generated on a replica as a hash of the replica ID and a "primary" random number that is common to all replicas. Hence, the whole system is still deterministic.
tf.random.Generator can also be created inside Strategy.run:
strat = tf.distribute.MirroredStrategy(devices=["cpu:0", "cpu:1"])
with strat.scope():
def f():
g = tf.random.Generator.from_seed(1)
a = g.normal([])
b = g.normal([])
return tf.stack([a, b])
print(strat.run(f))
print(strat.run(f))
INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:CPU:0', '/job:localhost/replica:0/task:0/device:CPU:1')
WARNING:tensorflow:Using MirroredStrategy eagerly has significant overhead currently. We will be working on improving this in the future, but for now please wrap `call_for_each_replica` or `experimental_run` or `run` inside a tf.function to get the best performance.
PerReplica:{
0: tf.Tensor([-0.87930447 -1.5822568 ], shape=(2,), dtype=float32),
1: tf.Tensor([0.02066157 0.77539235], shape=(2,), dtype=float32)
}
WARNING:tensorflow:Using MirroredStrategy eagerly has significant overhead currently. We will be working on improving this in the future, but for now please wrap `call_for_each_replica` or `experimental_run` or `run` inside a tf.function to get the best performance.
PerReplica:{
0: tf.Tensor([-0.87930447 -1.5822568 ], shape=(2,), dtype=float32),
1: tf.Tensor([0.02066157 0.77539235], shape=(2,), dtype=float32)
}
We no longer recommend passing tf.random.Generator as arguments to Strategy.run, because Strategy.run generally expects the arguments to be tensors, not generators.
Saving generators
Generally for saving or serializing you can handle a tf.random.Generator the same way you would handle a tf.Variable or a tf.Module (or its subclasses). In TF there are two mechanisms for serialization: Checkpoint and SavedModel.
Checkpoint
Generators can be freely saved and restored using tf.train.Checkpoint. The random-number stream from the restoring point will be the same as that from the saving point.
filename = "./checkpoint"
g = tf.random.Generator.from_seed(1)
cp = tf.train.Checkpoint(generator=g)
print(g.normal([]))
tf.Tensor(0.43842277, shape=(), dtype=float32)
cp.write(filename)
print("RNG stream from saving point:")
print(g.normal([]))
print(g.normal([]))
RNG stream from saving point: tf.Tensor(1.6272374, shape=(), dtype=float32) tf.Tensor(1.6307176, shape=(), dtype=float32)
cp.restore(filename)
print("RNG stream from restoring point:")
print(g.normal([]))
print(g.normal([]))
RNG stream from restoring point: tf.Tensor(1.6272374, shape=(), dtype=float32) tf.Tensor(1.6307176, shape=(), dtype=float32)
You can also save and restore within a distribution strategy:
filename = "./checkpoint"
strat = tf.distribute.MirroredStrategy(devices=["cpu:0", "cpu:1"])
with strat.scope():
g = tf.random.Generator.from_seed(1)
cp = tf.train.Checkpoint(my_generator=g)
print(strat.run(lambda: g.normal([])))
INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:CPU:0', '/job:localhost/replica:0/task:0/device:CPU:1')
INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:CPU:0', '/job:localhost/replica:0/task:0/device:CPU:1')
PerReplica:{
0: tf.Tensor(-0.87930447, shape=(), dtype=float32),
1: tf.Tensor(0.020661574, shape=(), dtype=float32)
}
with strat.scope():
cp.write(filename)
print("RNG stream from saving point:")
print(strat.run(lambda: g.normal([])))
print(strat.run(lambda: g.normal([])))
RNG stream from saving point:
PerReplica:{
0: tf.Tensor(-1.5822568, shape=(), dtype=float32),
1: tf.Tensor(0.77539235, shape=(), dtype=float32)
}
PerReplica:{
0: tf.Tensor(-0.5039703, shape=(), dtype=float32),
1: tf.Tensor(0.1251838, shape=(), dtype=float32)
}
with strat.scope():
cp.restore(filename)
print("RNG stream from restoring point:")
print(strat.run(lambda: g.normal([])))
print(strat.run(lambda: g.normal([])))
RNG stream from restoring point:
PerReplica:{
0: tf.Tensor(-1.5822568, shape=(), dtype=float32),
1: tf.Tensor(0.77539235, shape=(), dtype=float32)
}
PerReplica:{
0: tf.Tensor(-0.5039703, shape=(), dtype=float32),
1: tf.Tensor(0.1251838, shape=(), dtype=float32)
}
You should make sure that the replicas don't diverge in their RNG call history (e.g. one replica makes one RNG call while another makes two RNG calls) before saving. Otherwise, their internal RNG states will diverge and tf.train.Checkpoint (which only saves the first replica's state) won't properly restore all the replicas.
