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AGENTS.md

Guidelines for AI coding agents working in the MLPerf Inference Endpoint Benchmarking System repository.

Project Overview

High-performance benchmarking tool for LLM inference endpoints targeting 50k+ QPS. Python 3.12+, Apache 2.0 licensed.

Common Commands

# Development setup
python3.12 -m venv venv && source venv/bin/activate
pip install -e ".[dev,test]"
pre-commit install

# Testing
pytest                                        # All tests (excludes slow/performance)
pytest -m unit                                # Unit tests only
pytest -m integration                         # Integration tests only
pytest --cov=src --cov-report=html            # With coverage
pytest -xvs tests/unit/path/to/test_file.py  # Single test file

# Code quality (run before commits)
pre-commit run --all-files

# Local testing with echo server
python -m inference_endpoint.testing.echo_server --port 8765
inference-endpoint probe --endpoints http://localhost:8765 --model test-model

# CLI usage
inference-endpoint benchmark offline --endpoints URL --model NAME --dataset PATH
inference-endpoint benchmark online --endpoints URL --model NAME --dataset PATH --load-pattern poisson --target-qps 100
inference-endpoint benchmark from-config --config config.yaml

Architecture

Core Data Flow

Dataset Manager --> Load Generator --> Endpoint Client --> External Endpoint
                        |
                   Metrics Collector (EventRecorder + MetricsReporter)

Key Components

Component Location Purpose
Load Generator src/inference_endpoint/load_generator/ Central orchestrator: BenchmarkSession owns the lifecycle, Scheduler controls timing, LoadGenerator issues queries
Endpoint Client src/inference_endpoint/endpoint_client/ Multi-process HTTP workers communicating via ZMQ IPC. HTTPEndpointClient is the main entry point
Dataset Manager src/inference_endpoint/dataset_manager/ Loads JSONL, HuggingFace, CSV, JSON, Parquet datasets. Dataset base class with load_sample()/num_samples() interface
Metrics src/inference_endpoint/metrics/ EventRecorder writes to SQLite, MetricsReporter reads and aggregates (QPS, latency, TTFT, TPOT)
Config src/inference_endpoint/config/, endpoint_client/config.py Pydantic-based YAML schema (schema.py), HTTPClientConfig (single Pydantic model for CLI/YAML/runtime), RuntimeSettings
CLI src/inference_endpoint/main.py, commands/benchmark/cli.py cyclopts-based, auto-generated from schema.py and HTTPClientConfig Pydantic models. Flat shorthands via cyclopts.Parameter(alias=...)
Async Utils src/inference_endpoint/async_utils/ LoopManager (uvloop + eager_task_factory), ZMQ transport layer, event publisher
OpenAI/SGLang src/inference_endpoint/openai/, sglang/ Protocol adapters and response accumulators for different API formats

Hot-Path Architecture

Multi-process, event-loop design optimized for throughput:

  • BenchmarkSession thread schedules samples with busy-wait timing
  • Worker processes (N instances) handle HTTP requests via ZMQ IPC
  • Uses eager_task_factory and uvloop for minimal async overhead
  • CPU affinity support (cpu_affinity.py) for performance tuning
  • Custom HTTP connection pooling (http.py) with httptools parser

CLI Modes

CLI is auto-generated from config/schema.py Pydantic models via cyclopts. Fields annotated with cyclopts.Parameter(alias="--flag") get flat shorthands; all other fields get auto-generated dotted flags (kebab-case).

