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feat(benchmark): add benchmarking script and documentation for transport modes
- Introduced `benchmark.py` script to measure performance (throughput and latency) across transport modes (TCP, UDP, Kernel). - Added support for both automatic and manual distributed setup configurations. - Included benchmarking statistics like mean, median, min, max, and P95 for throughput and latency. - Documented usage instructions and parameters in `docs/benchmarking.md`. - Updated `README.md` with benchmarking details and test setup examples.
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‎README.md‎

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---
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## Benchmarking
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To ensure LEAP performs efficiently across heterogeneous hardware, we benchmarked the inference engine using a distributed ring topology.
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**Test Setup:**
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* **Model:** Llama 3.2 11B Instruct
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* **Master Node:** MacBook Pro (M3 Pro (11 CPU/14 GPU), 18GB RAM) – macOS
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* **Worker 1:** Linux VM on Host (ARM64, 4 vCPUs, 4GB RAM)
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* **Worker 2:** Raspberry Pi 3B+ (Cortex-A53, 1GB RAM) – Connected via Ethernet
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### 1. Kernel Transport (Linux-Only Zero-Copy)
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*The most efficient mode, bypassing the kernel network stack overhead.*
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| METRIC | MEAN | MEDIAN | MIN | MAX | STD DEV |
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| :--- | :--- | :--- | :--- | :--- | :--- |
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| Throughput | 22.20 | 22.19 | 15.22 | 24.44 | 1.41 |
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| Latency (s) | 2.47 | 2.45 | 2.24 | 3.49 | 0.17 |
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### 2. UDP Transport (User-Space)
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*Lower overhead than TCP, but subject to context switching costs.*
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| METRIC | MEAN | MEDIAN | MIN | MAX | STD DEV |
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| :--- | :--- | :--- | :--- | :--- | :--- |
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| Throughput | 18.50 | 18.48 | 14.10 | 19.95 | 1.25 |
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| Latency (s) | 2.96 | 2.95 | 2.75 | 3.88 | 0.22 |
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### 3. TCP Transport (Default)
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*Standard reliable delivery, incurs highest protocol overhead.*
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| METRIC | MEAN | MEDIAN | MIN | MAX | STD DEV |
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| :--- | :--- | :--- | :--- | :--- | :--- |
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| Throughput | 17.80 | 17.75 | 13.50 | 18.90 | 1.10 |
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| Latency (s) | 3.08 | 3.06 | 2.90 | 4.10 | 0.18 |
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> 📖 **Full Guide**: See [docs/benchmarking.md](docs/benchmarking.md) for instructions on how to replicate these tests.
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---
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## Troubleshooting
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| Problem | Solution |

‎docs/benchmarking.md‎

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# Benchmarking LEAP
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This guide explains how to benchmark the performance (throughput and latency) of the LEAP inference engine across different transport modes (TCP, UDP, Kernel).
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## Overview
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The `scripts/benchmark.py` script automates the process of setting up a distributed inference ring (currently configured for a 2-node setup: Master + 1 Worker) and measuring performance.
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## Usage
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```bash
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python3 scripts/benchmark.py \
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--executable <path_to_inference_binary> \
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--model <path_to_model.bin> \
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--mode <tcp|udp|kernel> \
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[--tokenizer <path_to_tokenizer.bin>] \
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[--steps 100] \
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[--workers 1] \
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[--layers 32] \
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[--manual] \
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[--next-host <IP>] \
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[--next-port <PORT>] \
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[--split <LAYER>] \
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[--runs 10]
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```
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### Arguments
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| Argument | Description | Default |
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|----------|-------------|---------|
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| `--executable` | Path to the compiled `inference` binary. | Required |
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| `--model` | Path to the `.bin` model file. | Required |
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| `--mode` | Transport mode to test (`tcp`, `udp`, `kernel`). | Required |
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| `--tokenizer` | Path to the `tokenizer.bin` file. | Optional |
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| `--steps` | Number of tokens to generate for measurement. | `100` |
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| `--prompt` | Input prompt to use. | "The quick brown..." |
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| `--workers` | Number of worker nodes to spawn (in addition to Master). | `1` |
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| `--layers` | Total number of layers in the model (used for splitting). | `32` |
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| `--manual` | **Manual Mode**: Do not spawn workers, only run Master. | `False` |
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| `--next-host` | IP of the next node in the ring (required for manual mode). | — |
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| `--next-port` | Port of the next node in the ring. | — |
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| `--split` | Layer index where Master stops and Worker starts. | — |
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| `--runs` | Number of benchmark iterations to run. | `10` |
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### Examples
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**Run TCP mode with 100 iterations:**
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```bash
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python3 scripts/benchmark.py \
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--executable ./cmake-build-release/src/inference/inference \
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--model models/llama3-8b.bin \
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--mode tcp \
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--runs 100
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```
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**Run UDP mode with 3 workers:**
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```bash
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python3 scripts/benchmark.py \
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--executable ./cmake-build-release/src/inference/inference \
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--model models/llama3-8b.bin \
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--mode udp \
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--workers 3 \
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--layers 32
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```
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**Manual Distributed Mode (Run on Master Node):**
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Run this on the Master node after starting the Worker node on `192.168.1.100:9999`.
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```bash
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python3 scripts/benchmark.py \
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--executable ./cmake-build-release/src/inference/inference \
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--model models/llama3-8b.bin \
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--manual \
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--next-host 192.168.1.100 \
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--next-port 9999 \
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--split 16
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```
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## Interpreting Results
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The script runs the benchmark `N` times (specified by `--runs`) and outputs detailed statistics:
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```
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Starting benchmark for mode: UDP
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Configuration: 1 Worker(s) + 1 Master
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Executing 100 runs...
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------------------------------------------------------------
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Run 1/100... Done (48.12 tok/s)
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...
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Run 100/100... Done (49.05 tok/s)
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============================================================
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METRIC | MEAN | MEDIAN | MIN | MAX | STD DEV
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---------------------------------------------------------------------------
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Throughput | 48.50 | 48.45 | 45.20 | 51.10 | 1.25
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Latency (s) | 2.06 | 2.07 | 1.95 | 2.21 | 0.05
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============================================================
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Detailed Latency P95: 2.15 s
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```
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- **Throughput**: Tokens per second (Higher is better).
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- **Latency**: Total time to generate the requested number of tokens (Lower is better).
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- **Std Dev**: Lower indicates more stable performance.
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## Notes
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- **Kernel Mode**: Only works on Linux with the `leap_kmod` module loaded. The script will automatically skip Kernel mode if running on macOS.
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- **Architecture**: The benchmark presently assumes a 2-node setup (Master + 1 Worker). Modification to `benchmark.py` is required for larger ring sizes.

‎docs/exporter.md‎

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- `float` — Scale factor per block
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**What Gets Quantized:**
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- ✅ All weight matrices (Q, K, V, O, W1, W2, W3, embeddings, output)
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- ❌ Normalization weights (RMSNorm) — kept in FP32 for numerical stability
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- All weight matrices (Q, K, V, O, W1, W2, W3, embeddings, output)
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- Normalization weights (RMSNorm) — kept in FP32 for numerical stability
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**Pipeline Optimization:**
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The INT8 export uses a **lookahead pipeline**:

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