README.md

---

language:
- en
license: apache-2.0
library_name: transformers
tags:
- tree-attention
- apple-m4
- mps
- pytorch
- structured-generation
- parallel-decoding
- constrained-decoding
- apple-silicon
- mlx
- classification
- json
pipeline_tag: text-generation
base_model: Qwen/Qwen2.5-1.5B-Instruct
spaces:
- drinkmoonshine/parallel-constrained-decoding
---


# Qwen-2.5-1B-RLCD-Fast

**On an Apple M4 Pro, tree attention is up to 2.37x faster for field decoding and

1.51x faster through the complete SDK. At 28 fields it reduces measured Metal

decode-allocation growth from 2,022 MiB to 40 MiB—a 98.0% reduction—while using

the same Qwen2.5-1.5B-Instruct weights and preserving the original output method.**

This is a derivative of [harshatheg/Qwen-2.5-1B-RLCD](https://huggingface.co/harshatheg/Qwen-2.5-1B-RLCD)
at commit `2af86848be75847ccb3553b0941cc51d6ef7e4e9`. The complete original model card
is preserved below; this section contains the M4-specific improvements and measurements.

This repository contains inference code, not new or fine-tuned weights. Despite the
repository name, the actual model is `Qwen/Qwen2.5-1.5B-Instruct`.

## Why it is faster and uses less memory

The original PyTorch path repeats the shared prefix KV cache for every field and evaluates
fields as a batch. The fast path stores that prefix once, packs field suffixes into a single
tree, and applies a 4D mask so each token sees the common prefix and only its own ancestors.
Branch-local position IDs preserve RoPE positions. It also projects only field endpoints
through the 151,936-token language-model head instead of projecting every suffix token.

This reduces duplicated KV storage, batch-shaped transformer work, and language-model-head
work. At 28 fields, estimated head work falls from 445.3 to 13.1 GFLOP. Fields remain
independent and cannot attend to sibling field values.

## M4 Pro performance

Apple M4 Pro, 14 cores, 48 GB, PyTorch 2.11.0, transformers 4.57.6, MPS FP16,
SDPA, and the exact base-weight revision shown above. Eight randomized paired trials and
two warmups per optimized batch/tree case.

| Preset | Fields | Batch decode (ms) | Tree decode (ms) | Decode speedup | Speedup with prefill |
|---|---:|---:|---:|---:|---:|
| fintech_fraud | 4 | 47.3 | 33.4 | **1.41x** | 1.07x |

| fintech_fraud | 16 | 166.4 | 83.1 | **2.00x** | 1.26x |
| fintech_fraud | 28 | 337.7 | 142.5 | **2.37x** | 1.41x |

| support_triage | 4 | 51.4 | 33.5 | **1.54x** | 1.09x |
| support_triage | 16 | 168.8 | 83.0 | **2.03x** | 1.27x |

| support_triage | 28 | 333.2 | 140.5 | **2.37x** | 1.41x |
| code_security | 4 | 50.9 | 34.9 | **1.46x** | 1.08x |

| code_security | 16 | 168.5 | 83.2 | **2.02x** | 1.26x |
| code_security | 28 | 331.4 | 140.7 | **2.35x** | 1.41x |



Four successive decode steps across four fields measured **2.41x**

decode speedup and **1.54x** including prefill. On the M4 Pro

CPU in FP32, 4/16/28-field decode speedups were **1.13x**,

**1.47x**, and **1.79x**.



## M4 Pro memory



| Preset | Fields | Batch extra (MiB) | Tree extra (MiB) | Extra reduction | Batch high-water (MiB) | Tree high-water (MiB) |

|---|---:|---:|---:|---:|---:|---:|

| fintech_fraud | 4 | 112 | 32 | 71.4% | 3279 | 3199 |
| fintech_fraud | 16 | 2352 | 8 | 99.7% | 5620 | 3276 |

| fintech_fraud | 28 | 2022 | 40 | 98.0% | 5295 | 3271 |
| support_triage | 4 | 104 | 8 | 92.3% | 3359 | 3263 |

| support_triage | 16 | 2352 | 24 | 99.0% | 5599 | 3271 |
| support_triage | 28 | 2062 | 48 | 97.7% | 5292 | 3276 |

| code_security | 4 | 120 | 32 | 73.3% | 3293 | 3205 |
| code_security | 16 | 2352 | 24 | 99.0% | 5618 | 3290 |

| code_security | 28 | 2022 | 32 | 98.4% | 5303 | 3271 |

These are Metal driver high-water measurements. “Extra” is growth over the pre-decode
driver allocation baseline; “high-water” includes resident model tensors and other driver
allocations. The weights are unchanged, so this is an inference-working-memory improvement,
not a smaller model.

