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mlx-knife/TESTING.md
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The BROKE Cluster Team 57bf6d86be 2.0.0-beta.3: Feature Complete - Full 1.1.1 Parity Achieved
Major Features Added:
  • Complete run command implementation with interactive/single-shot modes
  • MLXRunner core engine ported from 1.x with modular architecture
  • OpenAI-compatible server with SIGINT-robust supervisor mode
  • Experimental push feature properly isolated behind environment variable

  Key Improvements:
  - Full feature parity with 1.1.1 stable releases
  - Enhanced human output formatting across all commands
  - Clean separation of stable (184 tests) vs experimental features
  - Updated demo GIF showcasing improved 2.0 interface

  Fixes:
  - Pull operation cache pollution (Issue #30) with preflight access checks
  - Test stability improvements across all environments

  Architecture:
  - Modular runner design with focused helper modules
  - Thread-safe model loading and memory management
  - stable testing across Python 3.9-3.13

  Ready for use as comprehensive 1.x alternative.
2025-09-14 18:04:18 +02:00

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MLX Knife Testing Guide

Current Status

184/184 tests passing (September 2025) — 2.0.0-beta.3; 30 skipped (opt-in) Apple Silicon verified (M1/M2/M3) Python 3.9-3.13 compatible Beta (CLI/JSON) — stable features only, experimental features opt-in Isolated test system - user cache stays pristine with temp cache isolation 3-category test strategy - optimized for performance and safety

Skipped Tests Breakdown (30 total)

  • 21 Push tests - Hidden experimental feature (requires MLXK2_ENABLE_EXPERIMENTAL_PUSH=1)
  • 1 Live push test - Network-dependent (requires multiple env vars)
  • 8 Other opt-in tests - Live tests, Issue #27 real-model tests (require specific env setup)

Quick Start (2.0 Default)

# Install package + tests
pip install -e .[test]

# Download test model (optional; most 2.0 tests use isolated cache)
# Only needed for opt-in live tests or local experiments
# mlxk pull mlx-community/Phi-3-mini-4k-instruct-4bit

# Run 2.0 tests (default discovery: tests_2.0/)
pytest -v  # 184 passed, 30 skipped

# Optional: Enable experimental push tests
MLXK2_ENABLE_EXPERIMENTAL_PUSH=1 pytest -v  # 205 passed, 9 skipped

# Live tests (opt-in; not part of default):
# - Live push (requires experimental push + env):
#   export MLXK2_ENABLE_EXPERIMENTAL_PUSH=1
#   export MLXK2_LIVE_PUSH=1
#   export HF_TOKEN=...; export MLXK2_LIVE_REPO=org/model; export MLXK2_LIVE_WORKSPACE=/abs/path
#   pytest -q -m live_push
# - Live list (uses your HF_HOME; requires at least one MLX chat + one MLX base in cache):
#   export HF_HOME=/path/to/huggingface/cache
#   pytest -q -m live_list

# Before committing
ruff check mlxk2/ --fix && mypy mlxk2/ && pytest -v

Notes

  • Reference environment: venv39 (Applenative Python 3.9) is the recommended dev base.
  • Extras [test] install httpx/FastAPI so the server minimal tests run.
  • For release smoke across multiple Python versions: ./test-multi-python.sh (logs: test_results_3_9.log, test_results_3_10.log, ...).
  • The macOS Python 3.9 LibreSSL warning from urllib3 is suppressed in tests via pytest.ini, and at runtime via package init.

Why Local Testing?

MLX Knife tests fall into three categories for 2.0:

  • Stable CLI/JSON tests (default): Run on any supported Python on macOS; no model inference required; use an isolated HF cache (no network). 184 tests
  • Experimental features (opt-in): Hidden experimental features like push require environment variables to enable. +21 tests
  • Live/Inference tests (opt-in): Network-dependent or requiring real models/cache setup. Various markers/env vars

Default test run covers all stable 2.0 features without experimental or live dependencies.

Test Structure

2.0 Test Structure (default)

Legend

  • spec/: JSON API spec/contract validation; stays in sync with docs/schema.
  • live/: Optin tests requiring env/config; skipped by default.
  • stubs/: Lightweight MLX/MLXLM replacements used only in unit/spec tests.
  • conftest.py: Isolated HF cache (temp), safety sentinel, core fixtures/helpers.
  • conftest_runner.py: Runnerfocused fixtures/mocks for generation tests.
  • *.py.disabled: Intentionally disabled suites (WIP/expanded scenarios, not run).
tests_2.0/
├── __init__.py
├── conftest.py                        # Isolated test cache (HF_HOME override), safety sentinel, core fixtures
├── conftest_runner.py                 # Runner-specific fixtures/mocks
├── stubs/                             # Minimal mlx/mlx_lm stubs for unit/spec tests
├── spec/
│   ├── test_cli_version_output.py             # Version command JSON shape
│   ├── test_spec_doc_examples_validate.py     # Docs examples validate against JSON schema
│   ├── test_spec_version_sync.py              # Code/docs version consistency check
│   ├── test_push_error_matches_schema.py      # Push error output matches schema
│   └── test_push_output_matches_schema.py     # Push success output matches schema
├── live/                                      # Opt-in live tests (markers)
│   ├── test_push_live.py                      # Live push flow (requires MLXK2_LIVE_PUSH, HF_TOKEN)
│   └── test_list_human_live.py                # Live list/health against user cache (requires HF_HOME)
├── test_json_api_list.py                      # JSON API list contract (shape/fields)
├── test_json_api_show.py                      # JSON API show contract (base/files/config)
├── test_human_output.py                       # Human rendering of list/health views
├── test_detection_readme_tokenizer.py         # README/tokenizer-based framework detection
├── test_edge_cases_adr002.py                  # Naming/health edge cases (ADR-002)
├── test_health_multifile.py                   # Multi-file health completeness (index vs pattern)
├── test_model_naming.py                       # Conversion rules, bijection, parsing
├── test_integration.py                        # Model resolution and health integration
├── test_issue_27.py                           # Health policy exploration (legacy scenarios)
├── test_issue_30_preflight.py                 # Preflight for gated/private/not-found repos (Issue #30)
├── test_robustness.py                         # Robustness for rm/pull/disk/timeout/concurrency
├── test_cli_push_args.py                      # Push CLI args and JSON error/output handling (offline)
├── test_push_minimal.py                       # Minimal push scenarios (offline)
├── test_push_extended.py                      # Extended push: no-op vs commit, branch/retry, .hfignore
├── test_push_dry_run.py                       # Push dry-run diff planning (added/modified/deleted)
├── test_push_workspace_check.py               # Push check-only: workspace validation without network
├── test_ctrl_c_handling.py                    # SIGINT handling during run/interactive flows
├── test_interactive_mode.py                   # Interactive CLI mode prompts/history/streaming
├── test_interruption_recovery.py              # Recovery semantics after interruption (flag reset)
├── test_run_complete.py                       # End-to-end run command (stream/batch/params)
├── test_runner_core.py                        # MLXRunner core generation/memory/stop tokens
├── test_token_limits.py                       # Dynamic token calculation; server vs run policies
├── test_server_api_minimal.py                 # Minimal OpenAI-compatible server endpoints (SSE, JSON)
└── test_server_api.py.disabled                # Disabled server API tests (WIP/expanded scenarios)

