TensorFlow is an end-to-end open source platform for machine learning. Specifying a negative dense shape in tf.raw_ops.SparseCountSparseOutput
results in a segmentation fault being thrown out from the standard library as std::vector
invariants are broken. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/countops.cc#L199-L213) assumes the first element of the dense shape is always positive and uses it to initialize a BatchedMap<T>
(i.e., std::vector<absl::flat_hash_map<int64,T>>
(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/countops.cc#L27)) data structure. If the shape
tensor has more than one element, num_batches
is the first value in shape
. Ensuring that the dense_shape
argument is a valid tensor shape (that is, all elements are non-negative) solves this issue. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3.