  • CLI mode (offline/online): cyclopts constructs OfflineBenchmarkConfig/OnlineBenchmarkConfig (subclasses in config/schema.py) directly from CLI args. Type locked via Literal. --dataset is repeatable with TOML-style format [perf|acc:]<path>[,key=value...] (e.g. --dataset data.csv,samples=500,parser.prompt=article). Full accuracy support via accuracy_config.eval_method=pass_at_1 etc.
  • YAML mode (from-config): BenchmarkConfig.from_yaml_file() loads YAML, resolves env vars, and auto-selects the right subclass via Pydantic discriminated union. Optional --timeout/--mode overrides via config.with_updates().
  • eval: Not yet implemented (raises CLIError with a tracking issue link)

Config Construction & Validation

Both CLI and YAML produce the same subclass via Pydantic discriminated union on type:

CLI offline/online:  cyclopts → OfflineBenchmarkConfig/OnlineBenchmarkConfig → with_updates(datasets) → run_benchmark
YAML from-config:    from_yaml_file(path) → discriminated union → same subclass → run_benchmark

OfflineBenchmarkConfig and OnlineBenchmarkConfig (in config/schema.py) inherit BenchmarkConfig:

  • type: locked via Literal[TestType.OFFLINE] / Literal[TestType.ONLINE]
  • settings: OfflineSettings (hides load pattern) / OnlineSettings
  • submission_ref, benchmark_mode: show=False on base class

Validation is layered:

  1. Field-level (Pydantic): Field(ge=0) on durations, Field(ge=-1) on workers, Literal on benchmark_mode
  2. Field validators: workers != 0 check
  3. Model validator (_resolve_and_validate): streaming AUTO resolution, model name from submission_ref, load pattern vs test type, cross-field duration check, duplicate datasets

Load Patterns

  • max_throughput: Offline burst (all queries at t=0)
  • poisson: Fixed QPS with Poisson arrival distribution
  • concurrency: Fixed concurrent requests

Code Organization

src/inference_endpoint/
├── main.py                    # Entry point + CLI app: cyclopts app, commands, error formatter, run()
├── exceptions.py              # CLIError, ExecutionError, InputValidationError, SetupError
├── commands/                  # Command execution logic
│   ├── benchmark/
│   │   ├── __init__.py
│   │   ├── cli.py             # benchmark_app: offline, online, from-config subcommands
│   │   └── execute.py         # Phased execution: setup/run_threaded/finalize + BenchmarkContext
│   ├── probe.py               # ProbeConfig + execute_probe()
│   ├── info.py                # execute_info()
│   ├── validate.py            # execute_validate()
│   └── init.py                # execute_init()
├── core/types.py              # APIType, Query, QueryResult, StreamChunk, QueryStatus (msgspec Structs)
├── load_generator/
│   ├── session.py             # BenchmarkSession - top-level orchestrator
│   ├── load_generator.py      # LoadGenerator, SchedulerBasedLoadGenerator
│   ├── scheduler.py           # Scheduler, timing strategies
│   ├── sample.py              # SampleEventHandler
│   └── events.py              # SessionEvent, SampleEvent enums
├── endpoint_client/
│   ├── http_client.py         # HTTPEndpointClient - main client interface
│   ├── worker.py              # Worker process implementation
│   ├── worker_manager.py      # Manages worker lifecycle
│   ├── http.py                # ConnectionPool, HttpRequestTemplate, raw HTTP
│   ├── http_sample_issuer.py  # Bridges load generator to HTTP client
│   ├── config.py              # HTTPClientConfig (single Pydantic model — CLI/YAML/runtime)
│   ├── adapter_protocol.py    # HttpRequestAdapter protocol
│   ├── accumulator_protocol.py # Response accumulation protocol
│   ├── cpu_affinity.py        # CPU pinning
│   └── utils.py               # Port range helpers
├── async_utils/
│   ├── loop_manager.py        # LoopManager (uvloop + eager_task_factory)
│   ├── runner.py              # run_async() — uvloop + eager_task_factory entry point for CLI commands
│   ├── event_publisher.py     # Async event pub/sub
│   ├── services/
│   │   ├── event_logger/      # EventLoggerService: writes EventRecords to JSONL/SQLite
│   │   └── metrics_aggregator/ # MetricsAggregatorService: real-time metrics (TTFT, TPOT, ISL, OSL)
│   └── transport/             # ZMQ-based IPC transport layer
│       ├── protocol.py        # Transport protocols + TransportConfig base
│       ├── record.py          # Transport records
│       └── zmq/               # ZMQ implementation (context, pubsub, transport, ZMQTransportConfig)
├── dataset_manager/
│   ├── dataset.py             # Dataset base class, DatasetFormat enum
│   ├── factory.py             # Dataset factory
│   ├── transforms.py          # ColumnRemap and other transforms
│   └── predefined/            # Built-in datasets (aime25, cnndailymail, gpqa, etc.)
├── metrics/
│   ├── recorder.py            # EventRecorder (SQLite-backed)
│   ├── reporter.py            # MetricsReporter (aggregation)
│   └── metric.py              # Metric types (Throughput, etc.)
├── config/
│   ├── schema.py              # Single source of truth: Pydantic models + cyclopts annotations
│   ├── runtime_settings.py    # RuntimeSettings dataclass
│   ├── ruleset_base.py        # BenchmarkSuiteRuleset base
│   ├── ruleset_registry.py    # Ruleset registry
│   ├── user_config.py         # UserConfig dataclass for ruleset user overrides
│   ├── rulesets/mlcommons/    # MLCommons-specific rules, datasets, models
│   └── templates/             # YAML config templates (_template.yaml minimal, _template_full.yaml all defaults)
├── openai/                    # OpenAI-compatible API types and adapters
│   ├── types.py               # OpenAI response types
│   ├── openai_adapter.py      # Request/response adapter
│   ├── openai_msgspec_adapter.py  # msgspec-based adapter (fast path)
│   ├── accumulator.py         # Streaming response accumulator
│   └── harmony.py             # openai_harmony integration
├── sglang/                    # SGLang API adapter
├── evaluation/                # Accuracy evaluation (extractor, scoring, livecodebench)
├── plugins/                   # Plugin system
├── profiling/                 # line_profiler integration, pytest plugin
├── testing/
│   ├── echo_server.py         # Local echo server for testing
│   ├── max_throughput_server.py # Max throughput test server
│   └── docker_server.py       # Docker-based server management
└── utils/
    ├── logging.py             # Logging setup
    ├── version.py             # Version info
    ├── dataset_utils.py       # Dataset utilities
    └── benchmark_httpclient.py # HTTP client throughput benchmarking utility

