diff --git a/.gitignore b/.gitignore index b9a0792..82c752a 100644 --- a/.gitignore +++ b/.gitignore @@ -41,3 +41,7 @@ sdist/ develop-eggs/ .installed.cfg +# ignore the models we test with +models/ +src/models/ + diff --git a/README.md b/README.md index 173546e..fc60a09 100644 --- a/README.md +++ b/README.md @@ -1,331 +1,257 @@ -# Kubz: Distributed zkML +# Dsperse: Distributed zkML -[![GitHub](https://img.shields.io/badge/GitHub-Repository-blue?style=flat-square&logo=github)](https://github.com/inferencelabs/kubz) -[![Discord](https://img.shields.io/badge/Discord-Join%20Community-7289DA?style=flat-square&logo=discord)](https://discord.gg/inferencelabs) -[![Telegram](https://img.shields.io/badge/Telegram-Join%20Channel-0088cc?style=flat-square&logo=telegram)](https://t.me/inferencelabs) -[![Twitter](https://img.shields.io/badge/Twitter-Follow%20Us-1DA1F2?style=flat-square&logo=twitter)](https://twitter.com/inferencelabs) -[![Website](https://img.shields.io/badge/Website-Visit%20Us-ff7139?style=flat-square&logo=firefox-browser)](https://inferencelabs.io) -[![Whitepaper](https://img.shields.io/badge/Whitepaper-Read-lightgrey?style=flat-square&logo=read-the-docs)](https://inferencelabs.io/whitepaper) +[![GitHub](https://img.shields.io/badge/GitHub-Repository-blue?style=flat-square&logo=github)](https://github.com/inference-labs-inc/dsperse) +[![Discord](https://img.shields.io/badge/Discord-Join%20Community-7289DA?style=flat-square&logo=discord)](https://discord.gg/7UDh8zqv) +[![Telegram](https://img.shields.io/badge/Telegram-Join%20Channel-0088cc?style=flat-square&logo=telegram)](https://t.me/inference_labs) +[![Twitter](https://img.shields.io/badge/Twitter-Follow%20Us-1DA1F2?style=flat-square&logo=twitter)](https://x.com/inference_labs) +[![Website](https://img.shields.io/badge/Website-Visit%20Us-ff7139?style=flat-square&logo=firefox-browser)](https://inferencelabs.com) +[![Whitepaper](https://img.shields.io/badge/Whitepaper-Read-lightgrey?style=flat-square&logo=read-the-docs)](http://arxiv.org/abs/2508.06972) -Kubz is a toolkit for slicing, analyzing, and running neural network models. It supports both PyTorch models and ONNX models, allowing you to break down complex models into smaller segments for detailed analysis, optimization, and verification. +Dsperse is a toolkit for slicing, analyzing, and running neural network models. It currently supports ONNX models, allowing you to break down complex models into smaller segments for detailed analysis, optimization, and verification. ## Features - **Model Slicing**: Split neural network models into individual layers or custom segments -- **ONNX Support**: Convert and slice ONNX models -- **PyTorch Support**: Work directly with PyTorch models +- **ONNX Support**: Slice and orchestrate ONNX models - **Layered Inference**: Run inference on sliced models, chaining the output of each segment - **Zero-Knowledge Proofs**: Generate proofs for model execution (via ezkl integration) - **Visualization**: Analyze model structure and performance -## Installation - -1. Clone the repository: - ```bash - git clone https://github.com/inference-labs-inc/kubz.git - cd kubz - ``` - -2. Create a virtual environment (optional but recommended): - ```bash - python -m venv venv - source venv/bin/activate # On Windows: venv\Scripts\activate - ``` - -3. Install dependencies: - ```bash - pip install -r requirements.txt - ``` - -4. Install the CLI: - ```bash - pip install -e . - ``` +## Documentation - This installs the `kubz` command-line tool, which you can use from anywhere on your system. +For more detailed information about the project, please refer to the following documentation: -## Usage Examples +- [Overview](docs/overview.md): A high-level overview of the project, its goals, and features +- [Architecture](docs/arc42.md): Detailed architecture documentation following the arc42 template -### Slicing a PyTorch Model - -```python -from src.model_slicer import ModelSlicer - -# Initialize the model slicer with the directory containing your model -model_slicer = ModelSlicer(model_directory="models/net") +## Installation -# Slice the model (creates slices in the model directory by default) -model_slicer.slice_model() +Preferred: one-step installer script -# You can also specify an output directory and slicing strategy -model_slicer.slice_model( - output_dir="custom_output_dir", - strategy="single_layer", # Slice by individual layers - input_file="path/to/input.json" # Sample input for analysis -) +- Unix/macOS: + - Make sure you have Python 3.9+ available (and optionally a virtualenv activated). + - From the repo root: +```bash +./install.sh ``` + - The script will: + - Install the Dsperse CLI in editable mode so the dsperse command is available + - Install EZKL (prompting for cargo or pip method if needed) + - Check EZKL SRS files (~/.ezkl/srs). It will offer to download them interactively (downloads can take a while) because having them locally speeds up circuitization/proving. -### Slicing an ONNX Model - -```python -from src.onnx_slicer import OnnxSlicer - -# Initialize the ONNX slicer with the path to your ONNX model -onnx_slicer = OnnxSlicer("models/yolov3/model.onnx") - -# Slice the model by individual layers -onnx_slicer.slice_model(mode="single_layer") +Non-interactive/CI-friendly: +```bash +./install.sh -n ``` -### Running Inference on a Sliced PyTorch Model - -```python -from src.runners.model_runner import ModelRunner +Manual install -# Initialize the model runner with the model directory -model_runner = ModelRunner(model_directory="models/net") +If you prefer to install manually or are on Windows: -# Run inference on the full model -result = model_runner.infer() -print(result) +1) Create and activate a virtual environment (optional but recommended) -# Run inference on the sliced model -result = model_runner.infer(mode="sliced") -print(result) +```bash +python -m venv venv +source venv/bin/activate # Windows: venv\Scripts\activate ``` -### Running Inference on a Sliced ONNX Model - -```python -from src.runners.onnx_runner import OnnxRunner - -# Initialize the ONNX runner with the model directory -onnx_runner = OnnxRunner(model_directory="models/yolov3") - -# Run inference on the full model -result = onnx_runner.infer() -print(result) +2) Install the Dsperse CLI -# Run inference on the sliced model -result = onnx_runner.infer(mode="sliced") -print(result) +```bash +pip install -e . ``` +This exposes the dsperse command. -### Zero-Knowledge Proofs for Models - -Kubz supports generating zero-knowledge proofs for neural network models using both ezkl and jstProve libraries. You can run proofs on either whole models or sliced models: - -#### Using ezkl - -```python -from src.runners.ezkl_runner import EzklRunner - -# Initialize the ezkl runner with the model directory -ezkl_runner = EzklRunner(model_directory="models/net") - -# Generate witness for the whole model -result = ezkl_runner.generate_witness() -print(result) - -# Generate witness for the sliced model -result = ezkl_runner.generate_witness(mode="sliced") -print(result) - -# Generate proof for the whole model -result = ezkl_runner.prove() -print(result) - -# Generate proof for the sliced model -result = ezkl_runner.prove(mode="sliced") -print(result) +3) Install EZKL CLI -# Verify proof for the whole model -result = ezkl_runner.verify() -print(result) - -# Verify proof for the sliced model -result = ezkl_runner.verify(mode="sliced") -print(result) +Recommended via cargo (requires Rust): +```bash +cargo install --locked ezkl +# Ensure $HOME/.cargo/bin is on your PATH +``` +Alternative via pip: +```bash +pip install -U ezkl +# Note: CLI availability may vary by platform with the pip package. Verify with: +ezkl --version ``` -#### Using jstProve - -```python -from src.runners.jstprove_runner import JSTProveRunner - -# Initialize the jstProve runner with the model directory -jstprove_runner = JSTProveRunner(model_directory="models/net") - -# Circuitize the whole model -result = jstprove_runner.circuitize() -print(result) - -# Circuitize the sliced model -result = jstprove_runner.circuitize(mode="sliced") -print(result) - -# Generate witness for the whole model -result = jstprove_runner.generate_witness() -print(result) - -# Generate witness for the sliced model -result = jstprove_runner.generate_witness(mode="sliced") -print(result) - -# Generate proof for the whole model -result = jstprove_runner.prove() -print(result) - -# Generate proof for the sliced model -result = jstprove_runner.prove(mode="sliced") -print(result) +4) (Optional but recommended) Download EZKL SRS files -# Verify proof for the whole model -result = jstprove_runner.verify() -print(result) +SRS files are stored at ~/.ezkl/srs (kzg.srs). They are needed for circuit setup/proving and downloading them ahead of time speeds things up. -# Verify proof for the sliced model -result = jstprove_runner.verify(mode="sliced") -print(result) +Example manual command to fetch one size: +```bash +ezkl get-srs --logrows 20 --commitment kzg ``` +Repeat for other logrows you need (commonly 2..21). -You can also use the CLI interface for various operations: - -### Command Line Interface (CLI) +Quickstart workflow -Kubz provides a powerful command-line interface for model slicing, inference, and zero-knowledge proof operations. +Below is an end-to-end walkthrough using the dsperse CLI. You can try it with the example model under src/models/net. -#### Basic Usage +1) Slice the model +- You can provide either the model.onnx file or the model directory containing it. +Common examples: ```bash -kubz [command] [options] +dsperse slice --model-dir src/models/net +dsperse slice --model-dir src/models/net/model.onnx ``` -Available commands: -- `slice`: Slice a model into segments -- `infer`: Run inference on a model -- `prove`: Generate a proof for a model -- `verify`: Verify a proof for a model - -#### Slicing Models - +Choose output directory (default: src/models/net/slices): ```bash -# Slice a PyTorch model using the default single_layer strategy -kubz slice --model-dir models/net - -# Slice a PyTorch model with a specific output directory and strategy -kubz slice --model-dir models/net --output-dir custom_slices --strategy by_type - -# Slice a model with a specific input file -kubz slice --model-dir models/net --input-file custom_input.json +dsperse slice --model-dir src/models/net --output-dir src/models/net/slices ``` -#### Running Inference - +Optionally save analysis metadata to a file (use --save-file; if flag is given without value, default path is model_dir/analysis/model_metadata.json): ```bash -# Run inference on a whole model (default) -kubz infer --model-dir models/net - -# Run inference on a sliced model -kubz infer --model-dir models/net --sliced +dsperse slice --model-dir src/models/net --save-file +dsperse slice --model-dir src/models/net --save-file src/models/net/analysis/model_metadata.json +``` -# Run inference with a specific input file and save results -kubz infer --model-dir models/net --input-file input.json --output-file results.json +What happens: +- Slices are written to src/models/net/slices/segment_/segment_.onnx +- A slices metadata.json is created at src/models/net/slices/metadata.json -# Run inference using the EZKL backend -kubz infer --model-dir models/net --ezkl +2) Circuitize with EZKL +- Circuitize either the whole model.onnx or the sliced segments (recommended for incremental proofs): -# Run inference using the jstProve backend -kubz infer --model-dir models/net --jstprove +Whole model: +```bash +dsperse circuitize --model-path src/models/net/model.onnx ``` -#### Generating Proofs - +Sliced model directory (auto-detects slices metadata): ```bash -# Generate a proof for a whole model using EZKL -kubz prove --model-dir models/net --ezkl - -# Generate a proof for a sliced model using EZKL -kubz prove --model-dir models/net --ezkl --sliced - -# Generate a proof using jstProve and save results -kubz prove --model-dir models/net --jstprove --output-file proof_results.json +dsperse circuitize --model-path src/models/net ``` -#### Verifying Proofs - +Optional calibration input to improve settings: ```bash -# Verify a proof for a whole model using EZKL -kubz verify --model-dir models/net --ezkl - -# Verify a proof for a sliced model using jstProve -kubz verify --model-dir models/net --jstprove --sliced - -# Verify a proof with a specific input file -kubz verify --model-dir models/net --jstprove --input-file input.json +dsperse circuitize --model-path src/models/net --input-file src/models/net/input.json ``` -#### Getting Help - +Optional layer selection (sliced models only): ```bash -# Show general help -kubz --help - -# Show help for a specific command -kubz slice --help -kubz infer --help -kubz prove --help -kubz verify --help +dsperse circuitize --model-path src/models/net --layers 2,3,4 +dsperse circuitize --model-path src/models/net --layers 0-2 ``` -#### Easter Eggs +What happens: +- For each selected segment, EZKL steps run: gen-settings, calibrate-settings, compile-circuit, setup +- Circuit artifacts are saved under each segment: src/models/net/slices/segment_/ezkl_circuitization/ + - segment_i_settings.json, segment_i_model.compiled, segment_i_vk.key, segment_i_pk.key +- Slices metadata is updated with ezkl_circuitization info per segment + +Note on missing slices: +- If you pass a model directory without slices metadata present, the CLI will prompt you to slice first. -The CLI includes some fun easter eggs! Try running: +3) Run inference +- Runs a chained execution over the slices using EZKL where available and falling back to ONNX per-segment on failure. +Common examples: ```bash -kubz --easter-egg +dsperse run --model-dir src/models/net +dsperse run --model-dir src/models/net/slices # you can point to slices directly ``` -Or just run any command - you might get lucky and see an easter egg 20% of the time! +You will be prompted for an input file if not provided (default: model_dir/input.json). -## Project Structure - -- `src/`: Main source code - - `model_slicer.py`: Slices PyTorch models - - `onnx_slicer.py`: Slices ONNX models - - `runners/`: Code for running inference on models - - `model_runner.py`: Runs inference on PyTorch models - - `onnx_runner.py`: Runs inference on ONNX models - - `utils/`: Utility functions - - `models/`: Example models -- `main.py`: CLI interface for running workflows -- `requirements.txt`: Project dependencies +To save the final output: +```bash +dsperse run --model-dir src/models/net --input-file src/models/net/input.json --output-file src/models/net/output.json +``` -## Advanced Usage +What happens: +- A run metadata file is auto-generated at src/models/net/run/metadata.json if missing +- A timestamped run directory is created: src/models/net/run/run_YYYYMMDD_HHMMSS/ +- Segment-by-segment inputs/outputs are saved under that run directory +- A run_result.json is written summarizing the chain execution -### Custom Slicing Strategies +4) Generate proofs +- Proves the segments that successfully produced EZKL witnesses in the selected run. -You can implement custom slicing strategies by modifying the `get_slice_points` method in `ModelUtils`. The default strategies include: +Typical usage: +```bash +dsperse prove --model-dir src/models/net +# You will be prompted to choose among existing runs under src/models/net/run/ +``` -- `single_layer`: Slice at every layer -- `by_type`: Group layers by type (e.g., all convolutional layers together) -- `by_size`: Group layers to achieve a target size per segment +Alternatively, specify a run directly: +```bash +dsperse prove --run-dir src/models/net/run/run_YYYYMMDD_HHMMSS +``` -### Working with Model Metadata +Optionally save the updated run results to a separate file: +```bash +dsperse prove --model-dir src/models/net --output-file src/models/net/proof_results.json +``` -After slicing a model, metadata is saved in a JSON file that contains information about each segment, including: +What happens: +- For each segment with a successful EZKL witness, the CLI calls ezkl prove +- Proof files are stored under the specific run’s segment folder +- The run_result.json is updated with proof_execution details -- Layer types -- Input/output shapes -- Dependencies between segments -- Paths to saved segment files +5) Verify proofs +- Verifies the proofs generated in step 4 against the stored verification keys and settings. -You can use this metadata to understand the model structure or to implement custom inference pipelines. +Typical usage: +```bash +dsperse verify --model-dir src/models/net +# You will be prompted to choose the run (same as in prove) +``` -## Contributing +Or specify a particular run: +```bash +dsperse verify --run-dir src/models/net/run/run_YYYYMMDD_HHMMSS +``` -Contributions are welcome! Please feel free to submit a Pull Request. +Optionally save verification results to a separate file: +```bash +dsperse verify --model-dir src/models/net --output-file src/models/net/verification_results.json +``` -## License +What happens: +- For each segment with a proof, the CLI calls ezkl verify +- The run_result.json is updated with verification_execution details +- A summary of verified segments is printed + +Tips and troubleshooting + +- EZKL not found: + - Ensure ezkl is on your PATH. If installed via cargo, add $HOME/.cargo/bin to PATH. +- SRS files missing/slow downloads: + - You can skip downloads during install and fetch later with ezkl get-srs --logrows --commitment kzg +- Circuitize says “slice first”: + - Run dsperse slice --model-dir to produce slices and metadata.json +- Paths in saved JSON are absolute on your machine; sharing outputs across machines may require path adjustments. + +Project structure (updated) + +- src/ + - slicer.py: orchestrator for slicing (uses OnnxSlicer) + - circuitizer.py: orchestrator for circuitization (uses EZKL backend pipeline) + - runner.py: chained execution across segments (EZKL or ONNX fallback) + - backends/ + - onnx_models.py: ONNX inference utilities + - ezkl.py: EZKL CLI bindings and circuitization pipeline + - cli/ + - base.py: shared CLI helpers + - slice.py: slice command + - circuitize.py: circuitize command + - run.py: run command + - prove.py: prove command + - verify.py: verify command + - analyzers/: metadata generation for runs/slices + - utils/: common helpers +- main.py: CLI entry point (dsperse) +- install.sh: installer for CLI, EZKL, and optional SRS + +Contributing + +Contributions are welcome! Please feel free to open issues and PRs. + +License See the LICENSE file for details. diff --git a/docs/arc42.md b/docs/arc42.md index 02e4d2d..80106fa 100644 --- a/docs/arc42.md +++ b/docs/arc42.md @@ -1,25 +1,28 @@ -# Kubz: Distributed zkML Proofs Architecture Document +# Dsperse: Distributed zkML Architecture Document -This document outlines the high-level architecture for the "kubz" project—a new approach for dynamic zkML proofs that splits the machine learning model into vertical layers for distributed proof computation. The goal is to reduce compute and RAM constraints, distribute proof computation across multiple machines, and ultimately optimize proof time on the Bittensor network. +This document outlines the high-level architecture for the "dsperse" project—a toolkit for slicing, analyzing, and running neural network models. It supports ONNX models, allowing users to break down complex models into smaller segments for detailed analysis, optimization, and verification. The project includes capabilities for distributed zkML proof computation, which splits machine learning models into manageable segments to reduce compute and RAM constraints. --- ## 1. Introduction and Goals - **Purpose:** - The kubz project introduces a distributed method for performing zkML proofs by "circuitizing" individual vertical layers of a neural network rather than the entire model. This enables more granular control over compute, RAM usage, and proof efficiency. + The dsperse project provides a toolkit for slicing, analyzing, and running neural network models. It introduces methods for model segmentation and distributed computation, including zkML proofs, enabling more granular control over compute, RAM usage, and overall efficiency. - **Scope:** - - Implement distributed zkML proofs for large neural nets. - - Leverage existing tools such as ezkl (which uses Halo 2 underneath) for circuit generation. - - Integrate with the Bittensor network to score and assign work to miners/validators. - - Expose CLI parameters for fine-tuning the number of layers per circuit and compute allocation. + - Support for ONNX models for slicing and analysis + - Implement distributed zkML proofs for large neural nets + - Provide comprehensive CLI interface for model operations + - Leverage existing tools such as ONNX and ezkl (which uses Halo 2 underneath) + - Expose parameters for fine-tuning model slicing and computation - **Goals and Objectives:** - - **Distribute Compute:** Split ML models into vertical layers to distribute inference proof computation across multiple machines. - - **Optimize Resource Usage:** Minimize RAM usage by breaking up large lookup tables and using a caching mechanism. - - **Increase Flexibility:** Provide knobs to control the number of layers per circuit, the amount of compute per miner, and the level of interaction for statistical soundness. - - **Improve Proof Time:** Drastically reduce overall proof time by balancing circuit size and distributed computation. + - **Model Slicing:** Split neural network models into individual layers or custom segments for detailed analysis + - **Distributed Compute:** Break down ML models into manageable pieces to distribute computation across multiple machines + - **Optimize Resource Usage:** Minimize RAM usage through model splitting and efficient inference pipelines + - **Increase Flexibility:** Support different model types and provide configurable slicing strategies + - **Enable Zero-Knowledge Proofs:** Generate and verify proofs for model execution via ezkl integration + - **Improve Performance:** Enhance overall processing time by balancing segment size and distributed computation --- @@ -43,13 +46,13 @@ This document outlines the high-level architecture for the "kubz" project—a ne ### 3.1. System Scope -- **Kubz** is designed to enable distributed zkML proof computation by splitting a neural network’s weight matrix into vertical layers. +- **Dsperse** is designed to enable distributed zkML proof computation by splitting a neural network’s weight matrix into vertical layers. - It is the first implementation aiming for distributed zkML proof generation, targeting use cases where large neural networks require distribution of workload. ### 3.2. External Interfaces and Context Diagram - **External Systems:** - - **ezkl/Halo 2:** Used for generating circuits and proofs. Kubz will adapt these tools to support vertical layer circuitization. + - **ezkl/Halo 2:** Used for generating circuits and proofs. Dsperse will adapt these tools to support vertical layer circuitization. - **Bittensor Network:** Provides the infrastructure for miners and validators; handles proof submission, scoring, and incentive distribution. --- @@ -102,7 +105,7 @@ This document outlines the high-level architecture for the "kubz" project—a ne Allows miners to set their preferred circuit size and compute allocation. - ![Vertical Layers Circuitization](images/vertical_layers.png) + ![Vertical Layers Circuitization](images/vertical_layers.jpg) *Note: In the diagram above, each colored node represents a vertical layer of the neural network, circuitized independently for distributed proof computation.* --- @@ -144,7 +147,7 @@ This document outlines the high-level architecture for the "kubz" project—a ne Distribute proofs across multiple nodes to balance load and reduce proof time. - **Resource Optimization:** Use caching and configurable circuit sizes to optimize RAM and compute usage. - + - **Quality Aspects:** - **Security and Soundness:** Adjust statistical conversational time and zk iteration counts as knobs to maintain overall proof security. @@ -160,11 +163,11 @@ This document outlines the high-level architecture for the "kubz" project—a ne _Decision:_ Split neural net circuits vertically instead of as a whole model. _Rationale:_ Reduces compute and RAM constraints, allows distributed proof computation, and provides finer control over resource allocation. _Alternatives:_ Circuitizing the entire model; however, this is less flexible and resource-intensive. - + - **Dynamic Grouping of Layers:** _Decision:_ Expose CLI parameters to allow grouping of multiple vertical layers into one circuit. _Rationale:_ Enables adaptation based on miner capacity, optimizing proof time and resource usage. - + - **Caching with Redis:** _Decision:_ Use Redis for table lookup caching. _Rationale:_ Minimizes RAM usage while providing fast lookup performance. @@ -190,7 +193,7 @@ This document outlines the high-level architecture for the "kubz" project—a ne Potential failures in assembling partial proofs due to communication or computation delays. - **Cache Performance:** Redis cache may not scale as expected, impacting lookup times. - + - **Mitigation Strategies:** - Implement fallback mechanisms to assign smaller circuits. - Penalty mechanisms for miners that consistently fail or are slow. diff --git a/docs/overview.md b/docs/overview.md index 4849edf..fe88089 100644 --- a/docs/overview.md +++ b/docs/overview.md @@ -1,36 +1,55 @@ +# Dsperse: Distributed zkML + +## Overview + +Dsperse is a toolkit for slicing, analyzing, and running neural network models. It supports ONNX models, allowing you to break down complex models into smaller segments for detailed analysis, optimization, and verification. + ### Core Purpose -The project aims to solve a significant challenge in zkML (zero-knowledge machine learning) by introducing a distributed approach to proof computation. +The project aims to solve a significant challenge in zkML (zero-knowledge machine learning) by introducing a distributed approach to proof computation and providing tools for model slicing and analysis. + ### Key Technical Innovation -The main innovation is the concept of "vertical layer circuitization" where: -1. Instead of generating proofs for an entire neural network at once -2. The system splits the neural network into vertical layers -3. Each layer (or group of layers) can be processed independently +The main innovation is the concept of "model slicing" where: +1. Instead of processing an entire neural network at once +2. The system splits the neural network into manageable segments +3. Each segment can be processed independently for analysis, inference, or proof generation ### Primary Goals -1. **Distributed Computation** +1. **Model Slicing** + - Split neural network models into individual layers or custom segments + - Support ONNX models + - Enable detailed analysis of model components + +2. **Distributed Computation** - Break down large ML models into manageable pieces - Enable parallel processing across multiple machines - Support both GPU and non-GPU nodes -2. **Resource Optimization** - - Reduce RAM requirements through layer splitting - - Implement caching mechanisms +3. **Resource Optimization** + - Reduce RAM requirements through model splitting + - Implement efficient inference pipelines - Better manage compute resources -3. **System Flexibility** - - Configurable number of layers per circuit - - Adjustable compute allocation - - Adaptable to different miner capabilities +4. **System Flexibility** + - Support for different model types + - Configurable slicing strategies + - Adaptable to different hardware capabilities -4. **Performance Enhancement** - - Reduce overall proof generation time - - Balance circuit size with distributed computation - - Optimize for the Bittensor network environment +5. **Zero-Knowledge Proofs** + - Generate proofs for model execution via ezkl integration + - Support for both whole model and sliced model proofs + - Optimize proof generation for distributed environments ### Implementation Framework - Built on top of existing tools: - - ezkl for circuit generation + - ONNX for model representation and interoperability + - ezkl for zero-knowledge proof generation - Halo 2 as the underlying proving system -- Designed to work within Bittensor network constraints -- Focuses on weight and bias matrices, making it applicable to various neural network architectures +- Comprehensive CLI interface for: + - Model slicing + - Inference + - Proof generation + - Proof verification + +- Designed to work with various neural network architectures +- Focuses on practical applications of zkML technology diff --git a/install.sh b/install.sh new file mode 100755 index 0000000..4acd5fa --- /dev/null +++ b/install.sh @@ -0,0 +1,285 @@ +#!/usr/bin/env bash +# install.sh - Installer for Dsperse CLI and EZKL (with lookup tables) +# This script installs: +# - Dsperse CLI (python package, console script: dsperse) +# - EZKL CLI (if missing) +# - EZKL lookup tables (if missing) +# It detects existing installations and will skip or prompt accordingly. + +set -euo pipefail + +INTERACTIVE=true +FORCE_REINSTALL=false +PYTHON_BIN="python3" +PIP_BIN="" + +print_usage() { + cat <&2 + print_usage + exit 1 + ;; + esac +done + +say() { echo -e "$1"; } +info() { say "[INFO] $1"; } +warn() { say "[WARN] $1"; } +err() { say "[ERROR] $1"; } + +confirm() { + local prompt="$1" + if [[ "$INTERACTIVE" == true ]]; then + read -r -p "$prompt [y/N]: " resp || true + [[ "$resp" =~ ^[Yy]$ ]] + else + return 0 + fi +} + +# Resolve pip for the chosen python +resolve_pip() { + if command -v "$PYTHON_BIN" >/dev/null 2>&1; then + PIP_BIN="$PYTHON_BIN -m pip" + elif command -v python >/dev/null 2>&1; then + PYTHON_BIN="python" + PIP_BIN="python -m pip" + else + err "Python not found. Please install Python 3 and re-run." + exit 1 + fi +} + +# Install Dsperse CLI via pip +install_dsperse_cli() { + info "Installing Dsperse CLI (pip editable install) ..." + if [[ "$FORCE_REINSTALL" == true ]]; then + eval $PIP_BIN install -e . + else + # Try to detect if dsperse is already available + if command -v dsperse >/dev/null 2>&1; then + info "Dsperse CLI already installed: $(command -v dsperse)" + if [[ "$INTERACTIVE" == true ]] && confirm "Reinstall/upgrade Dsperse CLI?"; then + eval $PIP_BIN install -e . + else + info "Skipping Dsperse CLI install." + fi + else + eval $PIP_BIN install -e . + fi + fi + if command -v dsperse >/dev/null 2>&1; then + info "✓ Dsperse CLI installed: $(command -v dsperse)" + else + warn "Dsperse CLI not found on PATH yet. It may appear after you restart your shell or activate your venv." + fi +} + +# Try installing ezkl via cargo (preferred if available) +install_ezkl_via_cargo() { + if ! command -v cargo >/dev/null 2>&1; then + return 1 + fi + info "Installing EZKL via cargo (this may take several minutes) ..." + if cargo install --list | grep -q "^ezkl "; then + info "EZKL already installed via cargo. Updating to latest compatible version ..." + fi + if cargo install --locked ezkl; then + # Ensure cargo bin in PATH for current session + export PATH="$HOME/.cargo/bin:$PATH" + return 0 + fi + return 1 +} + +# Try installing ezkl via pip (provides python package; CLI availability may vary) +install_ezkl_via_pip() { + info "Attempting to install ezkl via pip ..." + if eval $PIP_BIN install -U ezkl; then + return 0 + fi + return 1 +} + +# Attempt to download lookup tables with available subcommands +install_lookup_tables() { + if ! command -v ezkl >/dev/null 2>&1; then + warn "Skipping EZKL lookup tables: ezkl not found." + return 0 + fi + info "Installing EZKL lookup tables (if required) ..." + set +e + if ezkl --help 2>/dev/null | grep -qi "lookup"; then + # Try common subcommands (names may vary by version) + ezkl get-lookup-tables 2>/dev/null && { set -e; info "✓ Lookup tables installed (get-lookup-tables)."; return 0; } + ezkl download-lookup-tables 2>/dev/null && { set -e; info "✓ Lookup tables installed (download-lookup-tables)."; return 0; } + ezkl lookup --download 2>/dev/null && { set -e; info "✓ Lookup tables installed (lookup --download)."; return 0; } + fi + set -e + warn "Could not detect a lookup tables subcommand on this EZKL version. If witness generation asks for lookup tables, please consult EZKL docs to download them." +} + +# Ensure EZKL installed +ensure_ezkl() { + if command -v ezkl >/dev/null 2>&1; then + info "EZKL already installed: $(command -v ezkl)" + ezkl --version || true + return 0 + fi + + if [[ "$INTERACTIVE" == true ]]; then + say "" + say "EZKL CLI is not installed. Choose an installation method:" + say " 1) cargo install (recommended if Rust is installed)" + say " 2) pip install ezkl (Python package; CLI availability may vary)" + say " 3) Skip (I'll install manually)" + read -r -p "Select [1/2/3]: " choice || true + case "$choice" in + 1) + install_ezkl_via_cargo || warn "cargo install failed." + ;; + 2) + install_ezkl_via_pip || warn "pip install ezkl failed." + ;; + *) + warn "Skipping EZKL installation as per user choice." + ;; + esac + else + # Non-interactive: best-effort cargo then pip + install_ezkl_via_cargo || install_ezkl_via_pip || true + fi + + if command -v ezkl >/dev/null 2>&1; then + info "✓ EZKL installed: $(command -v ezkl)" + ezkl --version || true + else + warn "EZKL CLI not found after installation attempts. You may need to add its install location to PATH or install manually." + warn "Common options:" + warn " - Cargo: cargo install --locked ezkl (ensure \"$HOME/.cargo/bin\" on PATH)" + warn " - Prebuilt binaries: see EZKL GitHub releases" + fi +} + +# Ensure SRS files (kzg commitment) exist under ~/.ezkl/srs +ensure_srs() { + if ! command -v ezkl >/dev/null 2>&1; then + warn "Skipping SRS setup: ezkl not found." + return 0 + fi + local SRS_DIR="$HOME/.ezkl/srs" + local MIN_LOGROWS=${MIN_LOGROWS:-2} + local MAX_LOGROWS=${MAX_LOGROWS:-24} + mkdir -p "$SRS_DIR" + + info "Checking EZKL SRS files in $SRS_DIR (kzg, logrows ${MIN_LOGROWS}-${MAX_LOGROWS}) ..." + local missing=() + local present=() + local n + for (( n=$MIN_LOGROWS; n<=$MAX_LOGROWS; n++ )); do + if [[ -f "$SRS_DIR/kzg${n}.srs" ]]; then + present+=("kzg${n}.srs") + else + missing+=("$n") + fi + done + + if (( ${#missing[@]} == 0 )); then + info "✓ All required SRS files already present (${#present[@]} files)." + return 0 + fi + + say "SRS files missing for logrows: ${missing[*]}" + if [[ "$INTERACTIVE" == true ]]; then + say "These downloads can take a while (several minutes per file), but having them locally will speed up EZKL circuitization and proof steps." + if ! confirm "Download missing SRS files now using 'ezkl get-srs --commitment kzg'?"; then + warn "Skipping SRS download at user request. You can run it later manually, e.g.: ezkl get-srs --logrows 20 --commitment kzg" + return 0 + fi + else + warn "Non-interactive mode: skipping automatic SRS downloads to avoid long install times." + warn "You can download later with: ezkl get-srs --logrows --commitment kzg (files are stored under $HOME/.ezkl/srs)." + return 0 + fi + + local ok_count=0 + local fail_count=0 + for n in "${missing[@]}"; do + info "Downloading SRS for logrows=$n (kzg) ..." + if ezkl get-srs --logrows "$n" --commitment kzg >/dev/null 2>&1; then + info "✓ Downloaded $SRS_DIR/kzg${n}.srs" + ok_count=$((ok_count+1)) + else + warn "Failed to download SRS for logrows=$n. You can retry: ezkl get-srs --logrows $n --commitment kzg" + fail_count=$((fail_count+1)) + fi + done + info "SRS download summary: success=$ok_count, failed=$fail_count, total_missing=${#missing[@]}" +} + +main() { + info "Installing dependencies for Dsperse ..." + resolve_pip + + # Display python and pip info + eval $PIP_BIN --version || true + + # Install Dsperse CLI + install_dsperse_cli + + # Ensure EZKL + ensure_ezkl + + # Ensure SRS data (kzg) exists + ensure_srs + + # Lookup tables (for some ezkl versions) + install_lookup_tables + + say "" + if command -v dsperse >/dev/null 2>&1; then + info "You can now run the Dsperse CLI: dsperse --help" + else + warn "dsperse command not yet available on PATH. Try restarting your shell or activating your virtualenv." + fi + + if command -v ezkl >/dev/null 2>&1; then + info "EZKL is ready: ezkl --help" + else + warn "EZKL not detected; some features will fall back to ONNX or fail." + fi + + say "\nInstallation complete!" +} + +main "$@" diff --git a/main.py b/main.py index b9d2b07..dd43c59 100755 --- a/main.py +++ b/main.py @@ -1,360 +1,64 @@ #!/usr/bin/env python3 """ -Kubz CLI - A command-line interface for the Kubz neural network model slicing and analysis toolkit. +Dsperse CLI - A command-line interface for the Dsperse neural network model slicing and analysis toolkit. -This CLI allows you to slice models and run verified inference (both whole and sliced) using -different backends (ezkl, jstprove, plain). +This CLI allows you to slice models and run verified inference on sliced models, with the runner +automatically determining the appropriate backend (EZKL or ONNX) for each slice. """ -import argparse -import os -import random import sys -import time -from typing import Optional, List, Dict, Any - -import colorama +import random +import logging from colorama import Fore, Style -# Custom ArgumentParser that shows header and easter egg with help -class KubzArgumentParser(argparse.ArgumentParser): - def print_help(self, file=None): - print_header() - if random.random() < 0.2: # 20% chance to show an easter egg - print_easter_egg() - super().print_help(file) - - def add_subparsers(self, **kwargs): - # Ensure that subparsers are also KubzArgumentParser instances - kwargs.setdefault('parser_class', KubzArgumentParser) - return super().add_subparsers(**kwargs) - -# Import Kubz modules -from src.model_slicer import ModelSlicer -from src.onnx_slicer import OnnxSlicer -from src.runners.model_runner import ModelRunner -from src.runners.onnx_runner import OnnxRunner -from src.runners.ezkl_runner import EzklRunner -from src.runners.jstprove_runner import JSTProveRunner - -# Initialize colorama -colorama.init() - -# Easter eggs -EASTER_EGGS = [ - "Did you know? Neural networks are just spicy linear algebra!", - "Fun fact: The first neural network was created in 1943 by Warren McCulloch and Walter Pitts.", - "Pro tip: Always normalize your inputs!", - "Kubz fact: Slicing models helps with interpretability and verification.", - "ZK fact: Zero-knowledge proofs allow you to prove you know something without revealing what it is.", - "Kubz was named after the idea of 'cubes' of computation in neural networks.", - "The answer to life, the universe, and everything is... 42 (but you need a neural network to understand why).", - "Neural networks don't actually think. They just do math really fast.", - "If you're reading this, you're awesome! Keep up the great work!", - "Kubz: Making neural networks more transparent, one slice at a time." -] - -def print_header(): - """Print the Kubz CLI header with ASCII art.""" - header = f""" -{Fore.CYAN} - ██ ▄█▀ █ ██ ▄▄▄▄ ▒███████▒ - ██▄█▒ ██ ▓██▒▓█████▄ ▒ ▒ ▒ ▄▀░ -▓███▄░ ▓██ ▒██░▒██▒ ▄██░ ▒ ▄▀▒░ -▓██ █▄ ▓▓█ ░██░▒██░█▀ ░ ▄▀▒ ░ -▒██▒ █▄▒▒█████▓ ░▓█ ▀█▓▒███████▒ -▒ ▒▒ ▓▒░▒▓▒ ▒ ▒ ░▒▓███▀▒░▒▒ ▓░▒░▒ -░ ░▒ ▒░░░▒░ ░ ░ ▒░▒ ░ ░░▒ ▒ ░ ▒ -░ ░░ ░ ░░░ ░ ░ ░ ░ ░ ░ ░ ░ ░ -░ ░ ░ ░ ░ ░ ░ - ░ -{Style.RESET_ALL} -{Fore.YELLOW}Distributed zkML Toolkit{Style.RESET_ALL} -""" - print(header) - -def print_easter_egg(): - """Print a random easter egg.""" - print(f"\n{Fore.GREEN}🥚 {random.choice(EASTER_EGGS)}{Style.RESET_ALL}\n") - -def slice_model(args): - """Slice a model based on the provided arguments.""" - print(f"{Fore.CYAN}Slicing model...