Parallel Constrained Decoding for Apple Silicon

Live Demo: Try the side-by-side comparison live on Hugging Face Spaces: drinkmoonshine/parallel-constrained-decoding.

A high-throughput inference engine for structured information extraction, decision routing, and categorical classification on Apple Silicon using MLX.

Parallel Constrained Decoding evaluates multi-field JSON schemas simultaneously rather than generating tokens sequentially. On an Apple Silicon M4 Max, it delivers 5.6x to 7.0x latency reductions compared to standard autoregressive decoding with 100% schema validity and calibrated field-level confidence scores.

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Performance Benchmarks (Apple Silicon M4 Max)

Evaluated with mlx-community/Qwen2.5-1.5B-Instruct-4bit on macOS Sequoia:

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Why Parallel Constrained Decoding?

The Problem with Autoregressive Structured Generation

Standard LLM structured generation (such as JSON mode or grammar-guided sampling) relies on token-by-token autoregressive decoding:

[Context Prompt] -> "{" -> "\n" -> " " -> "risk" -> ":" -> " " -> "HIGH" -> ...
(Requires 150 to 500 sequential forward passes)

Each token requires a distinct GPU/NPU forward pass and sequential memory bandwidth roundtrips. As schema size grows, latency scales linearly with output token length:

$$T_{\text{autoregressive}} = \sum_{k=1}^{K} t_{\text{step}}(k)$$

Additionally, autoregressive decoding is susceptible to syntax degradation, field omission, and hallucinated keys.

The Solution: Parallel Evaluation via KV-Cache Broadcasting

In structured extraction and classification, field values belong to bounded candidate sets (booleans or categorical enums). Parallel Constrained Decoding exploits this property:

                          +---> [Field 1: "risk_level"] -------> Logit Slicing -> Top Choice
                          |
[Context Prefix Prefill] -+---> [Field 2: "requires_review"] ---> Logit Slicing -> Top Choice
(Single KV-Cache State)   |
                          +---> [Field M: "action_tier"] ------> Logit Slicing -> Top Choice
                          
                     (All fields evaluated simultaneously)

1. Single Broadcast Prefill: The context document and semantic schema descriptions are prefilled once into an MLX Key-Value (KV) cache.
2. KV-Cache Broadcasting: The KV-cache is broadcast across all $M$ schema fields in parallel.
3. Sub-Vocabulary Logit Slicing: For each field, only candidate token IDs belonging to valid schema choices are evaluated. The remaining vocabulary is masked.
4. Calibrated Softmax Probabilities: Exact normalized probabilities are calculated over the candidate slice: $$P(c_i) = \frac{\exp(z_i / T)}{\sum_{j=1}^{C} \exp(z_j / T)}$$
5. Token Tree Disambiguation: When candidate choices share multi-token prefix roots, the engine executes continuation steps using sliced cache states with zero memory reallocation.
6. Programmatic Assembly: Output JSON is constructed directly from verified values, guaranteeing 100% valid syntax without JSON parsing errors.

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Installation

Prerequisites

• Apple Silicon Mac (M1, M2, M3, M4 series)
• macOS 14.0 or later
• Python 3.10+

Setup

Clone the repository and install dependencies:

git clone https://github.com/your-org/parallel-constrained-decoding.git
cd parallel-constrained-decoding

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

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Developer SDK Quickstart

1. Defining Schemas

Schemas are defined using StructuredSchema. Each field specifies a type (enum or boolean), a description to guide model reasoning, and choices (for enum types, supporting up to 255 choices):

from core.schema import StructuredSchema, FieldDefinition

# Option A: Dictionary-based definition
schema_dict = {
    "priority": {
        "type": "enum",
        "choices": ["P0_CRITICAL", "P1_HIGH", "P2_NORMAL", "P3_LOW"],
        "description": "Urgency tier based on customer business impact"
    },
    "requires_escalation": {
        "type": "boolean",
        "description": "Whether an on-call engineer must be notified immediately"
    },
    "department": {
        "type": "enum",
        "choices": ["BILLING", "INFRASTRUCTURE", "SECURITY", "PRODUCT_SUPPORT"],
        "description": "Target handling department"
    }
}

schema = StructuredSchema(schema_dict)

You can also construct fields explicitly using FieldDefinition:

fields = {
    "tariff_classification": FieldDefinition(
        name="tariff_classification",
        field_type="enum",
        description="Harmonized System 6-digit tariff category code",
        choices=["0101.21", "0101.29", "8471.30", "8517.12", "8542.31", ...] # Up to 255 choices
    )
}

2. Running Parallel Generation

Execute parallel constrained inference on your context string:

from core.engine import run_parallel_generation

context = """
Incident Report: Production database db-primary-01 CPU at 100%.
Payment gateway failing for 40% of checkout requests.
Tier 1 Enterprise customer affected: Acme Global.
"""

result = run_parallel_generation(context, schema)

print(f"Latency: {result['elapsed_ms']} ms")
print(f"Prefill Time: {result['prefill_ms']} ms")
print(f"Passes: {result['sequential_forward_passes']}")
print("\nExtracted JSON:")
print(result["parsed_json"])