Note: Live tests are opt-in via markers (-m live_push, -m live_list) and environment. Default pytest discovery runs only the offline suite above.

MLX/MLXLM Stubs (fast offline tests)

  • Purpose: Unit/spec tests run platformneutral and without real MLX/MLXLM runtime.
  • Mechanics: tests_2.0/conftest.py prepends tests_2.0/stubs/ to sys.path, so import mlx/mlx_lm resolve to minimal stubs.
  • Effect: Fast, deterministic tests without GPU/large RAM footprint; live/heavy path remains optin.
  • Production: CLI/server still use the real packages; stubs are not installed.

Push Testing (2.0)

This section summarizes what our test suite covers for the experimental push feature and what still requires live/manual checks.

Reference: Push CLI and JSON

  • Usage: mlxk2 push <local_dir> <org/model> --private [--create] [--branch main] [--commit <msg>] [--check-only] [--json] [--verbose]

  • Args:

    • --private (required in alpha): Safety gate to avoid public uploads.
    • --create: Create the repository if it does not exist (model repo).
  • --branch: Target branch, default main. Missing branches are tolerated; with --create, the branch is proactively created (and upload retried once if the hub initially rejects the revision).

    • --commit: Commit message, default "mlx-knife push".
    • --check-only: Analyze workspace locally; no network call; returns data.workspace_health.
    • --dry-run: Compare local workspace to the remote branch and summarize changes without uploading (requires repo read access).
    • --json: Print JSON response; in JSON mode, logs/progress are suppressed by default.
    • --verbose: Human mode — append details (e.g., commit URL). In JSON mode, only toggles console log verbosity; the JSON payload is unchanged.
  • JSON fields (data):

    • repo_id: string — target org/model.
    • branch: string — target branch.
    • commit_sha: string|null — commit id; null when no_changes:true or on noop.
    • commit_url: string|null — link to commit; null when no commit created.
    • repo_url: stringhttps://huggingface.co/<org/model>.
    • uploaded_files_count: int|null — number of changed files; set to 0 on no_changes:true.
    • local_files_count: int|null — approximate local file count scanned.
    • no_changes: boolean — true when hub reports an empty commit (preferred signal) or no file operations are detected.
    • created_repo: boolean — true when repo was created (with --create).
    • change_summary: {added:int, modified:int, deleted:int} — optional; derived from hub response when available.
    • message: string|null — short human hint; mirrors hub on noop.
    • hf_logs: string[] — buffered hub log lines (not printed in JSON mode unless --verbose).
    • experimental: true and disclaimer: string — feature state markers.
    • workspace_health: {...} — present only with --check-only:
      • healthy: bool, anomalies: [], config, weights.index, weights.pattern_complete, etc.
    • dry_run: true — present only with --dry-run.
    • dry_run_summary: {added:int, modified:int, deleted:int} — present with --dry-run.
    • would_create_repo: bool / would_create_branch: bool — planning hints when target does not exist.
  • Error types (error.type):

    • dependency_missinghuggingface-hub not installed.
    • auth_error — missing HF_TOKEN (unless --check-only).
    • workspace_not_found — local_dir missing/not a directory.
    • repo_not_found — repo missing without --create.
    • upload_failed — hub returned an error (e.g., 403/permission).
    • push_operation_failed — unexpected internal failure wrapper.
  • Exit codes: success → 0; any status:error1.

Notes on output verbosity and behavior

  • JSON is quiet by default: only the final JSON object is printed. Use --verbose to allow hub logs/progress to reach the console (the JSON payload remains unchanged). For assertions, prefer data.hf_logs.
  • Human mode is chatty by default: progress + oneliner summary. --verbose appends the commit URL when present.
  • Nochanges detection: If the hub reports “No files have been modified… Skipping to prevent empty commit.”, JSON sets no_changes: true, uploaded_files_count: 0, and nulls commit_sha/commit_url. Human shows “— no changes”. This hub signal is preferred over inferring from file lists.
  • --dry-run human output: prints a concise plan line dry-run: +A ~M -D (modifications are an approximation and may be ~? in rare cases).
  • Branch creation with --create: Even if the push is a noop, the target branch is created upfront.

Examples (expected)

  • Noop repush (JSON): commit_sha: null, commit_url: null, uploaded_files_count: 0, no_changes: true, message mirrors hub text, hf_logs contains hub lines.

  • Commit (JSON): commit_sha/commit_url populated; uploaded_files_count == sum(change_summary.values()); message summarizes counts.