{Style.RESET_ALL}") - - if not os.path.exists(args.model_dir): - print(f"{Fore.RED}Error: Model directory '{args.model_dir}' does not exist.{Style.RESET_ALL}") - return - - # Determine if it's a PyTorch or ONNX model based on model_type or auto-detect - is_onnx = False - if args.model_type: - is_onnx = args.model_type.lower() == 'onnx' - else: - # Auto-detect model type - if os.path.exists(os.path.join(args.model_dir, "model.onnx")): - is_onnx = True - print(f"{Fore.YELLOW}Auto-detected ONNX model.{Style.RESET_ALL}") - elif os.path.exists(os.path.join(args.model_dir, "model.pth")): - print(f"{Fore.YELLOW}Auto-detected PyTorch model.{Style.RESET_ALL}") - else: - print(f"{Fore.RED}Error: No model.pth or model.onnx found in '{args.model_dir}'.{Style.RESET_ALL}") - return - - # Create output directory if specified - output_dir = args.output_dir - if output_dir: - os.makedirs(output_dir, exist_ok=True) - - try: - if is_onnx: - # Slice ONNX model - onnx_path = os.path.join(args.model_dir, "model.onnx") - if not os.path.exists(onnx_path): - print(f"{Fore.RED}Error: ONNX model file not found at '{onnx_path}'.{Style.RESET_ALL}") - return - slicer = OnnxSlicer(onnx_path) - slicer.slice_model(mode="single_layer") - print(f"{Fore.GREEN}✓ ONNX model sliced successfully!{Style.RESET_ALL}") - else: - # Slice PyTorch model - pth_path = os.path.join(args.model_dir, "model.pth") - if not os.path.exists(pth_path): - print(f"{Fore.RED}Error: PyTorch model file not found at '{pth_path}'.{Style.RESET_ALL}") - return - slicer = ModelSlicer(model_directory=args.model_dir) - slicer.slice_model( - output_dir=output_dir, - strategy="single_layer", # Default to single_layer strategy - input_file=args.input_file - ) - print(f"{Fore.GREEN}✓ PyTorch model sliced successfully!{Style.RESET_ALL}") - except Exception as e: - print(f"{Fore.RED}Error slicing model: {e}{Style.RESET_ALL}") - import traceback - traceback.print_exc() - -def run_inference(args): - """Run inference on a model based on the provided arguments.""" - print(f"{Fore.CYAN}Running inference...{Style.RESET_ALL}") - - if not os.path.exists(args.model_dir): - print(f"{Fore.RED}Error: Model directory '{args.model_dir}' does not exist.{Style.RESET_ALL}") - return - - # Determine if it's a PyTorch or ONNX model - is_onnx = False - if os.path.exists(os.path.join(args.model_dir, "model.onnx")): - is_onnx = True - elif not os.path.exists(os.path.join(args.model_dir, "model.pth")): - print(f"{Fore.RED}Error: No model.pth or model.onnx found in '{args.model_dir}'.{Style.RESET_ALL}") - return - - # Determine the mode (sliced or whole) - mode = "sliced" if args.sliced else None - - try: - # Run inference with the appropriate backend - if args.ezkl: - # Run inference with ezkl - runner = EzklRunner(model_directory=args.model_dir) - start_time = time.time() - result = runner.generate_witness(mode=mode, input_file=args.input_file) - elapsed_time = time.time() - start_time - print(f"{Fore.GREEN}✓ EZKL inference completed in {elapsed_time:.2f} seconds!{Style.RESET_ALL}") - elif args.jstprove: - # Run inference with jstProve - runner = JSTProveRunner(model_directory=args.model_dir) - start_time = time.time() - result = runner.generate_witness(mode=mode, input_file=args.input_file) - elapsed_time = time.time() - start_time - print(f"{Fore.GREEN}✓ jstProve inference completed in {elapsed_time:.2f} seconds!{Style.RESET_ALL}") - else: - # Run plain inference - if is_onnx: - runner = OnnxRunner(model_directory=args.model_dir) - else: - runner = ModelRunner(model_directory=args.model_dir) - - start_time = time.time() - result = runner.infer(mode=mode, input_path=args.input_file) - elapsed_time = time.time() - start_time - print(f"{Fore.GREEN}✓ Inference completed in {elapsed_time:.2f} seconds!{Style.RESET_ALL}") - - # Save the result if output file is specified - if args.output_file: - import json - with open(args.output_file, 'w') as f: - json.dump(result, f, indent=2) - print(f"{Fore.GREEN}✓ Results saved to {args.output_file}{Style.RESET_ALL}") - - # Print the result - print(f"\n{Fore.YELLOW}Results:{Style.RESET_ALL}") - print(result) - - except Exception as e: - print(f"{Fore.RED}Error during inference: {e}{Style.RESET_ALL}") - import traceback - traceback.print_exc() - -def run_proof(args): - """Generate a proof for a model based on the provided arguments.""" - print(f"{Fore.CYAN}Generating proof...{Style.RESET_ALL}") - - if not os.path.exists(args.model_dir): - print(f"{Fore.RED}Error: Model directory '{args.model_dir}' does not exist.{Style.RESET_ALL}") - return - - # Determine the mode (sliced or whole) - mode = "sliced" if args.sliced else None - - try: - # Generate proof with the appropriate backend - if args.ezkl: - # Generate proof with ezkl - runner = EzklRunner(model_directory=args.model_dir) - start_time = time.time() - result = runner.prove(mode=mode) - elapsed_time = time.time() - start_time - print(f"{Fore.GREEN}✓ EZKL proof generated in {elapsed_time:.2f} seconds!{Style.RESET_ALL}") - elif args.jstprove: - # Generate proof with jstProve - runner = JSTProveRunner(model_directory=args.model_dir) - start_time = time.time() - result = runner.prove(mode=mode) - elapsed_time = time.time() - start_time - print(f"{Fore.GREEN}✓ jstProve proof generated in {elapsed_time:.2f} seconds!{Style.RESET_ALL}") - else: - print(f"{Fore.RED}Error: Please specify a proof backend (--ezkl or --jstprove).{Style.RESET_ALL}") - return - - # Save the result if output file is specified - if args.output_file: - import json - with open(args.output_file, 'w') as f: - json.dump(result, f, indent=2) - print(f"{Fore.GREEN}✓ Results saved to {args.output_file}{Style.RESET_ALL}") - - # Print the result - print(f"\n{Fore.YELLOW}Results:{Style.RESET_ALL}") - print(result) - - except Exception as e: - print(f"{Fore.RED}Error generating proof: {e}{Style.RESET_ALL}") - import traceback - traceback.print_exc() - -def verify_proof(args): - """Verify a proof for a model based on the provided arguments.""" - print(f"{Fore.CYAN}Verifying proof...{Style.RESET_ALL}") - - if not os.path.exists(args.model_dir): - print(f"{Fore.RED}Error: Model directory '{args.model_dir}' does not exist.{Style.RESET_ALL}") - return - - # Determine the mode (sliced or whole) - mode = "sliced" if args.sliced else None - - try: - # Verify proof with the appropriate backend - if args.ezkl: - # Verify proof with ezkl - runner = EzklRunner(model_directory=args.model_dir) - start_time = time.time() - result = runner.verify(mode=mode) - elapsed_time = time.time() - start_time - print(f"{Fore.GREEN}✓ EZKL proof verified in {elapsed_time:.2f} seconds!{Style.RESET_ALL}") - elif args.jstprove: - # Verify proof with jstProve - runner = JSTProveRunner(model_directory=args.model_dir) - start_time = time.time() - result = runner.verify(mode=mode, input_file=args.input_file) - elapsed_time = time.time() - start_time - print(f"{Fore.GREEN}✓ jstProve proof verified in {elapsed_time:.2f} seconds!{Style.RESET_ALL}") - else: - print(f"{Fore.RED}Error: Please specify a proof backend (--ezkl or --jstprove).{Style.RESET_ALL}") - return - - # Save the result if output file is specified - if args.output_file: - import json - with open(args.output_file, 'w') as f: - json.dump(result, f, indent=2) - print(f"{Fore.GREEN}✓ Results saved to {args.output_file}{Style.RESET_ALL}") - - # Print the result - print(f"\n{Fore.YELLOW}Results:{Style.RESET_ALL}") - print(result) - - except Exception as e: - print(f"{Fore.RED}Error verifying proof: {e}{Style.RESET_ALL}") - import traceback - traceback.print_exc() +# Import CLI modules +from src.cli import ( + DsperseArgumentParser, print_header, print_easter_egg, configure_logging, logger, + setup_slice_parser, slice_model, + setup_run_parser, run_inference, + setup_prove_parser, run_proof, + setup_verify_parser, verify_proof, + setup_circuitize_parser, circuitize_model +) def main(): - """Main entry point for the Kubz CLI.""" + """Main entry point for the Dsperse CLI.""" # Create the main parser - parser = KubzArgumentParser( - description="Kubz - Distributed zkML Toolkit", - formatter_class=argparse.RawDescriptionHelpFormatter, + parser = DsperseArgumentParser( + description="Dsperse - Distributed zkML Toolkit", + formatter_class=sys.modules['argparse'].RawDescriptionHelpFormatter, epilog=f"Made with {Fore.RED}❤️{Style.RESET_ALL} by the Inference Labs team" ) # Add version argument - parser.add_argument('--version', action='version', version='Kubz CLI v1.0.0') + parser.add_argument('--version', action='version', version='Dsperse CLI v1.0.0') + + # Add logging level argument + parser.add_argument('--log-level', + choices=['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'], + default='WARNING', + help='Set the logging level (default: WARNING)') # Add easter egg argument - parser.add_argument('--easter-egg', action='store_true', help=argparse.SUPPRESS) + parser.add_argument('--easter-egg', action='store_true', help=sys.modules['argparse'].SUPPRESS) # Create subparsers for different commands subparsers = parser.add_subparsers(dest='command', help='Command to execute') - # Slice command - slice_parser = subparsers.add_parser('slice', help='Slice a model into segments') - slice_parser.add_argument('--model-dir', required=True, help='Directory containing the model') - slice_parser.add_argument('--output-dir', help='Directory to save the sliced model (default: model_dir/slices)') - slice_parser.add_argument('--model-type', choices=['onnx', 'pth'], - help='Type of model to slice (auto-detected if not specified)') - slice_parser.add_argument('--input-file', help='Path to input file for analysis (default: model_dir/input.json)') - - # Infer command - infer_parser = subparsers.add_parser('infer', help='Run inference on a model') - infer_parser.add_argument('--model-dir', required=True, help='Directory containing the model') - infer_parser.add_argument('--input-file', help='Path to input file (default: model_dir/input.json)') - infer_parser.add_argument('--output-file', help='Path to save output results') - infer_parser.add_argument('--sliced', action='store_true', help='Run inference on sliced model') - - # Add backend group for inference - infer_backend_group = infer_parser.add_mutually_exclusive_group() - infer_backend_group.add_argument('--ezkl', action='store_true', help='Use EZKL backend for inference') - infer_backend_group.add_argument('--jstprove', action='store_true', help='Use jstProve backend for inference') - infer_backend_group.add_argument('--plain', action='store_true', help='Use plain inference (default)') - - # Prove command - prove_parser = subparsers.add_parser('prove', help='Generate a proof for a model') - prove_parser.add_argument('--model-dir', required=True, help='Directory containing the model') - prove_parser.add_argument('--output-file', help='Path to save output results') - prove_parser.add_argument('--sliced', action='store_true', help='Generate proof for sliced model') - - # Add backend group for proving - prove_backend_group = prove_parser.add_mutually_exclusive_group(required=True) - prove_backend_group.add_argument('--ezkl', action='store_true', help='Use EZKL backend for proving') - prove_backend_group.add_argument('--jstprove', action='store_true', help='Use jstProve backend for proving') - - # Verify command - verify_parser = subparsers.add_parser('verify', help='Verify a proof for a model') - verify_parser.add_argument('--model-dir', required=True, help='Directory containing the model') - verify_parser.add_argument('--input-file', help='Path to input file (for jstProve)') - verify_parser.add_argument('--output-file', help='Path to save output results') - verify_parser.add_argument('--sliced', action='store_true', help='Verify proof for sliced model') - - # Add backend group for verifying - verify_backend_group = verify_parser.add_mutually_exclusive_group(required=True) - verify_backend_group.add_argument('--ezkl', action='store_true', help='Use EZKL backend for verification') - verify_backend_group.add_argument('--jstprove', action='store_true', help='Use jstProve backend for verification') + # Set up parsers for each command + setup_slice_parser(subparsers) + setup_run_parser(subparsers) + setup_prove_parser(subparsers) + setup_verify_parser(subparsers) + setup_circuitize_parser(subparsers) # Parse arguments args = parser.parse_args() + # Configure logging + configure_logging(args.log_level) + logger.debug(f"Logging configured with level: {args.log_level}") + # Print header print_header() @@ -366,15 +70,14 @@ def main(): # Handle commands if args.command == 'slice': slice_model(args) - elif args.command == 'infer': - # Set plain as default if no backend specified - if not (args.ezkl or args.jstprove): - args.plain = True + elif args.command == 'run': run_inference(args) elif args.command == 'prove': run_proof(args) elif args.command == 'verify': verify_proof(args) + elif args.command == 'circuitize': + circuitize_model(args) else: # If no command is provided, show help parser.print_help() diff --git a/new_analyzer.py b/new_analyzer.py new file mode 100644 index 0000000..0f3b95d --- /dev/null +++ b/new_analyzer.py @@ -0,0 +1,675 @@ +#!