3. Response Structure

The output dictionary provides both the structured JSON and detailed field telemetry:

{
    "mode": "parallel_constrained_calibrated",
    "elapsed_ms": 74.5,
    "prefill_ms": 52.1,
    "suffix_eval_ms": 18.2,
    "sequential_forward_passes": 1,
    "is_valid_json": True,
    "schema_match": True,
    "parsed_json": {
        "priority": { "value": "P0_CRITICAL", "prob": 0.9924 },
        "requires_escalation": { "value": "true", "prob": 0.9981 },
        "department": { "value": "INFRASTRUCTURE", "prob": 0.9815 }
    },
    "field_telemetry": {
        "priority": {
            "value": "P0_CRITICAL",
            "confidence": 0.9924,
            "cardinality": 4,
            "top_choices": [
                { "choice": "P0_CRITICAL", "probability": 0.9924 },
                { "choice": "P1_HIGH", "probability": 0.0068 },
                { "choice": "P2_NORMAL", "probability": 0.0006 },
                { "choice": "P3_LOW", "probability": 0.0002 }
            ]
        }
    }
}

4. Streaming Autoregressive Baseline

To compare against standard autoregressive generation:

from core.engine import stream_naive_generation

for event in stream_naive_generation(context, schema):
    if event["type"] == "token":
        print(event["token"], end="", flush=True)
    elif event["type"] == "done":
        print(f"\nCompleted in {event['result']['elapsed_ms']} ms")

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Interactive Web Visualizer

The repository includes a web interface for side-by-side latency and accuracy comparison.

To launch the web server:

bash run.sh

Or run directly with uvicorn:

python3 -m uvicorn server.app:app --host 0.0.0.0 --port 8000

Open http://localhost:8000 in your browser.

Features

• Side-by-Side Comparison: Parallel Constrained Decoding vs. Autoregressive Streaming.
• Live Millisecond Timers: Real-time elapsed latency counters.
• Synchronized Scrolling: Matching keys align across both panes.
• Interactive Row Highlighting: Hover over any field in either panel to highlight the corresponding key in the other.
• Hallucination Detection: Highlights omitted or hallucinated keys in naive autoregressive output.

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Command-Line Benchmark Runner

Run the benchmark suite across pre-configured enterprise presets:

python3 -m core.benchmark

Output example:

======================================================================
Parallel Constrained vs. Autoregressive Generation Benchmark
======================================================================
--> Running preset: Fintech Fraud Detection (4 fields)...
    Autoregressive Baseline :    421.3 ms | 148 tokens (122.4 tok/s) | Passes: 148
    Parallel Constrained    :     74.8 ms |   0 tokens (O(1))           | Passes: 1
    >> SPEEDUP: 5.6x faster (Step reduction: 148.0x)
    >> Schema match: Naive=True | Parallel=True (100% guaranteed)
----------------------------------------------------------------------
--> Running preset: Support Triage Matrix (28 fields)...
    Autoregressive Baseline :   1894.2 ms | 312 tokens (131.2 tok/s) | Passes: 312
    Parallel Constrained    :    268.4 ms |   0 tokens (O(1))           | Passes: 1
    >> SPEEDUP: 7.1x faster (Step reduction: 312.0x)
    >> Schema match: Naive=True | Parallel=True (100% guaranteed)
----------------------------------------------------------------------
--> Running preset: High-Cardinality Tariff (1 field, 255 choices)...
    Autoregressive Baseline :    498.7 ms |  42 tokens (116.5 tok/s) | Passes: 42
    Parallel Constrained    :     88.6 ms |   0 tokens (O(1))           | Passes: 1
    >> SPEEDUP: 5.6x faster (Step reduction: 42.0x)
    >> Schema match: Naive=True | Parallel=True (100% guaranteed)
----------------------------------------------------------------------

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Repository Structure

.
├── core/
│   ├── __init__.py           # SDK package exports
│   ├── engine.py             # Parallel constrained decoding & autoregressive engines
│   ├── schema.py             # Schema definitions, metadata compiler & logit mapping
│   ├── prompt_builder.py     # Prompt templates for prefill catalog and naive baseline
│   └── benchmark.py          # Command-line benchmark runner
├── presets/
│   ├── fintech_fraud.json    # Fraud detection scenario (4 fields)
│   ├── code_security.json    # Vulnerability audit scenario (4 fields)
│   ├── support_triage.json   # Enterprise ticket triage (28 fields)
│   └── high_cardinality_255.json # 255-choice tariff classifier
├── server/
│   ├── app.py                # FastAPI endpoints (/api/run-parallel, /api/stream-naive)
│   └── main.py               # Server launcher
├── web/
│   ├── index.html            # Side-by-side comparison UI
│   ├── app.js                # Frontend streaming & synchronized scrolling
│   └── style.css             # UI styling
├── MODEL_CARD.md             # Hugging Face model card documentation
├── requirements.txt          # Python package requirements
├── run.sh                    # Startup script
└── README.md                 # Project documentation

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Supported Models

The engine is currently configured for mlx-community/Qwen2.5-1.5B-Instruct-4bit.

Any decoder LLM supported by mlx-lm can be loaded by setting MODEL_ID in core/engine.py.

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License

Apache 2.0