  • Dry-run (existing repo/branch, no changes) — JSON:

    {
      "status": "success",
      "command": "push",
      "error": null,
      "data": {
        "repo_id": "org/model",
        "branch": "main",
        "commit_sha": null,
        "commit_url": null,
        "repo_url": "https://huggingface.co/org/model",
        "uploaded_files_count": 0,
        "local_files_count": 11,
        "no_changes": true,
        "created_repo": false,
        "message": "Dry-run: no changes",
        "experimental": true,
        "disclaimer": "Experimental feature (M0: upload only). No validation/filters; review results on the Hub.",
        "dry_run": true,
        "dry_run_summary": {"added": 0, "modified": null, "deleted": 0},
        "change_summary": {"added": 0, "modified": 0, "deleted": 0},
        "would_create_repo": false,
        "would_create_branch": false,
        "added_files": [],
        "deleted_files": []
      }
    }
    
  • Dry-run (existing repo/branch, changes present) — JSON:

    {
      "status": "success",
      "command": "push",
      "error": null,
      "data": {
        "repo_id": "org/model",
        "branch": "main",
        "commit_sha": null,
        "commit_url": null,
        "repo_url": "https://huggingface.co/org/model",
        "uploaded_files_count": 0,
        "local_files_count": 11,
        "no_changes": false,
        "created_repo": false,
        "message": "Dry-run: +2 ~? -1",
        "experimental": true,
        "disclaimer": "Experimental feature (M0: upload only). No validation/filters; review results on the Hub.",
        "dry_run": true,
        "dry_run_summary": {"added": 2, "modified": null, "deleted": 1},
        "change_summary": {"added": 2, "modified": 0, "deleted": 1},
        "would_create_repo": false,
        "would_create_branch": false,
        "added_files": ["new.txt", "weights/model.safetensors"],
        "deleted_files": ["old.txt"]
      }
    }
    
  • Dry-run — Human output:

    push (experimental): org/model@main — dry-run: no changes
    push (experimental): org/model@main — dry-run: +2 ~? -1
    

Spec/Schema

  • The JSON API spec version and schema live in mlxk2/spec.py and docs/json-api-specification.md. The docs schema includes support for command: "push" and its fields. Keep tests in sync with those sources of truth.

Automated (offline)

  • Token/Workspace errors: Missing HF_TOKEN and missing workspace produce proper JSON errors.
  • CLI args (JSON mode): Missing positional args emit JSON errors rather than usage text.
  • Schema shape: Push success/error outputs validate against docs/json-api-schema.json.
  • No-op push: Detects no_changes: true, sets uploaded_files_count: 0, carries hub message into JSON (message/hf_logs), and human output shows "no changes" without duplicate logs.
  • Commit path: Extracts commit_sha, commit_url, change_summary (+/~/), correct uploaded_files_count; human --verbose includes URL.
  • Repo/Branch handling: Missing repo requires --create; with --create sets created_repo: true. Missing branch is tolerated; upload attempts proceed. With --create, the branch is proactively created and the upload is retried once if the hub rejects the revision (e.g., “Invalid rev id”).
  • Ignore rules: .hfignore is merged with default ignores and forwarded to the hub.

Files:

  • tests_2.0/test_cli_push_args.py (CLI errors and JSON outputs)
  • tests_2.0/test_push_extended.py (no-op vs commit, branch/repo, .hfignore, human; includes retry on invalid revision with --create)
  • tests_2.0/spec/test_push_output_matches_schema.py (schema success path)

Run (venv39):

  • source venv39/bin/activate && pip install -e .
  • pytest -q tests_2.0/test_cli_push_args.py tests_2.0/test_push_extended.py
  • pytest -q tests_2.0/spec/test_push_output_matches_schema.py
  • Targeted retry test: pytest -q tests_2.0/test_push_extended.py::test_push_retry_creates_branch_on_upload_revision_error

Live (opt-in / wet)

  • Purpose: sanity-check real HF behavior (auth, no-op vs commit, URLs).
  • Defaults: Live tests are skipped. Enable with env vars and markers.
  • Env:
    • MLXK2_LIVE_PUSH=1
    • HF_TOKEN (write-enabled)
    • MLXK2_LIVE_REPO='org/model'
    • MLXK2_LIVE_WORKSPACE='/abs/path/to/workspace'
  • Command:
    • pytest -q -m wet tests_2.0/live/test_push_live.py
    • or pytest -q -m live_push

Pull/Preflight (Issue #30)

Goal: Gated/private/notfound repos must not pollute the cache and should fail fast.

  • Behavior (2.0):

    • Preflight uses huggingface_hub.HfApi.model_info() (metadata only; no download).
    • Gated/Forbidden/Unauthorized/NotFound → access_denied before download; clear hint to set HF_TOKEN.
    • Network timeouts/unspecific HTTP errors in preflight → degrade to a warning; allow the download layer (to surface meaningful error/timeout paths).
    • Tokens: prefer HF_TOKEN (legacy HUGGINGFACE_HUB_TOKEN is read, but not promoted).
    • Tests use isolated caches; the user cache is never touched.
  • Relevant tests: tests_2.0/test_issue_30_preflight.py

    • test_preflight_private_model_without_token
    • test_preflight_nonexistent_model
    • test_preflight_integration_in_pull
    • test_preflight_prevents_cache_pollution
  • Quick checks:

    • pytest -q tests_2.0/test_issue_30_preflight.py
    • CLI: unset HF_TOKEN HUGGINGFACE_HUB_TOKEN; mlxk-json pull meta-llama/Llama-2-7b-hf --json

Runner: Interruption & Recovery

  • Semantics (2.0): A new generation resets _interrupted = False at the start (recovery behavior). A previous CtrlC does not block the next generation.
  • Streaming:
    • During an active generation, the runner yields a line "[Generation interrupted by user]" and stops.
    • Token diffing in streaming is robust against minimal mocks (no StopIteration due to short decode sequences).
  • Batch:
    • Resets the flag at the start of a new generation; filters stop tokens; chat stop tokens optional via use_chat_stop_tokens=True.
  • Relevant tests:
    • tests_2.0/test_ctrl_c_handling.py (SIGINT, interruption behavior, interactive)
    • tests_2.0/test_interruption_recovery.py (resetting the flag for new generations)
    • tests_2.0/test_runner_core.py (consistency/batch/streaming, error handling)

Server Minimal Tests

  • Dependencies: httpx, fastapi, uvicorn, pydantic (via [test]).
  • Scope: OpenAIcompatible endpoints (minimal smoke); no real models required.
  • Optional for local verification; in CI currently “nice to have” (Backlog, not part of the 2.0 Guide).