/usr/bin/env python3 +""" +Simplified ONNX Model Analyzer - No GraphSurgeon or Complex Nodes +""" + +import os +import json +import onnx +from typing import Dict, Any, List + +class SimpleOnnxAnalyzer: + """ + A simplified class for analyzing ONNX models without GraphSurgeon dependencies. + """ + + def __init__(self, onnx_model=None, model_path=None): + """ + Initialize the SimpleOnnxAnalyzer with either an ONNX model or a path to an ONNX model. + + Args: + onnx_model: An ONNX model object + onnx_path: Path to an ONNX model file + """ + if onnx_model is not None: + self.onnx_model = onnx_model + elif model_path is not None: + if os.path.isabs(model_path): + self.onnx_path = model_path + else: + self.onnx_path = model_path + self.onnx_model = onnx.load(self.onnx_path) + else: + raise ValueError("onnx_model path is not found") + + self.model_metadata = None + + def analyze(self) -> Dict[str, Any]: + """ + Analyze the ONNX model and generate basic metadata. + + Returns: + Dict[str, Any]: Basic metadata about the ONNX model + """ + # Create output directory for analysis results + output_dir = os.path.join(os.path.dirname(self.onnx_path), "simple_analysis") + if not os.path.exists(output_dir): + os.makedirs(output_dir, exist_ok=True) + + # Extract basic model metadata + graph = self.onnx_model.graph + + # Create maps for initializers and value info + initializer_map = {init.name: init for init in graph.initializer} + + # Build basic value_info map + value_info_map = {vi.name: vi for vi in graph.value_info} + value_info_map.update({vi.name: vi for vi in graph.input}) + value_info_map.update({vi.name: vi for vi in graph.output}) + + model_input_shape = self._get_model_input_shapes(graph, initializer_map) + model_output_shape = self._get_model_output_shapes(graph) + + # Store basic node metadata + node_metadata = {} + + # Process each node for basic metadata + for i, node in enumerate(graph.node): + node_info = self.analyze_node(node, i, initializer_map) + node_key = node.name if node.name else f"{node.op_type}_{i}" + node_metadata[node_key] = node_info + + # Create simplified model metadata + model_metadata = { + "original_model": self.onnx_path, + "model_type": "ONNX", + "node_count": len(graph.node), + "initializer_count": len(graph.initializer), + "input_shape": model_input_shape, + "output_shapes": model_output_shape, + "nodes": node_metadata, + "total_parameters": sum(node_info.get("parameters", 0) for node_info in node_metadata.values()) + } + + # Save model metadata + self._save_metadata_file(model_metadata, output_dir, "simple_model_metadata.json") + self.model_metadata = model_metadata + + print(f"✅ Analysis complete: {len(node_metadata)} nodes analyzed") + return model_metadata + + def analyze_node(self, node, index, initializer_map): + """ + Analyze a single node from the ONNX graph and gather basic metadata. + + Args: + node: ONNX node to analyze + index: Index of the node in the graph + initializer_map: Map of initializer names to initializers + + Returns: + dict: Basic metadata for the node + """ + node_inputs = list(node.input) + node_outputs = list(node.output) + + # Gather parameter information + parameters, parameter_details = self._get_parameter_info(node, node_inputs, initializer_map) + + # Determine in_features and out_features + in_features, out_features = self._get_feature_info(node, parameter_details) + + # Determine node type + node_type = node.op_type + + # Return basic node metadata + return { + "index": index, + "segment_name": f"{node_type}_{index}", + "parameters": parameters, + "node_type": node_type, + "in_features": in_features, + "out_features": out_features, + "parameter_details": parameter_details, + "dependencies": { + "input": node_inputs, + "output": node_outputs + } + } + + def _get_model_input_shapes(self, graph, initializer_map): + """ + Extract input shapes from the model graph. + + Args: + graph: ONNX model graph + initializer_map: Map of initializer names to initializers + + Returns: + list: List of input shapes + """ + model_input_shapes = [] + for input_info in graph.input: + if input_info.name not in initializer_map: # Skip initializers (weights) + shape = [] + if input_info.type.tensor_type.shape.dim: + for dim in input_info.type.tensor_type.shape.dim: + if dim.dim_param: + shape.append(dim.dim_param) + else: + shape.append(dim.dim_value if dim.dim_value != 0 else None) + model_input_shapes.append(shape) + return model_input_shapes + + def _get_model_output_shapes(self, graph): + """ + Extract output shapes from the model graph. + + Args: + graph: ONNX model graph + + Returns: + list: List of output shapes + """ + model_output_shapes = [] + for output_info in graph.output: + shape = [] + if output_info.type.tensor_type.shape.dim: + for dim in output_info.type.tensor_type.shape.dim: + if dim.dim_param: + shape.append(dim.dim_param) + else: + shape.append(dim.dim_value if dim.dim_value != 0 else None) + model_output_shapes.append(shape) + return model_output_shapes + + def _get_parameter_info(self, node, node_inputs, initializer_map): + """ + Determine parameter information for a node. + + Args: + node: ONNX node + node_inputs: List of node inputs + initializer_map: Map of initializer names to initializers + + Returns: + tuple: (parameters, parameter_details) + """ + # Calculate parameters if possible + parameters = 0 + parameter_details = {} + + # For Conv and Gemm nodes, we can extract parameter information from initializers + if node.op_type in ["Conv", "Gemm"]: + for inp in node_inputs: + if inp in initializer_map: + init = initializer_map[inp] + # Calculate size (number of elements) + size = 1 + for dim in init.dims: + size *= dim + + # Add to total parameters + parameters += size + + # Store parameter details + parameter_details[inp] = { + "shape": list(init.dims), + "size": size + } + + return parameters, parameter_details + + def _get_feature_info(self, node, parameter_details): + """ + Determine in_features and out_features for a node. + + Args: + node: ONNX node + parameter_details: Dictionary of parameter details + + Returns: + tuple: (in_features, out_features) + """ + in_features = None + out_features = None + + if node.op_type == "Conv": + # For Conv, in_features is input channels, out_features is output channels + if len(parameter_details) >= 1: + # Typically weight shape for Conv is [out_channels, in_channels, kernel_h, kernel_w] + weight_name = next(iter(parameter_details)) + weight_shape = parameter_details[weight_name]["shape"] + if len(weight_shape) >= 2: + out_features = weight_shape[0] + in_features = weight_shape[1] + + elif node.op_type in ["Gemm", "MatMul"]: + # For Gemm/MatMul, in_features is input dim, out_features is output dim + if len(parameter_details) >= 1: + # Typically weight shape for Gemm is [out_features, in_features] + weight_name = next(iter(parameter_details)) + weight_shape = parameter_details[weight_name]["shape"] + if len(weight_shape) >= 2: + out_features = weight_shape[0] + in_features = weight_shape[1] + + return in_features, out_features + + @staticmethod + def _save_metadata_file(metadata, output_dir, filename): + """Save metadata to a JSON file.""" + filepath = os.path.join(output_dir, filename) + with open(filepath, 'w') as f: + json.dump(metadata, f, indent=2) + print(f"Metadata saved to: {filepath}") + + def get_nodes_with_parameters(self): + """ + Get list of node indices that have parameters (Conv, Gemm, etc.) + + Returns: + List[int]: List of node indices with parameters + """ + if not self.model_metadata: + raise ValueError("Please run analyze() first") + + slice_points = [] + for node_name, node_info in self.model_metadata["nodes"].items(): + if node_info.get("parameter_details") and node_info["parameter_details"]: + slice_points.append(node_info["index"]) + + return sorted(slice_points) + + def determine_slice_points(self): + """ + Determine the slice points for the model based on nodes with parameter_details + and complex nodes in the model_metadata. + + Returns: + List[int]: List of indices representing nodes with parameter details and complex nodes + """ + if not self.model_metadata or "nodes" not in self.model_metadata: + raise ValueError("Invalid model metadata. Please run analyze() first.") + + # Find nodes with parameter_details in model_metadata + slice_points = [] + for node_name, node_info in self.model_metadata["nodes"].items(): + if node_info.get("parameter_details") and node_info["parameter_details"]: + slice_points.append(node_info["index"]) + + # Add complex nodes as slice points using the complex node info from analyzer + if "complex_nodes" in self.model_metadata: + complex_nodes = self.model_metadata["complex_nodes"] + + # Add complex node indices to slice points + for node_type, nodes in complex_nodes.items(): + for i, node_info in enumerate(nodes): + node_name = node_info['node_name'] + # For generated names like "Conv_0", extract the index + if '_' in node_name and node_name.split('_')[-1].isdigit(): + try: + generated_idx = int(node_name.split('_')[-1]) + # Find nodes with the same operation type at the corresponding position + op_type = '_'.join(node_name.split('_')[:-1]) + matching_nodes = [(name, info) for name, info in self.model_metadata["nodes"].items() + if info.get("node_type") == op_type] + + if generated_idx < len(matching_nodes): + _, meta_node_info = matching_nodes[generated_idx] + slice_points.append(meta_node_info["index"]) + print(f"Added complex node {node_type} '{node_name}' at index {meta_node_info['index']} as slice point") + except (ValueError, IndexError): + # Fallback: try to find any node with this operation type + op_type = node_name.split('_')[0] if '_' in node_name else node_name + for meta_node_name, meta_node_info in self.model_metadata["nodes"].items(): + if meta_node_info.get("node_type") == op_type: + slice_points.append(meta_node_info["index"]) + print(f"Added complex node {node_type} '{node_name}' at index {meta_node_info['index']} as slice point (fallback)") + break + + # Sort slice points by index and remove duplicates + slice_points = sorted(list(set(slice_points))) + + return slice_points + + def get_model_summary(self): + """ + Get a summary of the analyzed model. + + Returns: + Dict[str, Any]: Model summary + """ + if not self.model_metadata: + return None + + nodes_with_params = self.get_nodes_with_parameters() + slice_points = self.determine_slice_points() + + summary = { + "model_path": self.model_metadata["original_model"], + "total_nodes": self.model_metadata["node_count"], + "total_parameters": self.model_metadata["total_parameters"], + "nodes_with_parameters": len(nodes_with_params), + "slice_points": len(slice_points), + "parameter_nodes_indices": nodes_with_params, + "all_slice_points": slice_points, + "input_shapes": self.model_metadata["input_shape"], + "output_shapes": self.model_metadata["output_shapes"], + "node_types": list(set(node["node_type"] for node in self.model_metadata["nodes"].values())) + } + + return summary + + def generate_slices_metadata(self, slice_points, output_dir=None): + """ + Generate metadata for sliced ONNX models. + + Args: + slice_points: List of indices representing nodes with parameter details + output_dir: Directory where the metadata will be saved + + Returns: + dict: Complete metadata for the sliced models + """ + # Get model-level metadata + model_overview = self._get_model_metadata(slice_points) + + # Process each segment + segments = [] + + for i in range(len(slice_points)): + segment_idx = i - 1 + if segment_idx < 0: + continue + + start_idx = slice_points[i - 1] if i > 0 else 0 + end_idx = slice_points[i] + + # Skip if start and end are the same + if start_idx == end_idx: + continue + + # Get segment metadata + segment_metadata = self._get_segment_metadata( + segment_idx, + start_idx, + end_idx + ) + + if segment_metadata: + segments.append(segment_metadata) + + # Add segments to metadata + model_overview["segments"] = segments + + # Save metadata if output_dir is provided + if output_dir: + self._save_metadata_file(model_overview, output_dir, "slices_metadata.json") + + return model_overview + + def _get_model_metadata(self, slice_points): + """ + Get model-level metadata. + + Args: + slice_points: List of indices representing nodes with parameter details + + Returns: + dict: Model-level metadata + """ + # Get model input and output shapes + model_input_shapes = self.model_metadata["input_shape"] + model_output_shapes = self.model_metadata["output_shapes"] + + # Calculate total parameters + total_parameters = self.model_metadata["total_parameters"] + + # Format the original_model path to be consistent with the expected format + original_model_path = self.model_metadata["original_model"] + model_type = self.model_metadata["model_type"] + + # Create model metadata + metadata = { + "original_model": original_model_path, + "model_type": model_type, + "total_parameters": total_parameters, + "input_shape": model_input_shapes, + "output_shapes": model_output_shapes, + "slice_points": slice_points[:-1] + } + + return metadata + + def _get_segment_metadata(self, segment_idx, start_idx, end_idx): + """ + Get metadata for a specific segment. + + Args: + segment_idx: Index of the segment + start_idx: Start index of the segment + end_idx: End index of the segment + + Returns: + dict: Segment metadata + """ + # Collect nodes for this segment + segment_nodes = [] + for idx in