tests/
├── conftest.py                # Shared fixtures (echo/oracle servers, datasets, settings)
├── test_helpers.py            # Test utility functions
├── unit/                      # Unit tests (mirror src/ structure)
├── integration/               # Integration tests (real servers, end-to-end)
│   ├── endpoint_client/       # HTTP client integration tests
│   └── commands/              # CLI command integration tests
├── performance/               # Performance benchmarks (pytest-benchmark)
└── datasets/                  # Test data (dummy_1k.jsonl, squad_pruned/)

Development Standards

Code Style and Pre-commit Hooks

  • Formatter/Linter: ruff (line-length 88, target Python 3.12)
  • Type checking: mypy (via pre-commit)
  • Formatting: ruff-format (double quotes, space indent)
  • License headers: Required on all Python files (enforced by pre-commit hook scripts/add_license_header.py)
  • Conventional commits: feat:, fix:, docs:, test:, chore:

Pre-commit Hooks

All of these run automatically on commit:

  • trailing-whitespace, end-of-file-fixer, check-yaml, check-merge-conflict, debug-statements
  • ruff (lint + autofix) and ruff-format
  • mypy type checking
  • prettier for YAML/JSON/Markdown
  • License header enforcement
  • regenerate-templates: auto-regenerates YAML config templates from schema defaults when schema.py, config.py, or regenerate_templates.py change

Always run pre-commit run --all-files before committing.

See Development Guide for full setup and workflow details.

Data Types & Serialization

  • Core types (Query, QueryResult, StreamChunk): msgspec.Struct with frozen=True, array_like=True, gc=False, omit_defaults=True
  • Config types: pydantic.BaseModel for validation
  • Enums: str, Enum pattern for serializable enums (e.g., LoadPatternType, APIType)
  • Serialization: msgspec.json for hot-path (ZMQ transport), pydantic for config

Testing

Coverage target: >90% for all new code.