You can also restore a saved checkpoint to a different distribution strategy with a different number of replicas. Because a tf.random.Generator object created in a strategy can only be used in the same strategy, to restore to a different strategy, you have to create a new tf.random.Generator in the target strategy and a new tf.train.Checkpoint for it, as shown in this example:
filename = "./checkpoint"
strat1 = tf.distribute.MirroredStrategy(devices=["cpu:0", "cpu:1"])
with strat1.scope():
g1 = tf.random.Generator.from_seed(1)
cp1 = tf.train.Checkpoint(my_generator=g1)
print(strat1.run(lambda: g1.normal([])))
INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:CPU:0', '/job:localhost/replica:0/task:0/device:CPU:1')
INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:CPU:0', '/job:localhost/replica:0/task:0/device:CPU:1')
PerReplica:{
0: tf.Tensor(-0.87930447, shape=(), dtype=float32),
1: tf.Tensor(0.020661574, shape=(), dtype=float32)
}
with strat1.scope():
cp1.write(filename)
print("RNG stream from saving point:")
print(strat1.run(lambda: g1.normal([])))
print(strat1.run(lambda: g1.normal([])))
RNG stream from saving point:
PerReplica:{
0: tf.Tensor(-1.5822568, shape=(), dtype=float32),
1: tf.Tensor(0.77539235, shape=(), dtype=float32)
}
PerReplica:{
0: tf.Tensor(-0.5039703, shape=(), dtype=float32),
1: tf.Tensor(0.1251838, shape=(), dtype=float32)
}
strat2 = tf.distribute.MirroredStrategy(devices=["cpu:0", "cpu:1", "cpu:2"])
with strat2.scope():
g2 = tf.random.Generator.from_seed(1)
cp2 = tf.train.Checkpoint(my_generator=g2)
cp2.restore(filename)
print("RNG stream from restoring point:")
print(strat2.run(lambda: g2.normal([])))
print(strat2.run(lambda: g2.normal([])))
INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:CPU:0', '/job:localhost/replica:0/task:0/device:CPU:1', '/job:localhost/replica:0/task:0/device:CPU:2')
INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:CPU:0', '/job:localhost/replica:0/task:0/device:CPU:1', '/job:localhost/replica:0/task:0/device:CPU:2')
RNG stream from restoring point:
PerReplica:{
0: tf.Tensor(-1.5822568, shape=(), dtype=float32),
1: tf.Tensor(0.77539235, shape=(), dtype=float32),
2: tf.Tensor(0.6851049, shape=(), dtype=float32)
}
PerReplica:{
0: tf.Tensor(-0.5039703, shape=(), dtype=float32),
1: tf.Tensor(0.1251838, shape=(), dtype=float32),
2: tf.Tensor(-0.58519536, shape=(), dtype=float32)
}
Although g1 and cp1 are different objects from g2 and cp2, they are linked via the common checkpoint file filename and object name my_generator. Overlapping replicas between strategies (e.g. cpu:0 and cpu:1 above) will have their RNG streams properly restored like in previous examples. This guarantee doesn't cover the case when a generator is saved in a strategy scope and restored outside of any strategy scope or vice versa, because a device outside strategies is treated as different from any replica in a strategy.