range(start_idx, end_idx): + for node_name, node_info in self.model_metadata["nodes"].items(): + if node_info["index"] == idx: + segment_nodes.append((node_name, node_info)) + + # Skip if no nodes in this segment + if not segment_nodes: + return None + + # Calculate segment parameters + segment_parameters = sum(node_info.get("parameters", 0) for _, node_info in segment_nodes) + + # Create layers information + layers = [] + for node_name, node_info in segment_nodes: + layer_metadata = self._get_layer_metadata(node_name, node_info) + layers.append(layer_metadata) + + segment_dependencies = self._get_segment_dependencies(start_idx, end_idx) + + segment_shape = self._get_segment_shape(end_idx, start_idx) + + # Create segment info + segment_info = { + "index": segment_idx, + "filename": f"segment_{segment_idx}.onnx", + "path": os.path.join(os.path.dirname(self.onnx_path), "simple_slices", f"segment_{segment_idx}.onnx"), + "parameters": segment_parameters, + "shape": segment_shape, + "dependencies": segment_dependencies, + "layers": layers, + } + + return segment_info + + def _get_segment_dependencies(self, start_idx, end_idx): + """ + Create segment dependencies. + + Args: + start_idx: Start index of the segment + end_idx: End index of the segment + + Returns: + dict: Segment dependencies + """ + # Create segment dependencies + segment_dependencies = { + "input": [], + "output": [] + } + + # Create an output_map dictionary to store all tensor names we have encountered + output_map = {} + + # Go through each node in segment and populate output_map + for idx in range(start_idx, end_idx): + for node_name, node_info in self.model_metadata['nodes'].items(): + if node_info['index'] == idx: + # Add outputs to map + for output in node_info['dependencies']['output']: + output_map[output] = True + + # Check inputs and add any missing to dependencies + for input_name in node_info['dependencies']['input']: + if input_name not in output_map: + if input_name not in segment_dependencies['input']: + segment_dependencies['input'].append(input_name) + + # Whatever outputs we have in the map that aren't already in input dependencies + # need to be added to segment output dependencies + for output in output_map: + if output not in segment_dependencies['input']: + segment_dependencies['output'].append(output) + + return segment_dependencies + + def _get_segment_shape(self, end_idx, start_idx): + """ + Get segment shape information. + + Args: + end_idx: End index of the segment + start_idx: Start index of the segment + + Returns: + dict: Segment shape information + """ + segment_shape = { + "input": [], + "output": [] + } + + # Get first and last nodes of segment + first_node = None + last_node = None + next_node = None + + for node_name, node_info in self.model_metadata['nodes'].items(): + if node_info['index'] == start_idx: + first_node = node_info + if node_info['index'] == end_idx - 1: + last_node = node_info + if node_info['index'] == end_idx: + next_node = node_info + + # Get segment shapes from first and last nodes if available + if start_idx == 0: + segment_shape["input"] = self.model_metadata["input_shape"][0] + elif first_node and "parameter_details" in first_node: + for param_name, param_info in first_node["parameter_details"].items(): + if "shape" in param_info: + segment_shape["input"] = param_info["shape"] + break + + # For the output shape: + if last_node: + # For the last segment, use model output shape + if end_idx == len(self.model_metadata['nodes']): + segment_shape["output"] = self.model_metadata["output_shapes"][0] + # Otherwise, use the weight shape of the next node + elif next_node: + # If the next node has dependencies, use the shape of the first input + if "dependencies" in next_node and "input" in next_node["dependencies"] and next_node["dependencies"]["input"]: + # Try to find the shape from the next node's parameter details + if "parameter_details" in next_node: + # First, try to find a weight parameter with a 4D shape (for Conv layers) + for param_name, param_info in next_node["parameter_details"].items(): + if "shape" in param_info and len(param_info["shape"]) == 4: + # This is likely a Conv weight tensor + segment_shape["output"] = param_info["shape"] + break + + # If we didn't find a 4D shape, try to find a 2D shape (for Gemm/Linear layers) + if not segment_shape["output"]: + for param_name, param_info in next_node["parameter_details"].items(): + if "shape" in param_info and len(param_info["shape"]) == 2: + # This is likely a Gemm/Linear weight tensor + segment_shape["output"] = param_info["shape"] + break + + # If we still didn't find a shape, try any parameter with a shape + if not segment_shape["output"]: + for param_name, param_info in next_node["parameter_details"].items(): + if "shape" in param_info and len(param_info["shape"]) > 1: + segment_shape["output"] = param_info["shape"] + break + + # If we couldn't determine the output shape from the next node, use the last node's output features if available + if not segment_shape["output"] and "out_features" in last_node: + segment_shape["output"] = ["batch_size", last_node["out_features"]] + + return segment_shape + + def _get_layer_metadata(self, node_name, node_info): + """ + Get metadata for a specific layer. + + Args: + node_name: Name of the node + node_info: Dictionary of node information + + Returns: + dict: Layer metadata + """ + # Determine layer type + layer_type = node_info["node_type"] + + # Determine activation function + activation = self._get_activation_info(node_info["node_type"]) + + # Add parameter details if available + node_details = {} + if "parameter_details" in node_info and node_info["parameter_details"]: + for param_name, param_info in node_info["parameter_details"].items(): + node_details[param_name] = param_info + + # Add in/out features if available + if "in_features" in node_info and node_info["in_features"] is not None: + node_details["in_features"] = node_info["in_features"] + node_details["in_channels"] = node_info["in_features"] + + if "out_features" in node_info and node_info["out_features"] is not None: + node_details["out_features"] = node_info["out_features"] + node_details["out_channels"] = node_info["out_features"] + + # For Conv layers, add kernel_size, stride, padding if available + if layer_type == "Conv" and "parameter_details" in node_info: + for param_name, param_info in node_info["parameter_details"].items(): + if "shape" in param_info and len(param_info["shape"]) == 4: # Conv weight shape: [out_channels, in_channels, kernel_h, kernel_w] + node_details["kernel_size"] = [param_info["shape"][2], param_info["shape"][3]] + # Default stride and padding (could be extracted from attributes if needed) + node_details["stride"] = [1, 1] + node_details["padding"] = [0, 0] + break + + # Create layer info + layer_info = { + "name": node_name, + "type": layer_type, + "activation": activation, + "parameter_details": node_details, + } + + return layer_info + + def _get_activation_info(self, node_type): + """ + Determine activation function for a node. + + Args: + node_type: Type of the node + + Returns: + str: Activation function name + """ + activation = node_type + if node_type == "Relu": + activation = "ReLU" + elif node_type == "Sigmoid": + activation = "Sigmoid" + elif node_type == "Tanh": + activation = "Tanh" + elif node_type == "Softmax": + activation = "Softmax" + + return activation diff --git a/src/onnx_slicer.py b/new_slicer.py similarity index 59% rename from src/onnx_slicer.py rename to new_slicer.py index d0b0f1d..1e546d1 100644 --- a/src/onnx_slicer.py +++ b/new_slicer.py @@ -1,15 +1,18 @@ +#!/usr/bin/env python3 +""" +Simplified ONNX Model Slicer - No GraphSurgeon or Complex Nodes +""" + import os.path import json import onnx -from src.utils.onnx_analyzer import OnnxAnalyzer -from pathlib import Path -import onnxruntime_extensions as ortx +from onnx import helper, numpy_helper +from typing import List, Dict, Tuple, Set import onnxruntime as ort from onnxruntime.tools import optimize_onnx_model, symbolic_shape_infer -from typing import List, Dict -class OnnxSlicer: +class SimpleOnnxSlicer: def __init__(self, onnx_path): self.onnx_path = onnx_path # load onnx model @@ -19,7 +22,7 @@ def __init__(self, onnx_path): def determine_slice_points(self, model_metadata) -> List[int]: """ - Determine the slice points for the model based on nodes with parameter_details in the model_metadata. + Determine the slice points for the model based on nodes with parameter_details. Args: model_metadata: The model analysis metadata containing node information. @@ -36,8 +39,8 @@ def determine_slice_points(self, model_metadata) -> List[int]: if node_info.get("parameter_details") and node_info["parameter_details"]: slice_points.append(node_info["index"]) - # Sort slice points by index - slice_points.sort() + # Sort slice points by index and remove duplicates + slice_points = sorted(list(set(slice_points))) self.slice_points = slice_points return slice_points @@ -54,7 +57,7 @@ def _slice_setup(self, model_metadata): index_to_node_name, index_to_segment_name, output_dir) """ # Create output directory - output_dir = os.path.join(os.path.dirname(self.onnx_path), "onnx_slices") + output_dir = os.path.join(os.path.dirname(self.onnx_path), "simple_slices") if not os.path.exists(output_dir): os.makedirs(output_dir, exist_ok=True) @@ -185,13 +188,11 @@ def _get_segment_details(segment_nodes, graph, value_info_map, initializer_map): ) segment_inputs.append(t) else: - # For unknown intermediate tensors, we need to infer reasonable shapes - # Look at the node that would consume this input to guess the shape - inferred_shape = OnnxSlicer._infer_input_shape(inp, segment_nodes) + # For unknown intermediate tensors, create basic tensor info t = onnx.helper.make_tensor_value_info( inp, onnx.TensorProto.FLOAT, - inferred_shape + ["batch_size", None] # Default shape ) segment_inputs.append(t) @@ -210,60 +211,16 @@ def _get_segment_details(segment_nodes, graph, value_info_map, initializer_map): if out in all_value_infos: segment_outputs.append(all_value_infos[out]) else: - # For unknown outputs, infer shape from the producing node - inferred_shape = OnnxSlicer._infer_output_shape(out, segment_nodes) + # For unknown outputs, create basic tensor info t = onnx.helper.make_tensor_value_info( out, onnx.TensorProto.FLOAT, - inferred_shape + ["batch_size", None] # Default shape ) segment_outputs.append(t) return segment_inputs, segment_outputs, segment_initializers - @staticmethod - def _infer_input_shape(input_name, segment_nodes): - """ - Infer a reasonable shape for an input tensor based on the nodes that consume it. - """ - for node in segment_nodes: - if input_name in node.input: - if node.op_type == "Conv": - # Conv expects 4D input: [batch, channels, height, width] - return ["batch_size", None, None, None] - elif node.op_type == "Gemm": - # Gemm expects 2D input: [batch, features] - return ["batch_size", None] - elif node.op_type in ["Relu", "BatchNormalization"]: - # These preserve input shape, so use a flexible 4D shape - return ["batch_size", None, None, None] - - # Default fallback for unknown cases - return ["batch_size", None] - - @staticmethod - def _infer_output_shape(output_name, segment_nodes): - """ - Infer a reasonable shape for an output tensor based on the node that produces it. - """ - for node in segment_nodes: - if output_name in node.output: - if node.op_type == "Conv": - # Conv output is 4D: [batch, out_channels, height, width] - return ["batch_size", None, None, None] - elif node.op_type == "Gemm": - # Gemm output is 2D: [batch, out_features] - return ["batch_size", None] - elif node.op_type in ["Relu", "BatchNormalization"]: - # These preserve input shape - return ["batch_size", None, None, None] - elif node.op_type == "Reshape": - # Reshape output depends on the target shape - return ["batch_size", None] - - # Default fallback - return ["batch_size", None] - def slice(self, slice_points: List[int], model_metadata): """ Slice the ONNX model based on the provided slice points. @@ -282,13 +239,20 @@ def slice(self, slice_points: List[int], model_metadata): if not model_metadata or "nodes" not in model_metadata: raise ValueError("Invalid model metadata. Please run 'analyze()' first.") + # Apply basic shape inference to the original model + print("🔧 Applying shape inference to original model...") + try: + self.onnx_model = symbolic_shape_infer.SymbolicShapeInference.infer_shapes(self.onnx_model) + print("✅ Shape inference applied successfully to original model") + except Exception as e: + print(f"⚠️ Shape inference failed on original model: {e}, continuing with original model") + # Set up slicing environment (graph, node_map, node_type_index_map, initializer_map, value_info_map, index_to_node_name, index_to_segment_name, output_dir) = self._slice_setup(model_metadata) # Add the end of the model as a final slice point max_index = max(node_info["index"] for node_info in model_metadata["nodes"].values()) - # Always add max_index + 1 to ensure we create a segment for the last node if max_index + 1 not in slice_points: slice_points.append(max_index + 1) @@ -321,7 +285,6 @@ def slice(self, slice_points: List[int], model_metadata): segment_nodes, graph, value_info_map, initializer_map) # Create