Test markers:

@pytest.mark.unit           # Unit tests
@pytest.mark.integration    # Integration tests (may need servers)
@pytest.mark.slow           # Skip in CI
@pytest.mark.performance    # No timeout, skip in CI
@pytest.mark.run_explicitly # Only run when explicitly selected

Async tests: Use @pytest.mark.asyncio(mode="strict") — the project uses strict asyncio mode.

Key fixtures (defined in tests/conftest.py):

  • mock_http_echo_server — real HTTP echo server on dynamic port
  • mock_http_oracle_server — dataset-driven response server
  • dummy_dataset — in-memory test dataset
  • hf_squad_dataset — HuggingFace squad dataset
  • events_db — pre-populated SQLite events database
  • max_throughput_runtime_settings, poisson_runtime_settings, concurrency_runtime_settings — preset configs
  • clean_sample_event_hooks — ensures event hooks are cleared between tests

Test data: tests/datasets/dummy_1k.jsonl (1000 samples), tests/datasets/squad_pruned/

Performance Guidelines

These apply especially to code in the hot path (load generator, endpoint client, transport):

  • No match statements in hot paths — use dict dispatch instead
  • Use dataclass(slots=True) for frequently instantiated classes (or msgspec.Struct)
  • Prefer generators over list comprehensions for large datasets
  • Minimize async suspends in hot path code
  • Use msgspec over json/pydantic for serialization in the data path
  • Connection pooling: The HTTP client uses custom ConnectionPool with httptools parser — not aiohttp/requests
  • Event loop: uvloop with eager_task_factory via LoopManager
  • IPC: ZMQ-based transport between main process and worker processes

Key Dependencies

Package Purpose
uvloop Performance-optimized event loop
httptools Fast HTTP parser for custom connection pool
msgspec Fast serialization for core types and ZMQ transport
pyzmq ZMQ IPC between main process and workers
pydantic Configuration validation
cyclopts CLI framework — auto-generates flags from Pydantic
duckdb Data aggregation
transformers Tokenization for OSL reporting

Files to NOT Modify

  • src/inference_endpoint/openai/openai_types_gen.py — auto-generated, excluded from ruff/pre-commit
  • src/inference_endpoint/openai/openapi.yaml — OpenAI API spec, excluded from pre-commit

Keeping AGENTS.md Up to Date

This file is the source of truth for AI agents working in this repo. If it is stale or wrong, every AI-assisted session starts from a broken foundation.

When to Update

Update AGENTS.md as part of any PR that includes a significant refactor, meaning:

  • Moved, renamed, or deleted modules/packages — update the Code Organization tree and Key Components table
  • Changed architectural boundaries (e.g., new IPC transport, replaced pydantic with msgspec for config) — update Architecture and Data Types sections
  • Added or removed CLI commands/subcommands — update CLI Modes and Common Commands
  • Changed test infrastructure (new fixtures, changed markers, new test directories) — update Testing section
  • Added or removed key dependencies — update Key Dependencies table
  • Changed build/tooling (new pre-commit hooks, changed ruff config, new CI steps) — update docs/DEVELOPMENT.md
  • Changed hot-path patterns (new transport, changed serialization, new performance constraints) — update Performance Guidelines

How to Update

  1. Treat AGENTS.md changes as part of the refactor itself — include them in the same PR, not as a follow-up
  2. Keep descriptions factual and concise — what exists and where, not aspirational design docs
  3. If you add a new top-level module under src/inference_endpoint/, add it to both the Key Components table and the Code Organization tree
  4. If you remove something, remove it from AGENTS.md — stale entries are worse than missing ones

Reviewer Checklist

When reviewing PRs with significant structural changes, verify:

  • Code Organization tree matches the actual directory structure post-merge
  • Key Components table reflects any moved/renamed/new components
  • No references to deleted files, classes, or modules remain

Common AI Coding Pitfalls

Known failure modes when AI tools generate code for this project. Reference these during code review of AI-assisted PRs.