Known Warnings

  • urllib3 LibreSSL notice on macOS Python 3.9
    • Message: “urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3' …”

    • Status: Harmless for our usage; suppressed in production code (see mlxk2/__init__.py, warnings.filterwarnings(...)).

    • Tests: May still appear in pytest summary if thirdparty dependencies import urllib3 before our package.

    • Optional suppression in tests: add to pytest.ini:

      filterwarnings =
          ignore:urllib3 v2 only supports OpenSSL 1.1.1+
      
  • Notes:
    • Live test does not use --create (safety). If the repo does not exist, create it once manually.
    • Manual create example: mlxk2 push --private --create "$MLXK2_LIVE_WORKSPACE" "$MLXK2_LIVE_REPO" --json

Manual Checklist (Live)

  • Create repo (first time): --private --create → expect created_repo: true, private repo on HF.
  • No-op re-push: identical workspace → no_changes: true, uploaded_files_count: 0, concise human "no changes".
  • Commit after change: edit a small file → push shows commit_sha, commit_url, change_summary matches expectations.
  • .hfignore behavior: add ignores (e.g., .idea/, .vscode/, *.ipynb) → verify excluded on HF.
  • Optional errors: invalid token or missing rights → JSON error (upload_failed / auth error), clear message.

Human vs JSON:

  • Human output is derived from JSON only; hub logs are not printed directly.
  • Use --verbose with human output to append the commit URL or short message; JSON content stays the same structurally.

Manual MLX Chat Model Smoke Test (2.0)

Goal: Pull a small MLX chat model, verify classification, prepare a local workspace, validate it offline, and push to a private repo while preserving chat intent. This helps issuers validate iOSfocused workflows.

Model choice (example)

  • mlx-community/Qwen2.5-0.5B-Instruct-4bit (small, chatoriented)

Steps

  • Pull (venv39):
    • mlxk2 pull mlx-community/Qwen2.5-0.5B-Instruct-4bit
  • Verify in cache:
    • mlxk2 list --health "Qwen2.5-0.5B-Instruct-4bit"
    • Expect: Framework MLX, Type chat, capabilities include chat
  • Prepare local workspace from cache (dereference symlinks):
    • Ensure HF_HOME points to your HF cache (optional, but recommended)
    • Compute cache path: $HF_HOME/models--mlx-community--Qwen2.5-0.5B-Instruct-4bit
    • Find latest snapshot hash under snapshots/
    • Copy to workspace and dereference symlinks:
      • rsync -aL "$HF_HOME/models--mlx-community--Qwen2.5-0.5B-Instruct-4bit/snapshots/<HASH>/" ./mymodel_test_workspace/
  • Recommended README frontmatter (to preserve intent on push):
    • Include YAML with tags and pipeline tag, e.g.
      • tags: [mlx, chat]
      • pipeline_tag: text-generation
      • base_model: <upstream_base>
    • Keep model name containing Instruct or chat to aid chat detection
  • Offline validation (no network):
    • mlxk2 push --check-only ./mymodel_test_workspace <org/model> --json
    • Expect: workspace_health.healthy: true; ensure tokenizer present (tokenizer.json or tokenizer.model) and at least one nonempty weight file
  • Push to private repo:
    • mlxk2 push --private --create ./mymodel_test_workspace <org/model> --json
    • Repush without changes should show no_changes: true
  • Postpush verification:
    • mlxk2 list --all --health <org/model>
    • Current limitation: Framework may show PyTorch for nonmlx-community orgs due to conservative detection. This does not affect content; future M1 will parse model card tags (mlx) to classify MLX across orgs.

Notes

  • Ensure tokenizer files exist (tokenizer.json/tokenizer.model) and optional generation_config.json for runnable chat contexts.
  • Avoid pushing unwanted files; use .hfignore for projectspecific filters.

1.x Legacy Test Suite (separate)

  • Location: tests/ (stable 1.x release on main).
  • Not part of the 2.0 default run; execute explicitly with pytest tests/ -v.
  • Contains extensive integration/server tests unrelated to the 2.0 JSON CLI.

Legacy 1.x: 3-Category Test Strategy (main)

MLX Knife uses a 3-category test strategy to balance test isolation, performance, and user cache protection:

🏠 CATEGORY 1: ISOLATED CACHE (Most Tests)

User cache stays pristine - Tests use temporary isolated caches with automatic cleanup

Implemented Tests (78 tests):

  • test_real_model_lifecycle.py - Full model lifecycle with tiny-random-gpt2 (~12MB download)
  • test_core_functionality.py - Basic CLI operations with patch_model_cache isolation
  • test_process_lifecycle.py - Process management with isolated cache + MODEL_CACHE patching
  • test_run_command_advanced.py - Run command edge cases with mock_model_cache in isolation
  • test_lock_cleanup_bug.py - Lock cleanup testing with temporary MODEL_CACHE override
  • test_health_checks.py - Mock corruption testing with isolated temp_cache_dir

Technical Pattern:

@pytest.mark.usefixtures("temp_cache_dir")
class TestBasicLifecycle:
    def test_something(self, temp_cache_dir, patch_model_cache):
        with patch_model_cache(temp_cache_dir / "hub"):
            # Test operates in complete isolation
            # User cache never touched, automatic cleanup

Benefits:

  • Clean User Cache: No test artifacts or broken models ever
  • Parallel Testing: No cache conflicts between test runs
  • Reproducible: No dependency on existing models in user cache
  • Fast CI: Small models (12MB vs 4GB) for most tests

🏥 CATEGORY 2: USER CACHE (Framework Diversity)

📋 Reserved for future - Real model diversity that cannot be mocked

Future Framework Validation Tests:

  • Multiple framework detection (MLX + PyTorch + Tokenizer-only models)
  • Health check diversity testing with naturally corrupted models
  • Cross-framework model compatibility validation

Currently: All health/framework tests use mock_model_cache and are Category 1 (isolated)

🖥️ CATEGORY 3: SERVER CACHE (Performance Tests)

🔒 Large models, user cache expected - Marked with @pytest.mark.server

Server Tests (Excluded from default pytest):

  • 🔒 test_issue_14.py - Chat self-conversation regression tests
  • 🔒 test_issue_15_16.py - Dynamic token limit validation
  • 🔒 test_end_token_issue.py - End-token filtering consistency
  • 🔒 test_server_functionality.py - OpenAI API compliance (basic tests only)

Technical Pattern:

@pytest.mark.server  # Excluded from default pytest
def test_server_feature(mlx_server, model_name: str):
    # Uses real models in user cache
    # Requires significant RAM and time

Characteristics:

  • 🔒 Not run by default - Must use pytest -m server
  • 💾 RAM-aware - Auto-skip models exceeding available memory
  • ⏱️ Longer execution - 20-40 minutes for full suite
  • 🎯 Model diversity - Tests across different model sizes/architectures

Test Prerequisites

Required Setup

  1. Apple Silicon Mac (M1/M2/M3)
  2. Python 3.9 or newer
  3. Test dependencies installed (includes jsonschema for Spec tests):
    pip install -e .[test]
    

Notes:

  • Spec validation requires jsonschema. Installing .[test] ensures it is available.
  • Without jsonschema, Spec example validation is skipped (you will see one extra SKIPPED test).
  • With jsonschema installed, expect one additional PASS in the -m spec and tests_2.0/ totals.

That's it! Most tests (Category 1) use isolated caches and download small test models automatically (~12MB).

Enabling Issue #27 Tests (optional)

Quick start (minimal)

  • Best practice: set your HF cache to an external volume before pytest: export HF_HOME=/Volumes/your-ssd/huggingface/cache.
  • Select a model: export MLXK2_ISSUE27_MODEL="org/model".
    • Tip: choose an upstream repo that provides an index file (model.safetensors.index.json or pytorch_model.bin.index.json) to avoid SKIPs.
  • Optional: if your cache has no index file for this repo, enable isolated index bootstrap (indexonly, no shards): export MLXK2_BOOTSTRAP_INDEX=1.
  • Run: pytest tests_2.0/test_issue_27.py -v.

Notes

  • Tests read from your user cache and copy a minimal subset into an isolated test cache.

  • Network is only used when MLXK2_BOOTSTRAP_INDEX=1 and the index file is not present locally.

  • Set your user cache:

    • EITHER set MLXK2_USER_HF_HOME=/absolute/path/to/your/huggingface/cache
    • OR set HF_HOME=/absolute/path/to/your/huggingface/cache before running pytest — the test harness preserves this original value and exposes it to the Issue #27 helpers while still isolating HF_HOME for the code under test.
  • Select a specific upstream model that includes an index file (strongly recommended):

    • export MLXK2_ISSUE27_MODEL="mistralai/Mixtral-8x7B-Instruct-v0.1"
    • or another upstream PyTorch repo that contains model.safetensors.index.json or pytorch_model.bin.index.json.
    • Note: Many mlx-community/... conversions do not ship the upstream safetensors index; prefer the original upstream repo to avoid SKIPs.
  • Minimize copy size (optional):

    • export MLXK2_SUBSET_COUNT=1 (Default 1; erhöht ggf. ShardAnzahl)
    • export MLXK2_MIN_FREE_MB=512 (Default 512 MB Sicherheitsmarge)
  • Run the focused tests: PYTHONPATH=. pytest tests_2.0/test_issue_27.py -v

Optional bootstrap (opt-in, minimal workflow):

  • Minimal preconditions to run all Issue #27 tests without SKIPs:
    • Select models to test:
      • Healthy check model (read-only): export MLXK2_ISSUE27_MODEL="org/model" (should be present and healthy in your user cache; single-shard small models are ideal, e.g., sshleifer/tiny-gpt2).
      • Index tests model (optional, can be different): export MLXK2_ISSUE27_INDEX_MODEL="org/model-with-index" (upstream repo that lists an index; not required to be fully downloaded locally).
  • Ensure your user cache root is set via MLXK2_USER_HF_HOME (or provide it via HF_HOME before pytest; the harness maps it across).
    • Enable index bootstrap: export MLXK2_BOOTSTRAP_INDEX=1 (fetches only index files into the ISOLATED test cache; never modifies your user cache).
    • Then: pytest tests_2.0/test_issue_27.py -v
    • Note: Network is only needed if your user cache does not already contain an index file for the chosen repo. If the index exists in your cache, the tests copy it into the isolated cache and no network is required.

If you still see SKIPs:

  • “No safetensors index found” → The chosen model snapshot lacks an index file. Pick a model that has model.safetensors.index.json (or pytorch_model.bin.index.json).
  • “Not enough free space” → Free disk space; tests create a subset copy into an isolated temp cache.
  • “User model not found” → Verify your model exists in the user cache and MLXK2_USER_HF_HOME points to the .../huggingface/cache root.

