Does deterministic=False remove all output-ordering guarantees in tf.data parallel map?

Short Answer

When deterministic=False is set on a Dataset.map with num_parallel_calls > 1, TensorFlow gives up all input-order preservation. Elements may emerge in any sequence, and no positional guarantee remains between input and output positions. The earlier research confirms this is the intended behavior across TF 2.13+, though earlier versions could retain partial ordering under specific configurations.

Confirmed Behavior

In a parallel map operation, TensorFlow schedules multiple invocations of the mapping function concurrently. When deterministic=True, the runtime buffers and reorders results to match input order. With deterministic=False, results pass through as soon as they complete, abandoning sequence entirely. This applies to all downstream operations: zip, enumerate, or positional indexing will see arbitrary ordering.

Version Sensitivity

TensorFlow 2.13 and later enforce this non-deterministic behavior more strictly. Versions 2.4 through 2.12 may exhibit partial ordering in some edge cases, particularly when the mapping function is fast enough that parallel tasks complete sequentially by chance. However, this is not a guarantee and should not be relied upon. The tf.config.experimental.enable_op_determinism() function affects individual op reproducibility (e.g., GPU kernel ordering) but does not restore dataset-level ordering once deterministic=False is set at the dataset level.

Verification Steps

To confirm the current behavior in your environment:

1. import tensorflow as tf; print(tf.__version__) to identify the installed version.
2. opts = tf.data.Options(); opts.experimental_deterministic = False; ds = tf.data.Dataset.range(10).with_options(opts).map(lambda x: x, num_parallel_calls=tf.data.AUTOTUNE); print(list(ds)) — the output will likely differ from [0,1,2,3,4,5,6,7,8,9].
3. Use tf.data.Options().to_dict() to inspect resolved option values.

Separating Dataset Ordering from Op-Level Determinism

Dataset-level determinism governs element ordering through the pipeline. Op-level determinism governs reproducibility within a single transformation (e.g., ensuring a specific reduction produces identical floating-point results). Setting deterministic=False at the dataset level disables ordering guarantees regardless of op-level settings. To validate a pipeline, test with enumerated inputs and verify that output positions do not match input positions when deterministic=False is active.