Architecture & Design

  • Inventing abstractions that don't exist: AI may introduce new base classes, registries, or factory patterns that don't match the existing architecture. This project uses concrete types and explicit wiring — check that new code follows existing patterns rather than imposing unfamiliar frameworks.
  • Misunderstanding the multi-process boundary: The endpoint client uses separate worker processes (not threads) communicating over ZMQ. AI-generated code often assumes shared memory, passes unpicklable objects across processes, or adds synchronization primitives (locks, semaphores) that don't work cross-process.
  • Confusing hot-path vs. cold-path: AI tends to treat all code uniformly. Code in load_generator/, endpoint_client/worker.py, and async_utils/transport/ is latency-critical. Pydantic validation, excessive logging, or try/except blocks in these paths will degrade throughput.

Serialization & Types

  • Using the wrong serialization library: This project uses msgspec for hot-path data and pydantic for config. AI frequently defaults to json.dumps/json.loads, dataclasses, or applies pydantic where msgspec is required. If the type is a msgspec.Struct, encode/decode with msgspec.json, not stdlib json.
  • Breaking msgspec Struct constraints: Core types (Query, QueryResult, StreamChunk) are frozen=True with array_like=True. AI may try to mutate fields directly (must use force_setattr), add mutable default fields, or assume dict-like serialization when the wire format is array-based.
  • Adding dataclass where msgspec.Struct is expected: If neighboring types use msgspec, new types in the same module should too. AI defaults to @dataclass out of habit.

Async & Concurrency

  • Mixing sync and async incorrectly: AI may await in a sync context, call blocking I/O inside an async def, or use asyncio.run() when a loop is already running (this project manages its own loops via LoopManager).
  • Creating new event loops: Workers and the main process already have managed event loops. AI may call asyncio.new_event_loop() or asyncio.run() which conflicts with the existing LoopManager/uvloop setup.
  • Ignoring eager_task_factory: The project uses Python 3.12's eager_task_factory for performance. AI-generated code that creates coroutines expecting lazy scheduling will behave differently than expected.

Testing

  • Generating mock-heavy tests for integration scenarios: This project has real echo/oracle server fixtures. AI tends to mock HTTP calls even when mock_http_echo_server or mock_http_oracle_server fixtures exist and should be used.
  • Missing test markers: Every test function needs @pytest.mark.unit, @pytest.mark.integration, or another marker. AI-generated tests almost always omit markers, which breaks CI filtering.
  • Wrong asyncio mode: Tests must use @pytest.mark.asyncio(mode="strict") — AI often writes bare @pytest.mark.asyncio or forgets it entirely, causing silent test skips or failures.
  • Fabricating fixture names: AI may invent fixtures that don't exist in conftest.py. Always check that referenced fixtures actually exist before using them.

Code Style & Repo Conventions

  • Missing license headers: Every Python file needs the Apache 2.0 SPDX header. AI never generates these — the pre-commit hook will add them, but be aware of this when reviewing diffs.
  • Importing removed or renamed modules: After refactors, AI (working from stale context) may import old module paths. Always verify imports resolve to actual files.
  • Over-documenting: AI generates verbose docstrings, inline comments explaining obvious code, and type annotations on trivial variables. This project prefers minimal comments — only where the why isn't obvious from the code.
  • Adding backwards-compatibility shims: If something was renamed or removed, AI may add re-exports, aliases, or deprecation wrappers. In this project, just delete the old thing and update all call sites.

Dependency & Environment

  • Adding new dependencies without justification: AI may pip install or add imports for packages not in pyproject.toml. Any new dependency must be justified, added to the correct optional group, and pinned to an exact version (==). After adding a dependency, run pip-audit (included in dev extras) to verify it has no known vulnerabilities.
  • Using requests/aiohttp for HTTP: This project has its own HTTP client (endpoint_client/http.py) using httptools. AI defaults to requests or aiohttp — these should not appear in production code (test dependencies are fine).