## Direct comparison with the original SDK

The full SDK comparison includes tokenization, schema compilation, prefill, suffix evaluation,
language-model projection, and JSON assembly. Across the three 28-field MPS cases, the fast
SDK is **1.50x–1.51x faster** than the original shipped PyTorch function.

## Correctness and limitations

- Full-vocabulary next-token argmax matched in every reported run. Constrained field decisions
  also matched except one `code_security` MPS FP16 near-tie. Its FP32 top-two margin was 0.00235
  logits, below the observed FP16 rounding gap; the harness records it instead of aborting.
- FP16 logits are numerically close, not bitwise identical. Small FP32 tests compare both paths
  with independent complete-sequence decoding and verify isolation between sibling branches.
- The inherited PyTorch candidate scorer evaluates only the first token after a shared character
  prefix. It is not a complete enum-trie decoder, and normalized candidate scores are not
  empirically calibrated confidence estimates.
- These measurements apply to this M4 Pro and these prompts. Dense masking still computes masked
  sibling attention, so results can change with kernels and workloads.
- The original MLX figures below compare parallel field evaluation with sequential JSON generation.
  The new figures compare tree attention with field batching and should not be multiplied together.

## Run on Apple Silicon

```bash

git clone https://huggingface.co/epsilon3/Qwen-2.5-1B-RLCD-Fast

cd Qwen-2.5-1B-RLCD-Fast

pip install -r requirements.txt

BACKEND=torch python -m unittest -v test_tree_decode test_sdk

BACKEND=torch python generate.py --device mps --preset fintech_fraud --mode tree

BACKEND=torch python benchmark.py --device mps --presets fintech_fraud support_triage code_security --fields 4 16 28 --repeats 8 --warmup 2 --output results.json

```

The PyTorch SDK uses tree attention by default. Set `RLCD_ATTENTION=batch` to select
the inherited batching path. If MLX is installed, set `BACKEND=torch` to select MPS.

See [the complete M4 tables](M4_RESULTS.md) and the raw `mac_*.json` and `h2h_*.json` files.

---

## Original model card (preserved)

# Parallel Constrained Decoding for Apple Silicon

[![Open in Spaces](https://huggingface.co/datasets/huggingface/badges/resolve/main/open-in-hf-spaces-sm.svg)](https://huggingface.co/spaces/drinkmoonshine/parallel-constrained-decoding)

> **Live Demo**: Try the side-by-side comparison live on Hugging Face Spaces: [drinkmoonshine/parallel-constrained-decoding](https://huggingface.co/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**.

---

## Performance Benchmarks (Apple Silicon M4 Max)

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

| Scenario | Fields | Autoregressive Baseline | Parallel Constrained | Latency Speedup | Syntax Validity |
| :--- | :--- | :--- | :--- | :--- | :--- |
| **Fintech Fraud Routing** | 4 fields | 420 ms (120 tok/s) | **75 ms** | **5.6x** | 100% guaranteed |
| **Code Security Audit** | 4 fields | 380 ms (125 tok/s) | **68 ms** | **5.6x** | 100% guaranteed |
| **High-Cardinality Tariff** | 1 field (255 choices) | 500 ms (118 tok/s) | **89 ms** | **5.6x** | 100% guaranteed |
| **Enterprise Support Triage** | 28 fields | 1,900 ms (130 tok/s) | **270 ms** | **7.0x** | 100% guaranteed |

---

## 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.

---

## 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:

```bash

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

```

---

## 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):

```python

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`:

```python

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:

```python

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:

```python

{

    "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:

```python

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")

```

---

## Interactive Web Visualizer

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

To launch the web server:

```bash

bash run.sh

```

Or run directly with uvicorn:

```bash

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.

---

## Command-Line Benchmark Runner

Run the benchmark suite across pre-configured enterprise presets:

```bash

python3 -m core.benchmark

```

Output example:

```text

======================================================================

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)

----------------------------------------------------------------------

```

---

## Repository Structure

```text

.

├── 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

```

---

## 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`.

---

## License

Apache 2.0