Introduction

Yes, you can split a TensorFlow tensor of unknown runtime length into fixed-size chunks, but you usually cannot do it with a single hard-coded tf.split call. The trick is to compute chunk sizes dynamically from tf.shape and then split using those sizes. Once you frame it that way, the problem becomes straightforward.

Why Dynamic Shapes Change the Solution

If the first dimension is known at graph-build time, you can often write a simple fixed split. But if the size is only known at runtime, the code has to derive the split sizes from the actual tensor length.

For example, suppose you want chunks of size 4 along axis 0.

• a tensor with length 12 should become 4, 4, 4
• a tensor with length 10 should become 4, 4, 2
• a tensor with length 3 should become 3

That last remainder chunk is the part people usually forget.

A Practical Dynamic Split Pattern

The most direct solution is:

1. get runtime length with tf.shape
2. compute full chunk count and remainder
3. build the split-size vector
4. call tf.split

1import tensorflow as tf
2
3
4def split_into_chunks(x, chunk_size):
5    length = tf.shape(x)[0]
6    full_chunks = length // chunk_size
7    remainder = length % chunk_size
8
9    sizes = tf.concat([
10        tf.fill([full_chunks], chunk_size),
11        tf.cond(
12            remainder > 0,
13            lambda: tf.reshape(remainder, [1]),
14            lambda: tf.constant([], dtype=tf.int32),
15        )
16    ], axis=0)
17
18    return tf.split(x, sizes, axis=0)
19
20
21x = tf.range(10)
22parts = split_into_chunks(x, 4)
23for p in parts:
24    print(p.numpy())

This works with runtime-determined lengths and preserves the remainder chunk.

Why tf.split Alone Is Not Enough

tf.split supports either:

• a number of equal splits
• a list of explicit split sizes

If the tensor length is unknown and not evenly divisible, you cannot just say “split into size 4 chunks” and expect TensorFlow to infer the remainder automatically. You have to build the size vector yourself.

That is why dynamic chunking usually starts with tf.shape, not with a hard-coded split count.

Chunking in a tf.function

The same pattern works inside traced TensorFlow code.

1import tensorflow as tf
2
3@tf.function
4
5def chunk_and_sum(x):
6    parts = split_into_chunks(x, 3)
7    return [tf.reduce_sum(part) for part in parts]
8
9result = chunk_and_sum(tf.range(8, dtype=tf.int32))
10for item in result:
11    tf.print(item)

Because the chunk sizes are built from tensor operations, the code still behaves correctly under tracing.

When a Ragged Representation Is Better

If the chunks are only an intermediate representation and you do not strictly need a Python list of tensors, a ragged approach can be cleaner for some pipelines.

But for many model-preprocessing tasks, a plain list from tf.split is still the easiest thing to work with, especially when each chunk is processed independently.

Alternative: Use a Loop with TensorArray

If you need more custom behavior per chunk, such as padding or per-chunk transformation, a loop and TensorArray may be more flexible than one split call.

1import tensorflow as tf
2
3
4def chunk_with_loop(x, chunk_size):
5    length = tf.shape(x)[0]
6    i = tf.constant(0)
7    out = tf.TensorArray(dtype=x.dtype, size=0, dynamic_size=True)
8
9    def cond(i, out):
10        return i < length
11
12    def body(i, out):
13        chunk = x[i:tf.minimum(i + chunk_size, length)]
14        out = out.write(out.size(), chunk)
15        return i + chunk_size, out
16
17    _, out = tf.while_loop(cond, body, [i, out])
18    return out.stack()

This is more verbose, but it gives you full control over each chunking step.

Choosing the Right Tool

Use dynamic tf.split when:

• you want a clean chunk list
• chunk size is fixed
• only tensor length is unknown

Use a loop when:

• each chunk needs custom processing
• you need padding or filtering during chunk creation
• you want tighter control over tracing behavior

The simpler tf.split route is usually enough unless the transformation itself is complex.

Common Pitfalls

A common mistake is giving tf.split a fixed number of chunks when the runtime size is not divisible evenly. That either fails or produces the wrong shape assumptions.

Another mistake is ignoring the remainder and silently dropping the last partial chunk.

People also sometimes mix Python integers and tensor values incorrectly inside traced functions. Build chunk sizes with TensorFlow ops when dynamic shapes are involved.

Finally, do not confuse dynamic shape support with unknown rank. This pattern assumes the axis exists and you are chunking along a known dimension.

Summary

• Split tensors of unknown runtime length by computing chunk sizes from tf.shape
• Use tf.split with a dynamically built size vector to preserve the remainder chunk
• This pattern works in eager mode and inside tf.function
• Use loop-based chunking only when per-chunk logic is more complex
• The key idea is not a special API, but explicit runtime size calculation