[kotselidis]

Description

The problem

convert_tensor_to_tsblock in iotdb/ainode/core/util/serde.py builds the TsBlock that AINode returns to the DataNode by appending to a bytes object one value at a time (binary += value.tobytes()). Each append copies the whole object, so the conversion is quadratic in the number of output values.

It matters whenever a model returns a value per input point, for example a custom model that scores every point of a series through CALL INFERENCE. Measured in AINode's own environment (2.0.11, Python 3.11) for a 1 x n float64 tensor:

n	Before
10,000	0.084 s
20,000	0.32 s
40,000	1.28 s
80,000	17.9 s
160,000	45.0 s

End to end, a CALL INFERENCE that returns 100,000 per-point scores from a custom PyTorch model on a GPU took 10.1 s, of which 9.7 s was this conversion and 0.33 s the model. Results of a few hundred thousand points take minutes and run into dn_connection_timeout_ms, and the DataNode then retries the call.

The change

The function now takes the tensor's columns as a numpy array and builds the same layout from a list of parts joined once:

• the header (column count, types, position count, encodings) as before;
• the time column as one bytes(8 * position_count) block, which is what the previous loop wrote (two big-endian int32 zeros per row);
• each value column as one big-endian ndarray.tobytes().

The output is byte-for-byte the same as before. The new tests/test_serde.py keeps the previous implementation as a reference and compares the two on scalars, 1-D and 2-D tensors, float32, float64, int32, int64 and bool, NaN, infinities and -0.0, a non-contiguous tensor, and a 2 x 20,000 float64 tensor.

After the change, the conversion takes 2 ms for 100,000 values and 5.5 ms for 1,000,000. The CALL INFERENCE above takes 0.34 s instead of 10.1 s.

struct is no longer used and its import is removed; pandas is still used by the type helpers.

Verification

cd iotdb-core/ainode
python -m unittest discover -s tests -p 'test_serde.py'
black --check iotdb/ainode/core/util/serde.py tests/test_serde.py
isort --check-only --profile black iotdb/ainode/core/util/serde.py tests/test_serde.py

Run with AINode 2.0.11's environment (PyTorch 2.9.1), with Black 25.1.0 and isort 6.0.1. I have not run it on the PyTorch 2.14 environment that master's pyproject.toml now pins, nor run the AINode cluster integration tests.

---

This PR has:

• been self-reviewed.
• added unit tests or modified existing tests to cover new code paths, ensuring the threshold for code coverage.

[Copilot]

🟡 Changes recommended

Higher-rank tensors are accepted without rank validation, allowing the TsBlock header’s declared position count to differ from the number of serialized values.

1 open issue

• Reject higher-rank tensors in classification postprocessing · New

What changed in this PR

This PR speeds up AINode inference-result serialization by replacing repeated byte appends with bulk NumPy conversion, targeting quadratic conversion costs for large outputs.

Changes:

• Builds time and value columns in bulk and joins serialized parts once.
• Adds byte-equivalence tests across tensor shapes and types, plus a large-output performance check.
• Removes the unused struct import and corrects the conversion comment.

File	Description
iotdb-core/​ainode/​tests/​test_serde.py	Adds reference-based serialization tests and a performance regression check.
iotdb-core/​ainode/​iotdb/​ainode/​core/​util/​serde.py	Uses bulk big-endian NumPy serialization instead of per-value appends.

_{Review effort: Balanced}

[SpriCoder]

Good Job~

[RkGrit]

LGTM~