Quick helper to list indexbearing models in your user cache:

find "$MLXK2_USER_HF_HOME/hub" -type f \
  \( -name 'model.safetensors.index.json' -o -name 'pytorch_model.bin.index.json' \) \
| sed 's#.*/hub/models--\(.*\)/snapshots/.*#\1#; s#--#/#g' | sort -u

With a suitable model (i.e., one that includes an upstream safetensors index) present and MLXK2_USER_HF_HOME set, the Issue #27 tests should run without SKIPs.

When Issue #27 realmodel tests make sense

Purpose

  • These tests validate the strict health policy against real upstream Hugging Face repositories that ship multishard safetensors with a model.safetensors.index.json. They complement the deterministic unit tests by exercising realworld layouts.

Run them when

  • Your user cache contains at least one upstream PyTorch repo with a safetensors index (not MLX/GGUF conversions). Good candidates:
    • mistralai/Mistral-7B-Instruct-v0.2 or -v0.3
    • Qwen/Qwen1.5-7B-Chat, Qwen/Qwen2-7B-Instruct
    • teknium/OpenHermes-2.5-Mistral
    • Gated: meta-llama/Llama-2-7b-chat-hf, meta-llama/Llama-3-8B-Instruct, google/gemma-7b-it
  • You want to sanitycheck indexbased completeness, shard deletion/truncation, and LFS pointer detection against real artifacts.

They are not useful when

  • Your cache only has MLX Community models (no model.safetensors.index.json) or GGUF models — the indexbased tests will skip by design. In that case, rely on tests_2.0/test_health_multifile.py for deterministic coverage.

  • Resource considerations

  • Disk: tests copy a minimal subset of files into an isolated cache (index + 1 smallest shard, oder 1 PatternShard). Optional Tuning:

    • export MLXK2_SUBSET_COUNT="1" (Default 1; erhöhe bei Bedarf)
    • export MLXK2_MIN_FREE_MB="512" (Default 512 MB; erhöhe bei knappem Platz)
  • Network: if you need to fetch a candidate model first, prefer downloading only config.json, model.safetensors.index.json, and 12 small shards to keep it light.

Summary

  • If you have a suitable upstream PyTorch chat/instruct model with an index in your user cache, enable the env vars above and run tests_2.0/test_issue_27.py for an extra layer of realmodel assurance. Otherwise, the deterministic tests already validate the policy thoroughly.

Optional Setup (Server Tests Only)

For server tests (@pytest.mark.server - excluded by default):

# Medium model for server testing
mlxk pull mlx-community/Phi-3-mini-4k-instruct-4bit

# Different architecture for variety  
mlxk pull mlx-community/Mistral-7B-Instruct-v0.3-4bit

Note: Server tests are excluded from default pytest and require manual execution with pytest -m server.

Environment & Caches

To keep results reproducible and caches safe on Apple Silicon:

  • Preferred Python/venv: Applenative 3.9 in a dedicated env
    • Example: python3.9 -m venv venv39 && source venv39/bin/activate && pip install -e .[test]
  • User cache (persistent): shared, real cache for manual ops and certain advanced/server tests
    • Example (external SSD): export HF_HOME="/Volumes/SomeExternalSSD/models"
    • Safe ops: list, health, show; Coordinate pull/rm (maintenance window)
  • Test cache (isolated/default): ephemeral via fixtures; default pytest runs must not force the user cache
    • Category 1 tests use temporary caches and should not depend on HF_HOME
    • Only server/advanced tests may require user cache and are excluded by default (-m server)
    • Deletion safety: tests set MLXK2_STRICT_TEST_DELETE=1 so delete ops fail if not in test cache

In PRs, please state your Python version and whether you used the user cache or isolated test caches.

Test Commands

Basic Test Execution

# All tests (recommended before commits)
pytest

# Only integration tests (system-level)
pytest tests/integration/

# Only unit tests (fast)
pytest tests/unit/

# Verbose output
pytest -v

# Show test coverage
pytest --cov=mlx_knife --cov-report=html

Specific Test Categories

# Process lifecycle tests (critical for production)
pytest tests/integration/test_process_lifecycle.py -v

# Health check robustness (model corruption detection)
pytest tests/integration/test_health_checks.py -v

# Core functionality (basic CLI commands)
pytest tests/integration/test_core_functionality.py -v

# Issue #20: End-token filtering consistency (new in 1.1.0-beta2)
pytest tests/integration/test_end_token_issue.py -v

# Advanced run command tests
pytest tests/integration/test_run_command_advanced.py -v

# Server functionality tests
pytest tests/integration/test_server_functionality.py -v

# Lock cleanup bug tests (Issue #23 - new in 1.1.0-beta3)
pytest tests/integration/test_lock_cleanup_bug.py -v

Test Filtering

# Run only basic operations tests
pytest -k "TestBasicOperations" -v

# Server tests are excluded by default (marked with @pytest.mark.server)
# Run server tests manually (requires large models in user cache)
pytest -m server -v

# Skip server tests explicitly (default behavior)
pytest -m "not server" -v

# Run only process lifecycle tests
pytest -k "process_lifecycle or zombie" -v

# Run health check tests only
pytest -k "health" -v

# Only JSON API contract/spec tests
pytest -m spec -v

Timeout and Performance

# Set custom timeout (default: 300s)
pytest --timeout=60

# Show slowest tests
pytest --durations=10

# Parallel execution (if pytest-xdist installed)
pytest -n auto

Server Tests (Advanced)

⚠️ Warning: Server tests require significant system resources and time.

