Randomness As Defined by Machine Learning’s Most Popular Language

Content Overview Setup The tf.random.Generator class Creating independent random-number streams Interaction with tf.function Interaction with distribution strategies Saving generators Stateless RNGs Algorithms General XLA devices Setup The tf.random.Generator class Creating independent random-number streams Interaction with tf.function Interaction with distribution strategies Saving generators Stateless RNGs Algorithms General XLA devices 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. tf.random Note: The random numbers are not guaranteed to be consistent across TensorFlow versions. See: Version Compatibility Note: The random numbers are not guaranteed to be consistent across TensorFlow versions. See: Version Compatibility Note: Version Compatibility Version Compatibility TensorFlow provides two approaches for controlling the random number generation process: Through the explicit use of tf.random.Generator objects. Each such object maintains a state (in tf.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. Through the explicit use of tf.random.Generator objects. Each such object maintains a state (in tf.Variable) that will be changed after each number generation. tf.random.Generator tf.Variable 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. tf.random.stateless_uniform Warning: The old RNGs from TF 1.x such as tf.random.uniform and tf.random.normal are not yet deprecated but strongly discouraged. Warning: The old RNGs from TF 1.x such as tf.random.uniform and tf.random.normal are not yet deprecated but strongly discouraged. Warning: tf.random.uniform tf.random.normal 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() ]) 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 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 tf.random.Generator 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. tf.random.Generator tf.Variable tf.Variable tf.Variable 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: tf.random.Generator tf.random.get_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])) 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 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: Generator.from_seed from_seed alg g1 = tf.random.Generator.from_seed(1, alg='philox') print(g1.normal(shape=[2, 3])) 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) 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. Algorithms 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. Generator.from_non_deterministic_state g = tf.random.Generator.from_non_deterministic_state() print(g.normal(shape=[2, 3])) 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) 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. tf.random.get_global_generator tf.random.get_global_generator tf.random.get_global_generator tf.device("gpu") 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. tf.random.set_global_generator tf.function tf.function Generator.reset_from_seed g = tf.random.Generator.from_seed(1) print(g.normal([])) print(g.normal([])) g.reset_from_seed(1) print(g.normal([])) 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) 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). Generator.split 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([])) 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) 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). split g normal new_gs 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 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) tf.Tensor(-0.66787744, shape=(), dtype=float32) Note: In theory, you can use constructors such as from_seed instead of split here to obtain a new generator, but by doing so you lose the guarantee that the new generator is independent of the global generator. You will also run the risk that you may accidentally create two generators with the same seed or with seeds that lead to overlapping random-number streams. Note: In theory, you can use constructors such as from_seed instead of split here to obtain a new generator, but by doing so you lose the guarantee that the new generator is independent of the global generator. You will also run the risk that you may accidentally create two generators with the same seed or with seeds that lead to overlapping random-number streams. Note: from_seed split You can do splitting recursively, calling split on split generators. There are no limits (barring integer overflow) on the depth of recursions. split Interaction with tf.function tf.function tf.random.Generator obeys the same rules as tf.Variable when used with tf.function. This includes three aspects. tf.random.Generator tf.Variable tf.function Creating generators outside tf.function tf.function tf.function can use a generator created outside of it. tf.function g = tf.random.Generator.from_seed(1) @tf.function def foo(): return g.normal([]) print(foo()) g = tf.random.Generator.from_seed(1) @tf.function def foo(): return g.normal([]) print(foo()) tf.Tensor(0.43842277, shape=(), dtype=float32) 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 tf.function Creation of generators inside a tf.function can only happened during the first run of the function. tf.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()) 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) tf.Tensor(0.43842277, shape=(), dtype=float32) tf.Tensor(1.6272374, shape=(), dtype=float32) Passing generators as arguments to tf.function tf.function When used as an argument to a tf.function, different generator objects will cause retracing of the tf.function. tf.function 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) 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 2 Note that this retracing behavior is consistent with tf.Variable: 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) 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 1 Interaction with distribution strategies There are two ways in which Generator interacts with distribution strategies. Generator 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) 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) 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([]))) 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) } 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) } Note: Currently tf.random.Generator doesn't provide an option to let different replicas get identical (instead of different) streams (which is technically not hard). If you have a use case for this feature, please let the TensorFlow developers know. Note: Currently tf.random.Generator doesn't provide an option to let different replicas get identical (instead of different) streams (which is technically not hard). If you have a use case for this feature, please let the TensorFlow developers know. Note: tf.random.Generator 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. Generator.from_seed tf.random.Generator can also be created inside Strategy.run: tf.random.Generator 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)) 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) } 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. tf.random.Generator Strategy.run Strategy.run 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. tf.random.Generator tf.Variable tf.Module Checkpoint 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. tf.train.Checkpoint filename = "./checkpoint" g = tf.random.Generator.from_seed(1) cp = tf.train.Checkpoint(generator=g) print(g.normal([])) 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) tf.Tensor(0.43842277, shape=(), dtype=float32) cp.write(filename) print("RNG stream from saving point:") print(g.normal([])) print(g.normal([])) 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) 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([])) 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) 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([]))) 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) } 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([]))) 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) } 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([]))) 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) } 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. tf.train.Checkpoint 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: tf.random.Generator tf.random.Generator tf.train.Checkpoint 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([]))) 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) } 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([]))) 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) } 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([]))) 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) } 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. g1 cp1 g2 cp2 filename my_generator cpu:0 cpu:1 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. tf.random.Generator 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()) 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) 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()) 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) 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()) 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) 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). tf.random.Generator 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). tf.random.Generator 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])) 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) 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. seed [2] 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. tf.random.Generator stateless "philox" tf.random.Algorithm.PHILOX 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. "threefry" tf.random.Algorithm.THREEFRY See paper 'Parallel Random Numbers: As Easy as 1, 2, 3' for more details about these algorithms. 'Parallel Random Numbers: As Easy as 1, 2, 3' Originally published on the TensorFlow website, this article appears here under a new headline and is licensed under CC BY 4.0. Code samples shared under the Apache 2.0 License. Originally published on the TensorFlow website, this article appears here under a new headline and is licensed under CC BY 4.0. Code samples shared under the Apache 2.0 License. Originally published on the TensorFlow website, this article appears here under a new headline and is licensed under CC BY 4.0. Code samples shared under the Apache 2.0 License. TensorFlow TensorFlow