SavedModel
tf.random.Generator can be saved to a SavedModel. The generator can be created within a strategy scope. The saving can also happen within a strategy scope.
filename = "./saved_model"
class MyModule(tf.Module):
def __init__(self):
super(MyModule, self).__init__()
self.g = tf.random.Generator.from_seed(0)
@tf.function
def __call__(self):
return self.g.normal([])
@tf.function
def state(self):
return self.g.state
strat = tf.distribute.MirroredStrategy(devices=["cpu:0", "cpu:1"])
with strat.scope():
m = MyModule()
print(strat.run(m))
print("state:", m.state())
INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:CPU:0', '/job:localhost/replica:0/task:0/device:CPU:1')
INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:CPU:0', '/job:localhost/replica:0/task:0/device:CPU:1')
PerReplica:{
0: tf.Tensor(-1.4154755, shape=(), dtype=float32),
1: tf.Tensor(-0.11388441, shape=(), dtype=float32)
}
state: tf.Tensor([256 0 0], shape=(3,), dtype=int64)
with strat.scope():
tf.saved_model.save(m, filename)
print("RNG stream from saving point:")
print(strat.run(m))
print("state:", m.state())
print(strat.run(m))
print("state:", m.state())
INFO:tensorflow:Assets written to: ./saved_model/assets
INFO:tensorflow:Assets written to: ./saved_model/assets
RNG stream from saving point:
PerReplica:{
0: tf.Tensor(-0.68758255, shape=(), dtype=float32),
1: tf.Tensor(0.8084062, shape=(), dtype=float32)
}
state: tf.Tensor([512 0 0], shape=(3,), dtype=int64)
PerReplica:{
0: tf.Tensor(-0.27342677, shape=(), dtype=float32),
1: tf.Tensor(-0.53093255, shape=(), dtype=float32)
}
state: tf.Tensor([768 0 0], shape=(3,), dtype=int64)
imported = tf.saved_model.load(filename)
print("RNG stream from loading point:")
print("state:", imported.state())
print(imported())
print("state:", imported.state())
print(imported())
print("state:", imported.state())
RNG stream from loading point: state: tf.Tensor([256 0 0], shape=(3,), dtype=int64) tf.Tensor(-1.0359411, shape=(), dtype=float32) state: tf.Tensor([512 0 0], shape=(3,), dtype=int64) tf.Tensor(-0.06425078, shape=(), dtype=float32) state: tf.Tensor([768 0 0], shape=(3,), dtype=int64)
Loading a SavedModel containing tf.random.Generator into a distribution strategy is not recommended because the replicas will all generate the same random-number stream (which is because replica ID is frozen in SavedModel's graph).
Loading a distributed tf.random.Generator (a generator created within a distribution strategy) into a non-strategy environment, like the above example, also has a caveat. The RNG state will be properly restored, but the random numbers generated will be different from the original generator in its strategy (again because a device outside strategies is treated as different from any replica in a strategy).
Stateless RNGs
Usage of stateless RNGs is simple. Since they are just pure functions, there is no state or side effect involved.
print(tf.random.stateless_normal(shape=[2, 3], seed=[1, 2]))
print(tf.random.stateless_normal(shape=[2, 3], seed=[1, 2]))
tf.Tensor( [[ 0.5441101 0.20738031 0.07356433] [ 0.04643455 -1.30159 -0.95385665]], shape=(2, 3), dtype=float32) tf.Tensor( [[ 0.5441101 0.20738031 0.07356433] [ 0.04643455 -1.30159 -0.95385665]], shape=(2, 3), dtype=float32)
Every stateless RNG requires a seed argument, which needs to be an integer Tensor of shape [2]. The results of the op are fully determined by this seed.
The RNG algorithm used by stateless RNGs is device-dependent, meaning the same op running on a different device may produce different outputs.
Algorithms
General
Both the tf.random.Generator class and the stateless functions support the Philox algorithm (written as "philox" or tf.random.Algorithm.PHILOX) on all devices.
Different devices will generate the same integer numbers, if using the same algorithm and starting from the same state. They will also generate "almost the same" float-point numbers, though there may be small numerical discrepancies caused by the different ways the devices carry out the float-point computation (e.g. reduction order).
XLA devices
On XLA-driven devices (such as TPU, and also CPU/GPU when XLA is enabled) the ThreeFry algorithm (written as "threefry" or tf.random.Algorithm.THREEFRY) is also supported. This algorithm is fast on TPU but slow on CPU/GPU compared to Philox.
See paper 'Parallel Random Numbers: As Easy as 1, 2, 3' for more details about these algorithms.