# Run comprehensive Issue #20 server tests (48 tests, ~30 minutes)
pytest tests/integration/test_end_token_issue.py -m server -v

# All server-marked tests (includes above + server functionality)
pytest -m server -v

# Quick server functionality test only
pytest tests/integration/test_server_functionality.py -v

# Server tests are RAM-aware - automatically skip models that don't fit

Server Test Requirements:

  • RAM: 8GB+ recommended (16GB+ for large models)
  • Time: 20-40 minutes for full suite
  • Models: Multiple 4-bit quantized models (1B-30B parameters)
  • Coverage: Streaming vs non-streaming consistency, token limits, API compliance

Python Version Compatibility

Verification Results (September 2025)

160/160 tests passing - All standard tests validated on Apple Silicon with isolated cache system

Python Version Status Tests Passing
3.9.6 (macOS) Verified 160/160
3.10.x Verified 160/160
3.11.x Verified 160/160
3.12.x Verified 160/160
3.13.x Verified 160/160

All versions tested with isolated cache system. Real MLX execution verified separately with server/run commands.

Manual Multi-Python Testing

If you have multiple Python versions installed, you can verify compatibility:

# Run the multi-Python verification script
./test-multi-python.sh

# Or manually test specific versions
python3.9 -m venv test_39
source test_39/bin/activate
pip install -e . && pip install pytest
pytest
deactivate && rm -rf test_39

Code Quality & Development

Code Quality Tools

MLX Knife includes comprehensive code quality tools:

# Install development tools
pip install ruff mypy

# Automatic code formatting and linting
ruff check mlx_knife/ --fix

# Type checking with mypy
mypy mlx_knife/

# Complete development workflow
ruff check mlx_knife/ --fix && mypy mlx_knife/ && pytest

MiniMatrix: What runs by default vs markers

Target How to Run Markers / Env Includes Network
Default 2.0 suite pytest -v JSONAPI (list/show/health), HumanOutput, ModelResolution, HealthPolicy, Push Offline (--check-only, --dry-run), Spec/Schema checks No
Speconly pytest -m spec -v spec Schema/contract tests, version sync, docs example validation No
Exclude Spec pytest -m "not spec" -v not spec Everything except spec/schema checks No
Push (experimental, optin) MLXK2_ENABLE_EXPERIMENTAL_PUSH=1 pytest -k push -v Env: MLXK2_ENABLE_EXPERIMENTAL_PUSH=1 Push offline tests (--check-only, --dry-run); push command hidden by default No
Live Push (optin) MLXK2_ENABLE_EXPERIMENTAL_PUSH=1 pytest -m live_push -v live_push (subset of wet) + Env: MLXK2_ENABLE_EXPERIMENTAL_PUSH=1, MLXK2_LIVE_PUSH=1, HF_TOKEN, MLXK2_LIVE_REPO, MLXK2_LIVE_WORKSPACE JSON push against the real Hub; on errors the test SKIPs (diagnostic) Yes
Issue #27 realmodel (optin) pytest -m issue27 tests_2.0/test_issue_27.py -v Marker: issue27; Env (required): MLXK2_USER_HF_HOME or HF_HOME (user cache, readonly). Env (optional): MLXK2_ISSUE27_MODEL, MLXK2_ISSUE27_INDEX_MODEL, MLXK2_SUBSET_COUNT=0. Copies real models from user cache into isolated test cache; validates strict health policy on indexbased models (no network) No (uses local cache)
Server/run (separate) pytest tests/integration -m server -v server Heavy server/run tests, RAMdependent, longer duration No (models local)

Useful commands

  • Only Spec: pytest -m spec -v
  • Push tests (offline): MLXK2_ENABLE_EXPERIMENTAL_PUSH=1 pytest -k "push and not live" -v
  • Exclude Spec: pytest -m "not spec" -v
  • Live Push only: MLXK2_ENABLE_EXPERIMENTAL_PUSH=1 MLXK2_LIVE_PUSH=1 HF_TOKEN=... MLXK2_LIVE_REPO=... MLXK2_LIVE_WORKSPACE=... pytest -m live_push -v
  • Issue #27 only: MLXK2_USER_HF_HOME=/path/to/user/cache pytest -m issue27 tests_2.0/test_issue_27.py -v
  • All live tests (umbrella): MLXK2_ENABLE_EXPERIMENTAL_PUSH=1 pytest -m wet -v (may include future live tests beyond push)

Markers: wet vs live_push

  • wet: umbrella marker for any optin “live” test that may require network, credentials, or user environment. Use to run all live tests.
  • live_push: narrow marker for pushspecific live tests only. Use to target push live checks without running other live suites.

Note: Without the required env vars, live tests remain SKIPPED.

Development Workflow

Before committing changes:

#!/bin/bash
# pre-commit-check.sh - Run before committing
set -e

echo "🧪 Running MLX Knife pre-commit checks..."

# 1. Code style
echo "Checking code style..."
ruff check mlx_knife/ --fix

# 2. Type checking
echo "Checking types..."
mypy mlx_knife/

# 3. Quick smoke test
echo "Running quick tests..."
pytest tests/unit/ -v

echo "✅ All checks passed. Safe to commit!"

Local Development Testing

Adding New Tests

  1. Integration tests go in tests/integration/
  2. Unit tests go in tests/unit/
  3. Use existing fixtures from conftest.py
  4. Follow naming: test_*.py, Test* classes, test_* methods

Test Categories (Markers)

@pytest.mark.integration  # Slower system tests
@pytest.mark.unit         # Fast isolated tests  
@pytest.mark.slow         # Tests >30 seconds
@pytest.mark.requires_model  # Needs actual MLX model
@pytest.mark.network      # Requires internet
@pytest.mark.server       # Requires MLX Knife server (excluded from default pytest)

Mock Utilities

  • mock_model_cache(): Creates fake model directories
  • mlx_knife_process(): Manages subprocess lifecycle
  • process_monitor(): Tracks zombie processes
  • temp_cache_dir(): Isolated test environment

Test Philosophy

Following the "Process Hygiene over Edge-Case Perfection" principle:

  1. Process Cleanliness: No zombies, no leaks
  2. Health Checks: Reliable corruption detection
  3. Core Operations: Basic functionality works
  4. Error Handling: Graceful failures

The test suite validates production readiness with real Apple Silicon hardware and actual MLX models.

