Fix TensorFlow 'Check failed: work_element_count > 0' (gpu_launch_config.h:129)

Check failed: work_element_count > 0 (-1018167296 vs. 0) from gpu_launch_config.h:129 looks like a low-level GPU crash, but it's really TensorFlow catching a bad tensor before it launches a kernel. Understanding the assertion tells you exactly where to look — and it isn't your GPU or drivers.

What the assertion means

Before TensorFlow runs a CUDA kernel, it calculates how many elements that kernel must process — work_element_count — and asserts the value is positive. It has to be: launching a kernel over zero or a negative number of elements is meaningless. When the assertion fails, some tensor reached a GPU op with no elements, or with an element count that computed to a garbage negative value. Either way, the real problem happened upstream, in your data or your shapes.

The value in the parentheses is a strong hint:

• A small non-positive number (0 or a small negative) → an empty tensor: a batch of size 0, an empty axis.
• A large negative number like -1018167296 → an int32 overflow: the product of a tensor's dimensions exceeded 2³¹ (~2.1 billion) and wrapped around. Something is far larger than you intended.

Why it happens

1. An empty batch reaches the GPU (most common). Your input pipeline yielded a batch of size 0 — an exhausted tf.data dataset, a filter() that removed everything, an empty shard in a distributed setup, or an edge case around drop_remainder. The GPU op then has nothing to process.
2. Shape math collapsed a dimension to 0. A convolution or pooling layer whose input is smaller than its kernel/stride can produce an output dimension of 0. A reshape, slice, or gather can also produce an empty axis. The tensor is technically valid but has zero elements.
3. An int32 overflow on a huge tensor. A dimension computed much larger than intended (a bad multiplication, an off-by-a-lot batch or sequence length) pushes the total element count past 2³¹, and it wraps negative.

Diagnose it

Reproduce with a single batch, eagerly, and inspect what's flowing in:

import tensorflow as tf
 
# 1) Are any batches empty?
for i, (x, y) in enumerate(dataset.take(50)):
    tf.debugging.assert_greater(tf.size(x), 0, message=f"empty batch at {i}")
    if i < 3:
        print("batch", i, "x.shape:", x.shape, "y.shape:", y.shape)
 
# 2) Does a layer collapse a dimension to 0 for your input size?
model.summary()   # look for any output shape with a 0 dimension

For the overflow case, check whether any tensor's total element count approaches 2³¹:

print(int(tf.size(x)))   # if this is near or above 2_147_483_648, that's your overflow

Fix it

• Guarantee non-empty batches. Filter out empty samples before batching, handle the exhausted-dataset case, and be deliberate with drop_remainder. In distributed training, make sure no replica gets an empty shard.
• Fix the shape math. Pad inputs so conv/pool layers never see an input smaller than their kernel, and verify slices/reshapes can't produce an empty axis.
• Tame the overflow. If a tensor is genuinely enormous, reduce the batch size or sequence length, split the operation, or restructure so no single op processes more than ~2 billion elements.

Prevent it

• Assert batch shapes at the pipeline boundary (tf.debugging.assert_greater(tf.size(x), 0)) so an empty batch fails loudly at the source instead of deep inside a kernel launch.
• Print model.summary() when you change input sizes and confirm no layer output has a 0 dimension.
• Sanity-check dataset cardinality after filters and shards so you never feed the GPU an empty batch.

Trace the empty or oversized tensor, fix the data or shape that produced it, and this internal crash goes away — no CUDA reinstall required.