Add ONNX model format testcase in MLServer test-suite

[Snomaan6846]

Signed-off-by: Snomaan6846 syedali@redhat.com

rh-pre-commit.version: 2.3.2
rh-pre-commit.check-secrets: ENABLED

Description

Added ONNX model format testcase in MLServer test-suite.

Jira-Link : https://issues.redhat.com/browse/RHOAIENG-47134

How Has This Been Tested?

Merge criteria:

• The commits are squashed in a cohesive manner and have meaningful messages.
• Testing instructions have been added in the PR body (for PRs involving changes that are not immediately obvious).
• The developer has manually tested the changes and verified that the changes work

Summary by CodeRabbit

• Tests
  • Added ONNX model format test coverage for MLServer basic model deployment scenarios, including REST and raw deployment configurations.
  • Expanded parameterized tests to include ONNX cases, added ONNX-specific test constants and a new snapshot capturing ONNX inference output for deterministic validation.

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[coderabbitai]

Caution

Review failed

Pull request was closed or merged during review

📝 Walkthrough

Walkthrough

The pull request adds ONNX model format support to the MLServer basic model deployment test suite by introducing a new ONNX snapshot JSON, adding an ONNX test parameter to the parameterized test, and defining ONNX-specific REST input constants and a MODEL_CONFIGS entry.

Changes

Cohort / File(s)	Summary
Test Snapshot tests/model_serving/model_runtime/mlserver/basic_model_deployment/__snapshots__/TestMLServerModels.test_mlserver_model_inference[onnx-raw-deployment].json	Adds a new snapshot JSON for ONNX raw deployment describing a model id onnx, version v1.0.0, one FP32 output predict with shape [1,1] and value -137.39060974121094.
Test Parameterization tests/model_serving/model_runtime/mlserver/basic_model_deployment/test_mlserver_basic_model_deployment.py	Adds a pytest.param entry for ModelFormat.ONNX to the existing parameterized test cases (mirrors other model formats).
Test Constants / Configs tests/model_serving/model_runtime/mlserver/constant.py	Introduces ONNX_REST_INPUT_QUERY constant and adds a ModelFormat.ONNX entry to MODEL_CONFIGS with model_version: "v1.0.0", rest_query: ONNX_REST_INPUT_QUERY, and output_type: OutputType.DETERMINISTIC.

Estimated code review effort

🎯 2 (Simple) | ⏱️ ~10 minutes

🚥 Pre-merge checks | ✅ 1 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Description check	⚠️ Warning	The description is incomplete. While it identifies the core change and provides a Jira link, it omits required template sections including Related Issues details, testing methodology specifics, and additional requirements checklist.	Complete the PR description template by detailing testing approach under 'How it has been tested', explicitly addressing whether this introduces new test images or Jenkins markers, and ensuring all required checkboxes are properly evaluated.

✅ Passed checks (1 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title directly and concisely describes the main change: adding an ONNX model format testcase to the MLServer test suite.

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[coderabbitai]

🧹 Nitpick comments (1)

tests/model_serving/model_runtime/mlserver/constant.py (1)

80-90: Missing type annotation for consistency.

Other REST input query constants (SKLEARN_REST_INPUT_QUERY, XGBOOST_REST_INPUT_QUERY, LIGHTGBM_REST_INPUT_QUERY) have explicit dict[str, Any] type annotations. Add it here for consistency.

Proposed fix

-ONNX_REST_INPUT_QUERY = {
+ONNX_REST_INPUT_QUERY: dict[str, Any] = {
     "id": "onnx",
     "inputs": [
         {

🤖 Prompt for AI Agents

Verify each finding against the current code and only fix it if needed.

In `@tests/model_serving/model_runtime/mlserver/constant.py` around lines 80 - 90,
The constant ONNX_REST_INPUT_QUERY should have an explicit type annotation for
consistency with other constants; update its declaration to include the type
dict[str, Any] (i.e., annotate ONNX_REST_INPUT_QUERY: dict[str, Any]) while
keeping the existing value unchanged so it matches SKLEARN_REST_INPUT_QUERY,
XGBOOST_REST_INPUT_QUERY, and LIGHTGBM_REST_INPUT_QUERY.

🤖 Prompt for all review comments with AI agents

Verify each finding against the current code and only fix it if needed.

Nitpick comments:
In `@tests/model_serving/model_runtime/mlserver/constant.py`:
- Around line 80-90: The constant ONNX_REST_INPUT_QUERY should have an explicit
type annotation for consistency with other constants; update its declaration to
include the type dict[str, Any] (i.e., annotate ONNX_REST_INPUT_QUERY: dict[str,
Any]) while keeping the existing value unchanged so it matches
SKLEARN_REST_INPUT_QUERY, XGBOOST_REST_INPUT_QUERY, and
LIGHTGBM_REST_INPUT_QUERY.

---

ℹ️ Review info

⚙️ Run configuration

Configuration used: Repository YAML (base), Central YAML (inherited), Organization UI (inherited)

Review profile: CHILL

Plan: Pro

Run ID: e24203e0-9042-4152-94d6-099567738e49

📥 Commits

Reviewing files that changed from the base of the PR and between 4628796 and cd10e3d.

📒 Files selected for processing (3)

• tests/model_serving/model_runtime/mlserver/basic_model_deployment/__snapshots__/test_mlserver_basic_model_deployment/TestMLServerModels.test_mlserver_model_inference[onnx-raw-deployment].json
• tests/model_serving/model_runtime/mlserver/basic_model_deployment/test_mlserver_basic_model_deployment.py
• tests/model_serving/model_runtime/mlserver/constant.py

[brettmthompson]

/lgtm

[github-actions]

Status of building tag latest: success.
Status of pushing tag latest to image registry: success.