Troubleshooting

Common Issues

Tests hang forever:

pytest --timeout=60

Import errors:

pip install -e . && pip install pytest

Process cleanup issues:

ps aux | grep mlx_knife  # Check for zombies

Cache conflicts:

export HF_HOME="/tmp/test_cache"
pytest --cache-clear

Test Environment

# Clean test run
rm -rf .pytest_cache __pycache__
pytest tests/ -v --cache-clear

# Debug specific test
pytest tests/integration/test_health_checks.py::TestHealthCheckRobustness::test_healthy_model_detection -v -s

Contributing Test Results

When submitting PRs, please include:

  1. Your test environment:

    • macOS version
    • Apple Silicon chip (M1/M2/M3)
    • Python version
    • Which model(s) you tested with
  2. Test results summary (2.0):

Platform: macOS 14.5, M2 Pro
Python: 3.11.6
Results: 98/98 tests passed; 9 skipped (opt-in)
  1. Any issues encountered and how you resolved them

Summary

Legacy 1.x Testing Status (main):

Stable - 150/150 tests passing
Isolated Test System - User cache stays pristine with temp cache isolation 3-Category Strategy - Optimized for performance and safety Multi-Python Support - Python 3.9-3.13 verified
Code Quality - ruff/mypy integration working
Real Model Testing - Server/run commands validated with multiple models Memory Management - Context managers prevent leaks
Exception Safety - Context managers ensure cleanup
Cache Directory Fix - Issue #21: Empty cache crash resolved LibreSSL Warning Fix - Issue #22: macOS Python 3.9 warning suppression Lock Cleanup Fix - Issue #23: Enhanced rm command with lock cleanup

This testing framework validates MLX Knife's stability through isolated testing with automatic model downloads and separate real MLX validation.

Server-Based Testing (Legacy 1.x; 2.0 RC planned)

In 1.x (main), some tests require a running MLX Knife server with loaded models and are marked with @pytest.mark.server. For 2.0, server/run will return in the RC; until then, server tests are legacy-only.

Why Separate Server Tests?

  • Test count varies by loaded models (makes CI reporting inconsistent)
  • Large memory requirements - need different models for different RAM sizes
  • Longer execution time - each model needs to load individually
  • Manual setup required - need to download appropriate models first

Prerequisites for Server Tests

System RAM Recommended Models Commands
16GB Small models only mlxk pull mlx-community/Qwen2.5-0.5B-Instruct-4bit
mlxk pull mlx-community/Llama-3.2-1B-Instruct-4bit
mlxk pull mlx-community/Llama-3.2-3B-Instruct-4bit
32GB + Medium models mlxk pull mlx-community/Phi-3-mini-4k-instruct-4bit
mlxk pull mlx-community/Mistral-7B-Instruct-v0.2-4bit
mlxk pull mlx-community/Mixtral-8x7B-Instruct-v0.1-4bit
64GB + Large models mlxk pull mlx-community/Mistral-Small-3.2-24B-Instruct-2506-4bit
mlxk pull mlx-community/Qwen3-30B-A3B-Instruct-2507-4bit
mlxk pull mlx-community/Llama-3.3-70B-Instruct-4bit
96GB+ + Huge models mlxk pull mlx-community/Qwen3-Coder-480B-A35B-Instruct-4bit

Running Server Tests

Issue #14 Regression Tests (Chat Self-Conversation Bug):

# Set environment
export HF_HOME=/path/to/your/cache

# Smoke test first (see which models are available)
python tests/integration/test_issue_14.py

# Run server tests only (excluded from default pytest)
pytest -m server -v

# Run specific Issue #14 tests
pytest tests/integration/test_issue_14.py -m server -v

Expected Output:

🦫 MLX Knife Issue #14 Test - Smoke Test
==================================================
📊 Safe models for this system: 6
💾 System RAM: 64GB total, 40GB available

  🎯 mlx-community/Mistral-7B-Instruct-v0.2-4bit
     └─ Size: 7B, RAM needed: 8GB
  🎯 mlx-community/Llama-3.2-3B-Instruct-4bit  
     └─ Size: 3B, RAM needed: 4GB
  [...]

========== test session starts ==========
tests/integration/test_issue_14.py::test_server_health[mlx_server] PASSED
tests/integration/test_issue_14.py::test_issue_14_self_conversation_regression_original[mlx-community/Mistral-7B-Instruct-v0.2-4bit-7B-8] PASSED
[...6 more model tests...]
========== 7 passed in 45.23s ==========

Additional Server Tests

Issues #15 & #16 - Dynamic Token Limits (Implemented in 1.1.0-beta1):

pytest tests/integration/test_issue_15_16.py -v

Issue #20 - End-Token Filtering (Implemented in 1.1.0-beta2):

pytest tests/integration/test_end_token_issue.py -m server -v

Troubleshooting Server Tests

Permission warnings are normal:

WARNING: ⚠️  Cannot scan network connections (permission denied)
INFO: 🔧 Falling back to process-based cleanup only

This is expected on macOS - the tests continue with process-based cleanup.

Memory issues:

  • Tests automatically skip models exceeding 80% available RAM
  • Use smaller models if you see consistent memory failures
  • Consider external SSD for model cache to reduce memory pressure

Server startup failures:

# Debug server manually
python -m mlx_knife.cli server --port 8000

# Check model health  
mlxk health

# Verify environment
echo $HF_HOME

Adding New Server Tests

When contributing server-based tests:

@pytest.mark.server
def test_new_feature(mlx_server, model_name: str, size_str: str, ram_needed: int):
    """Test new feature with MLX models.""" 
    # Use mlx_server fixture for automatic server management
    # Test implementation here
  1. Mark with @pytest.mark.server - excludes from default pytest
  2. Use mlx_server fixture - automatic server lifecycle management
  3. Test RAM requirements - use get_safe_models_for_system() helper
  4. Document in TESTING.md - add to this guide