EdgeFlow Compiler with Semantic Analysis

DSL for deploying AI models on edge devices.

EdgeFlow is a domain-specific language (DSL) and compiler for optimizing AI models for edge deployment. Users write simple .ef configuration files that describe optimization strategies (e.g., INT8 quantization), target devices, and goals like latency or size. The CLI parses these configs and runs the optimization pipeline.

NEW: Comprehensive Semantic Analysis System - The compiler now includes a sophisticated semantic analyzer that validates DSL models for shape compatibility, parameter ranges, resource constraints, and device compatibility before code generation.

Project Status: CLI with semantic analysis, tests, and CI. Parser and optimizer integration points ready.

Overview

• Language: EdgeFlow .ef configuration files
• Targets: TFLite models on edge devices (e.g., Raspberry Pi)
• Pipeline: parse config → optimize model → (future) benchmark → report

Example .ef

model_path = "path/to/model.tflite"
output_path = "path/to/optimized_model.tflite"
quantize = int8
target_device = "raspberry_pi"
optimize_for = latency

Installation

• Python 3.11 (CI target)
• Install runtime dependencies:

pip install -r requirements.txt

For development (linting, tests, coverage, hooks):

pip install -r requirements-dev.txt

Recommended: Install as editable package

pip install -e .

This allows you to use edgeflow command directly instead of python -m edgeflow.compiler.edgeflowc.

Quick Start

1. Clone and Install

# Clone repository
git clone https://github.com/pointblank-club/edgeFlow.git
cd edgeFlow

# Create and activate virtual environment (recommended)
python3 -m venv .venv
source .venv/bin/activate  # Linux/Mac
# On Windows: .venv\Scripts\activate

# Install package
pip install -e .

2. Verify Installation

edgeflow --version
edgeflow --help

Note: You may see a warning about missing 'dynamic_device_profiles'. This is expected and doesn't affect functionality.

3. Create Your Own Config

Create a file my_config.ef in the project root directory:

Note: You'll need an actual TensorFlow Lite model file (.tflite). You can also use .h5, .onnx, or .keras format models.

model = "path/to/your/model.tflite"
output = "optimized_model.tflite"
quantize = int8
target_device = "raspberry_pi"
optimize_for = latency

Then run:

edgeflow my_config.ef

Alternative: Without Installation

If you prefer not to install, set PYTHONPATH:

export PYTHONPATH=$(pwd)/src
python -m edgeflow.compiler.edgeflowc my_config.ef

Usage

Basic:

python -m edgeflow.compiler.edgeflowc path/to/config.ef

Verbose:

python -m edgeflow.compiler.edgeflowc path/to/config.ef --verbose

Help and Version:

python -m edgeflow.compiler.edgeflowc --help
python -m edgeflow.compiler.edgeflowc --version

Expected Behavior

• Missing file:

python -m edgeflow.compiler.edgeflowc non_existent.ef
# Error: File 'non_existent.ef' not found

• Wrong extension:

python -m edgeflow.compiler.edgeflowc invalid.txt
# Error: Invalid file extension. Expected '.ef' file

Semantic Analysis System

The EdgeFlow compiler now includes a comprehensive semantic analysis system that validates DSL models before code generation. This ensures that generated models are correct, efficient, and compatible with target devices.

Key Features

• Shape Compatibility Validation: Ensures tensor shapes match between connected layers
• Parameter Range Checking: Validates that all layer parameters are within acceptable ranges
• Device Compatibility: Checks if the model is compatible with target device constraints
• Resource Analysis: Validates memory usage and computational requirements
• Forbidden Configuration Detection: Identifies problematic layer sequences and configurations
• Graph Structure Validation: Detects cycles, connectivity issues, and missing components

Quick Start with Semantic Analysis

from semantic_analyzer import SemanticAnalyzer, IRGraph, semantic_check
from semantic_analyzer import get_edge_device_config

# Create or load your IR graph
graph = create_your_model_graph()

# Run semantic analysis
config = get_edge_device_config()  # For edge devices
errors = semantic_check(graph, config)

# Check results
if errors.has_errors():
    errors.print_summary()
else:
    print("✅ Model validation passed!")

Example Error Output

📊 Semantic Analysis Summary:
   Errors: 2
   Warnings: 1
   Info: 0
   Fatal: 0

📝 Detailed Report:
  [ERROR] at model.dsl:line 7: Expected input shape (1, 256), got (1, 28, 28, 3).
    Suggestion: Ensure the previous layer outputs shape (1, 256)
  [ERROR] at model.dsl:line 10: Dense layer requires Flatten layer after Conv2D
    Suggestion: Add a Flatten layer between the convolutional and dense layers
  [WARNING] at model.dsl:line 5: Kernel size 13 exceeds recommended maximum (11) for target device

Project Structure

edgeFlow/
├── .github/workflows/    # CI/CD GitHub Actions
├── scripts/              # Build and verification scripts
├── src/edgeflow/         # Core source code
│   ├── analysis/         # Static and semantic analysis
│   ├── backend/          # Backend API
│   ├── benchmarking/     # Performance benchmarking
│   ├── compiler/         # Core compiler logic
│   ├── config/           # Configuration handling
│   ├── deployment/       # Deployment orchestration
│   ├── frontend/         # Frontend application
│   ├── ir/               # Intermediate Representation
│   ├── optimization/     # Optimization strategies
│   ├── parser/           # Parser logic
│   ├── pipeline/         # Pipeline orchestration
│   ├── reporting/        # Error reporting
│   ├── semantic_analyzer/ # Semantic analysis system
│   └── utils/            # Utility functions
├── tests/                # Unit and integration tests
├── Dockerfile            # Docker container build
├── docker-compose.yml    # Docker Compose configuration
├── requirements.txt      # Runtime dependencies
├── requirements-dev.txt  # Dev/test dependencies
└── README.md             # This file

Integration Points

• Parser (parser.parse_ef(path)): edgeflowc.load_config tries to import and call this. If not found yet, it falls back to returning a minimal config with raw text.
• Optimizer (optimizer.optimize(config)): edgeflowc.optimize_model tries to import and call this. If not found yet, it logs a message and continues.

Development

Set up pre-commit hooks:

pre-commit install

Run linters and type checks:

black .
isort --profile black .
flake8 .
mypy --ignore-missing-imports .

Run tests with coverage:

pytest -q --cov=edgeflowc --cov-report=term-missing

Contributing

See CONTRIBUTING.md for detailed guidelines on how to contribute to EdgeFlow.

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

Security Notes

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.