the segment model - # Create a graph with the nodes segment_graph = onnx.helper.make_graph( segment_nodes, f"segment_{segment_idx}_graph", @@ -333,93 +296,53 @@ def slice(self, slice_points: List[int], model_metadata): # Create a model from the graph segment_model = onnx.helper.make_model(segment_graph) + # Apply shape inference to each segment + print(f"🔧 Applying shape inference to segment {segment_idx}...") + try: + segment_model = symbolic_shape_infer.SymbolicShapeInference.infer_shapes(segment_model) + print(f"✅ Shape inference applied successfully to segment {segment_idx}") + except Exception as e: + print(f"⚠️ Shape inference failed on segment {segment_idx}: {e}") + # Save the segment model save_path = os.path.join(output_dir, f"segment_{segment_idx}.onnx") onnx.save(segment_model, save_path) slice_paths.append(save_path) + print(f"✅ Created segment {segment_idx}: {os.path.basename(save_path)}") + return slice_paths @staticmethod - def slice_post_process(slices_paths): - for path in slices_paths: - print(f"Processing {path}") - # Convert to absolute path if it's relative - if not os.path.isabs(path): - path = os.path.join(os.path.dirname(__file__), path) - print(f"Path: {path}") + def validate_segment(segment_path: str) -> bool: + """ + Validate that a sliced segment is a valid ONNX model. - try: - # Load the model - model = onnx.load(path) - print(f"Model loaded successfully") - - # Check if model is valid before optimization - onnx.checker.check_model(model) - print(f"Model validation passed") - - # Create output path for optimized model - path_obj = Path(path) - optimized_path = str(path_obj) - - try: - # Use onnxruntime-extensions for optimization - # This provides more advanced optimization than the basic onnxruntime optimizer - ortx.optimize_model(model, optimized_path) - print(f"Model optimization with onnxruntime-extensions successful") - - # Load the optimized model - model = onnx.load(optimized_path) - except Exception as opt_error: - print(f"Optimization with onnxruntime-extensions failed: {opt_error}, trying fallback optimization") - - # Fallback to original optimization method - try: - optimized_model = optimize_onnx_model.optimize_model(path_obj, output_path=path_obj) - if optimized_model is not None: - model = optimized_model - print(f"Fallback model optimization successful") - else: - print(f"Fallback model optimization returned None, using original model") - except Exception as fallback_error: - print(f"Fallback optimization failed: {fallback_error}, continuing with original model") - - # Try shape inference - try: - model = symbolic_shape_infer.SymbolicShapeInference.infer_shapes(model) - print(f"Shape inference successful") - except Exception as shape_error: - print(f"Shape inference failed: {shape_error}, continuing without shape inference") - - # Save the processed model - onnx.save(model, path) - print(f"Model saved successfully to {path}") - - # Additional verification step - try to create a session with the optimized model - try: - # Create session options with additional optimizations - session_options = ort.SessionOptions() - session_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL - session_options.optimized_model_filepath = str(path_obj) #+ ".optimized" - - # Register custom ops from onnxruntime-extensions - session_options.register_custom_ops_library(ortx.get_library_path()) - - # Create a session to verify and further optimize the model - _ = ort.InferenceSession(path, session_options) - print(f"Additional optimization and verification successful") - except Exception as verify_error: - print( - f"Additional optimization verification failed: {verify_error}, but model should still be usable") + Args: + segment_path: Path to the segment file - except Exception as e: - print(f"Error processing {path}: {e}") - # Continue with next slice instead of failing completely - continue + Returns: + bool: True if valid, False otherwise + """ + try: + # Load the model + model = onnx.load(segment_path) + + # Check if model is valid + onnx.checker.check_model(model) + + # Try to create a session (basic validation) + session = ort.InferenceSession(segment_path) + print(f"✅ Segment validation passed: {os.path.basename(segment_path)}") + return True + + except Exception as e: + print(f"❌ Segment validation failed: {os.path.basename(segment_path)} - {e}") + return False def slice_model(self, model_metadata=None): """ - Run the complete workflow: determine slice points and slice. + Run the complete slicing workflow. Args: model_metadata: The model analysis metadata. If None, it will be determined. @@ -427,43 +350,42 @@ def slice_model(self, model_metadata=None): Returns: Dict[str, Any]: Metadata about the sliced model """ - # Step 1: Set model metadata if provided - if not model_metadata: - # Check if model metadata exists in onnx_analysis directory - metadata_path = os.path.join(os.path.dirname(self.onnx_path), "onnx_analysis", "model_metadata.json") + # Set model metadata if provided + if model_metadata: + self.model_metadata = model_metadata + else: + # Try to load from analysis directory + metadata_path = os.path.join(os.path.dirname(self.onnx_path), "simple_analysis", "simple_model_metadata.json") if os.path.exists(metadata_path): with open(metadata_path, 'r') as f: self.model_metadata = json.load(f) else: - raise ValueError("Model metadata not found. Please run 'analyze()' first.") - - # Step 2: Determine slice points - slice_points = self.determine_slice_points(model_metadata) - - # Step 3: Slice the model - slices_paths = self.slice(slice_points, model_metadata) - self.slice_post_process(slices_paths) + raise ValueError("Model metadata not found. Please run analyzer first.") - # Step 4: generate slices metadata - onnx_analyzer.generate_slices_metadata(model_metadata, slice_points, os.path.join(os.path.dirname(self.onnx_path), "onnx_slices")) + # Determine slice points + slice_points = self.determine_slice_points(self.model_metadata) - return slices_paths + print(f"\n=== Simple Slicing Strategy ===") + print(f"Total nodes: {self.model_metadata['node_count']}") + print(f"Nodes with parameters: {len(slice_points)}") + print(f"Slice points: {slice_points}") -if __name__ == "__main__": + # Slice the model + slices_paths = self.slice(slice_points, self.model_metadata) - model_choice = 1 # Change this to test different models - - base_paths = { - 1: "models/doom", - 2: "models/net", - 3: "models/resnet", - 4: "models/yolov3" - } - - model_dir = os.path.join(base_paths[model_choice], "model.onnx") - onnx_analyzer = OnnxAnalyzer(model_path=model_dir) - onnx_slicer = OnnxSlicer(model_dir) - - # Run the complete workflow: analyze, determine slice points, and slice - model_analysis = onnx_analyzer.analyze() # Produces the onnx_analysis/model_metadata.json file - onnx_slicer.slice_model(model_analysis) # this uses the model_metadata.json file to first get slice points, then it slices it. + # Validate all segments + print(f"\n=== Validation Results ===") + valid_count = 0 + for path in slices_paths: + if self.validate_segment(path): + valid_count += 1 + + print(f"Valid segments: {valid_count}/{len(slices_paths)}") + + return { + 'slice_paths': slices_paths, + 'slice_points': slice_points, + 'total_segments': len(slices_paths), + 'valid_segments': valid_count, + 'model_metadata': self.model_metadata + } diff --git a/setup.py b/setup.py index c1b471b..2330e6d 100644 --- a/setup.py +++ b/setup.py @@ -4,7 +4,7 @@ requirements = f.read().splitlines() setup( - name="kubz", + name="dsperse", version="1.0.0", description="Distributed zkML Toolkit", author="Inference Labs", @@ -15,7 +15,7 @@ install_requires=requirements, entry_points={ "console_scripts": [ - "kubz=main:main", + "dsperse=main:main", ], }, ) diff --git a/src/runners/__init__.py b/src/analyzers/__init__.py similarity index 100% rename from src/runners/__init__.py rename to src/analyzers/__init__.py diff --git a/src/utils/model_analyzer.py b/src/analyzers/model_analyzer.py similarity index 100% rename from src/utils/model_analyzer.py rename to src/analyzers/model_analyzer.py diff --git a/src/utils/onnx_analyzer.py b/src/analyzers/onnx_analyzer.py similarity index 78% rename from src/utils/onnx_analyzer.py rename to src/analyzers/onnx_analyzer.py index ca42f1a..ddf488e 100644 --- a/src/utils/onnx_analyzer.py +++ b/src/analyzers/onnx_analyzer.py @@ -1,7 +1,9 @@ import os import json +from pathlib import Path + import onnx -from ..utils.onnx_utils import OnnxUtils +from src.utils.utils import Utils from typing import Dict, Any, List class OnnxAnalyzer: @@ -9,37 +11,22 @@ class OnnxAnalyzer: A class for analyzing ONNX models and generating metadata. """ - def __init__(self, onnx_model=None, model_path=None): + def __init__(self, model_path:str): """ Initialize the OnnxAnalyzer with either an ONNX model or a path to an ONNX model. - - Args: - onnx_model: An ONNX model object - onnx_path: Path to an ONNX model file """ - if onnx_model is not None: - self.onnx_model = onnx_model - elif model_path is not None: - path = os.path.join(os.path.dirname(os.path.dirname(__file__)), model_path) - self.onnx_path = path - self.onnx_model = onnx.load(self.onnx_path) - else: - raise ValueError("onnx_model path is not found") + self.onnx_path = os.path.abspath(model_path) + self.onnx_model = onnx.load(self.onnx_path) self.model_metadata = None - def analyze(self) -> Dict[str, Any]: + def analyze(self, save_path:str = None) -> Dict[str, Any]: """ Analyze the ONNX model and generate comprehensive metadata. Returns: Dict[str, Any]: Comprehensive metadata about the ONNX model """ - # Create output directory for analysis results - output_dir = os.path.join(os.path.dirname(self.onnx_path), "onnx_analysis") - if not os.path.exists(output_dir): - os.makedirs(output_dir, exist_ok=True) - # Extract model metadata graph = self.onnx_model.graph @@ -61,7 +48,11 @@ def analyze(self) -> Dict[str, Any]: for i, node in enumerate(graph.node): # Analyze the node and store metadata node_info = self.analyze_node(node, i, initializer_map) - node_metadata[node.name] = node_info + # Preserve original node name (may be empty or non-unique in ONNX) + node_info["original_name"] = node.name + # Use a unique, deterministic key for nodes metadata + key = node_info["segment_name"] # e.g., "Conv_12" + node_metadata[key] = node_info # Create model metadata model_metadata = { @@ -75,7 +66,13 @@ def analyze(self) -> Dict[str, Any]: } # Save model metadata - OnnxUtils.save_metadata_file(model_metadata, output_dir, "model_metadata.json") + if save_path is not None: + # output_dir = os.path.join(os.path.dirname(self.onnx_path), "onnx_analysis") + if Path(save_path).is_dir(): + os.makedirs(save_path, exist_ok=True) + save_path = os.path.join(save_path, "model_metadata.json") + + Utils.save_metadata_file(model_metadata, save_path) self.model_metadata = model_metadata return model_metadata @@ -310,7 +307,7 @@ def _create_layer_info(self, node_name, node_info): return layer_info - def generate_slices_metadata(self, model_metadata, slice_points, output_dir=None): + def generate_slices_metadata(self, model_metadata, slice_points, slices_paths, output_dir=None): """ Generate metadata for sliced ONNX models. @@ -318,6 +315,7 @@ def generate_slices_metadata(self, model_metadata, slice_points, output_dir=None model_metadata: The model analysis metadata containing node information slice_points: List of indices representing nodes with parameter details output_dir: Directory where the metadata will be saved + slices_paths: Paths to sliced onnx files Returns: dict: Complete metadata for the sliced models @@ -340,14 +338,18 @@ def generate_slices_metadata(self, model_metadata, slice_points, output_dir=None if start_idx == end_idx: continue + slice_path = slices_paths[segment_idx] if slices_paths else None + # Get segment metadata segment_metadata = self._get_segment_metadata( model_metadata, segment_idx, start_idx, - end_idx + end_idx, + slice_path, + output_dir ) - + # extract shape if segment_metadata: segments.append(segment_metadata) @@ -355,7 +357,7 @@ def generate_slices_metadata(self, model_metadata, slice_points, output_dir=None model_overview["segments"] = segments # Save metadata if output_dir is provided - OnnxUtils.save_metadata_file(model_overview, output_dir=output_dir) + Utils.save_metadata_file(model_overview, output_path=output_dir) return model_overview @@ -398,7 +400,7 @@ def _get_model_metadata(self, model_metadata, slice_points): return metadata - def _get_segment_metadata(self, model_metadata, segment_idx, start_idx, end_idx): + def _get_segment_metadata(self, model_metadata, segment_idx, start_idx, end_idx, slice_path, output_dir=None): """ Get metadata for a specific segment. @@ -407,6 +409,7 @@ def _get_segment_metadata(self, model_metadata, segment_idx, start_idx, end_idx) segment_idx: Index of the segment start_idx: Start index of the segment end_idx: End index of the segment + slice_path: Path to the sliced ONNX model Returns: dict: Segment metadata @@ -433,13 +436,17 @@ def _get_segment_metadata(self, model_metadata, segment_idx, start_idx, end_idx) segment_dependencies = self._get_segment_dependencies(model_metadata, start_idx, end_idx) - segment_shape = self._get_segment_shape(end_idx, model_metadata, start_idx) + segment_shape = self._get_segment_shape(end_idx, model_metadata, start_idx, slice_path) + + output_dir = os.path.join(os.path.dirname(output_dir), "slices", "segment_{}".format(segment_idx)) if output_dir else os.path.join(os.path.dirname(self.onnx_path), "slices", "segment_{}".format(segment_idx)) + os.makedirs(output_dir, exist_ok=True) + segment_path = os.path.abspath(os.path.join(output_dir, f"segment_{segment_idx}.onnx")) # Create segment info segment_info = { "index": segment_idx, "filename": f"segment_{segment_idx}.onnx", - "path": os.path.join(os.path.dirname(self.onnx_path), "onnx_slices", f"segment_{segment_idx}.onnx"), + "path": segment_path, "parameters": segment_parameters, "shape": segment_shape, "dependencies": segment_dependencies, @@ -449,11 +456,11 @@ def _get_segment_metadata(self, model_metadata, segment_idx, start_idx, end_idx) return segment_info def _get_segment_dependencies(self, model_metadata, start_idx, end_idx): - # TODO: fix doubling of output # Create segment dependencies segment_dependencies = { "input": [], - "output": [] + "output": [], + "filtered_inputs": [] } # Create an output_map dictionary to store all tensor names we have encountered @@ -478,12 +485,79 @@ def _get_segment_dependencies(self, model_metadata, start_idx, end_idx): for output in output_map: if output not in segment_dependencies['input']: segment_dependencies['output'].append(output) + + # Filter input names to exclude weights and biases + filtered_inputs = [] + for input_name in segment_dependencies['input']: + # Only include actual inputs that are not weights or biases + # Typically, weights and biases have names containing "weight" or "bias" + if not any(pattern in input_name.lower() for pattern in ["weight", "bias"]): + # Include model inputs and intermediate tensors + if input_name in [inp.name for inp in self.onnx_model.graph.input] or input_name.startswith('/'): + filtered_inputs.append(input_name) + + # If there are no inputs after filtering, include the first non-weight/bias input + if not filtered_inputs: + for input_name in segment_dependencies['input']: + if not any(pattern in input_name.lower() for pattern in ["weight", "bias"]): + filtered_inputs.append(input_name) + break + + # If still no inputs, use the first input as a fallback + if not filtered_inputs and segment_dependencies['input']: + filtered_inputs.append(segment_dependencies['input'][0]) + + segment_dependencies['filtered_inputs'] = filtered_inputs return segment_dependencies @staticmethod - def _get_segment_shape(end_idx, model_metadata, start_idx): + def _get_segment_shape(end_idx, model_metadata, start_idx, slice_path): segment_shape = { + "weight_shape": OnnxAnalyzer._get_weight_shape(end_idx, model_metadata, start_idx), + "tensor_shape": OnnxAnalyzer._get_tensor_shape(slice_path), + } + + return segment_shape + + + @staticmethod + def _get_tensor_shape(slice_path): + tensor_shape = { + "input": [], + "output": [] + } + + if slice_path: + onnx_model = onnx.load(slice_path) + graph = onnx_model.graph + for init in graph.initializer: + tensor_shape["input"].append(list(init.dims)) + for inp in graph.input: + # Convert Dimension objects to simple values (string or number) + dimensions = [] + for dim in inp.type.tensor_type.shape.dim: + if dim.HasField('dim_param'): + dimensions.append(dim.dim_param) # Use the string parameter directly + else: + dimensions.append(dim.dim_value) # Use the numeric value directly + tensor_shape["input"].append(dimensions) + for out in graph.output: + # Convert Dimension objects to simple values (string or number) + dimensions = [] + for dim in out.type.tensor_type.shape.dim: + if dim.HasField('dim_param'): + dimensions.append(dim.dim_param) # Use the string parameter directly + else: + dimensions.append(dim.dim_value) # Use the numeric value directly + tensor_shape["output"].append(dimensions) + + return tensor_shape + + + @staticmethod + def _get_weight_shape(end_idx, model_metadata, start_idx): + weight_shape = { "input": [], "output": [] } @@ -501,18 +575,18 @@ def _get_segment_shape(end_idx, model_metadata, start_idx): # Get segment shapes from first and last nodes if available if start_idx == 0: - segment_shape["input"] = model_metadata["input_shape"][0] + weight_shape["input"] = model_metadata["input_shape"][0] elif first_node and "parameter_details" in first_node: for param_name, param_info in first_node["parameter_details"].items(): if "shape" in param_info: - segment_shape["input"] = param_info["shape"] + weight_shape["input"] = param_info["shape"] break # For the output shape: if last_node: # For the last segment, use model output shape if end_idx == len(model_metadata['nodes']): - segment_shape["output"] = model_metadata["output_shapes"][0] + weight_shape["output"] = model_metadata["output_shapes"][0] # Otherwise, use the weight shape of the next node elif next_node: # If the next node has dependencies, use the shape of the first input @@ -524,29 +598,29 @@ def _get_segment_shape(end_idx, model_metadata, start_idx): for param_name, param_info in next_node["parameter_details"].items(): if "shape" in param_info and len(param_info["shape"]) == 4: # This is likely a Conv weight tensor - segment_shape["output"] = param_info["shape"] + weight_shape["output"] = param_info["shape"] break # If we didn't find a 4D shape, try to find a 2D shape (for Gemm/Linear layers) - if not segment_shape["output"]: + if not weight_shape["output"]: for param_name, param_info in next_node["parameter_details"].items(): if "shape" in param_info and len(param_info["shape"]) == 2: # This is likely a Gemm/Linear weight tensor - segment_shape["output"] = param_info["shape"] + weight_shape["output"] = param_info["shape"] break # If we still didn't find a shape, try any parameter with a shape - if not segment_shape["output"]: + if not weight_shape["output"]: for param_name, param_info in next_node["parameter_details"].items(): if "shape" in param_info and len(param_info["shape"]) > 1: - segment_shape["output"] = param_info["shape"] + weight_shape["output"] = param_info["shape"] break # If we couldn't determine the output shape from the next node, use the last node's output features if available - if not segment_shape["output"] and "out_features" in last_node: - segment_shape["output"] = ["batch_size", last_node["out_features"]] + if not weight_shape["output"] and "out_features" in last_node: + weight_shape["output"] = ["batch_size", last_node["out_features"]] - return segment_shape + return weight_shape def _get_layer_metadata(self, node_name, node_info): """ diff --git a/src/analyzers/runner_analyzer.py b/src/analyzers/runner_analyzer.py new file mode 100644 index 0000000..e5a4353 --- /dev/null +++ b/src/analyzers/runner_analyzer.py @@ -0,0 +1,237 @@ +""" +Generates execution chain metadata for EzKL circuit and ONNX slice inference +with proper fallback mapping and security calculation. +""" +import logging +import json +import os +from pathlib import Path + +from src.utils.utils import Utils + +logger = logging.getLogger(__name__) +logging.basicConfig(level=logging.INFO) + +class RunnerAnalyzer: + def __init__(self, model_directory): + """ + Args: + model_directory: Path to the model directory. + """ + self.model_directory = model_directory + self.slices_dir = Path(os.path.join(model_directory, "slices")).resolve() + self.slices_metadata_path = self.slices_dir / "metadata.json" + + self.size_limit = 100 * 1024 * 1024 # 100MB + + if not self.slices_dir.exists(): + raise FileNotFoundError(f"Slice output directory not found: {self.slices_dir}") + + def generate_metadata(self, save_path=None): + """ + Generate runner metadata and save to run_metadata.json. + Returns: + Path to generated metadata for running the slices and model + """ + + logger.info(f"Generating runner metadata...") + slices_metadata = self._load_slices_metadata() + + runner_metadata = self._generate_metadata(slices_metadata) + + save_path = Path(save_path) if save_path else Path(self.model_directory) / "run" / "metadata.json" + save_path = save_path.resolve() + save_path.parent.mkdir(parents=True, exist_ok=True) + runner_metadata["run_directory"] = str(save_path.parent) + + print(f"Saving runner metadata to {save_path}") + Utils.save_metadata_file(runner_metadata, save_path) + + logger.info(f"Runner metadata saved to {save_path}") + + return save_path + + def _load_slices_metadata(self): + try: + with open(self.slices_metadata_path, 'r') as f: + return json.load(f) + except Exception as e: + logger.error(f"Failed to load slices metadata: {e}") + raise + + def _generate_metadata(self, slices_metadata): + segments = slices_metadata.get('segments', []) + slices = self._process_slices(segments) + execution_chain = self._build_execution_chain(segments) + circuit_slices = self._build_circuit_slices(slices) + overall_security = self._calculate_security(slices) + return { + "model_path": str(self.model_directory), + "overall_security": overall_security, + "slices": slices, + "execution_chain": execution_chain, + "circuit_slices": circuit_slices, + } + + def _process_slices(self, segments): + """ + Build the slices dictionary with metadata for each segment. + """ + + slices = {} + for segment in segments: + segment_idx = segment['index'] + segment_key = f"segment_{segment_idx}" + + # Get EZKL paths directly from metadata + ezkl_circuitization = segment.get('ezkl_circuitization', {}) + + # Use paths from metadata or set to None if not present + compiled_circuit_path = ezkl_circuitization.get('compiled', None) + settings_path = ezkl_circuitization.get('settings', None) + pk_path = ezkl_circuitization.get('pk_key', None) + vk_path = ezkl_circuitization.get('vk_key', None) + + # Set circuit_exists and keys_exist flags based on whether the paths exist in metadata + circuit_exists = compiled_circuit_path is not None and settings_path is not None + keys_exist = pk_path is not None and vk_path is not None + + # Determine circuit size and use_circuit flag + circuit_size = 0 + if circuit_exists: + try: + circuit_size = Path(compiled_circuit_path).stat().st_size + except: + # If there's any error, just use 0 as the size + circuit_size = 0 + + use_circuit = circuit_exists and keys_exist and circuit_size <= self.size_limit + + onnx_slice_path = segment.get('path', '') + if not onnx_slice_path: + logger.warning(f"No ONNX slice path for segment {segment_idx}") + + # Build slice metadata + slice_metadata = { + "path": onnx_slice_path, + "input_shape": segment.get('shape', {}).get('tensor_shape', {}).get('input', ["batch_size", "unknown"]), + "output_shape": segment.get('shape', {}).get('tensor_shape', {}).get('output', + ["batch_size", "unknown"]), + "ezkl_compatible": True, + "ezkl": use_circuit, + "circuit_size": circuit_size, + "dependencies": segment.get('dependencies', {}), + "parameters": segment.get('parameters', 0) + } + + # Add circuit paths to metadata if they exist in the segment metadata + if circuit_exists: + # Get the parent directory for the circuit to derive proof and witness paths + circuit_dir = str(Path(compiled_circuit_path).parent) + + # Use standard naming convention for proof and witness paths + # proof_path = f"{circuit_dir}/segment_{segment_idx}_proof.json" + # witness_path = f"{circuit_dir}/segment_{segment_idx}_witness.json" + + slice_metadata.update({ + "circuit_path": compiled_circuit_path, + # "proof_path": proof_path, + # "witness_path": witness_path, + "settings_path": settings_path + }) + + if keys_exist: + slice_metadata.update({ + "vk_path": vk_path, + "pk_path": pk_path + }) + + slices[segment_key] = slice_metadata + + return slices + + @staticmethod + def _build_execution_chain(segments): + """ + Build the execution chain with proper node connections and fallback mapping. + """ + execution_chain = { + "head": "segment_0" if segments else None, + "nodes": {}, + "fallback_map": {} + } + + for segment in segments: + segment_idx = segment['index'] + segment_key = f"segment_{segment_idx}" + + # Get EZKL paths from metadata + ezkl_circuitization = segment.get('ezkl_circuitization', {}) + compiled_circuit_path = ezkl_circuitization.get('compiled', None) + + # Get ONNX path + onnx_slice_path = segment.get('path', '') + + # Determine if circuit is usable based purely on metadata presence + circuit_exists = compiled_circuit_path is not None + use_circuit = circuit_exists and ezkl_circuitization.get('pk_key') is not None + + # Set up the execution chain node + next_slice = f"segment_{segment_idx + 1}" if segment_idx < len(segments) - 1 else None + execution_chain["nodes"][segment_key] = { + "segment_id": segment_key, + "primary": compiled_circuit_path if use_circuit else onnx_slice_path, + "fallback": onnx_slice_path, + "use_circuit": use_circuit, + "next": next_slice, + "circuit_path": compiled_circuit_path if circuit_exists else None, + "onnx_path": onnx_slice_path + } + + # Set up the fallback map + if circuit_exists and onnx_slice_path: + execution_chain["fallback_map"][compiled_circuit_path] = onnx_slice_path + elif onnx_slice_path: + execution_chain["fallback_map"][segment_key] = onnx_slice_path + + return execution_chain + + @staticmethod + def _build_circuit_slices(slices): + """ + Build dictionary tracking which slices use circuits. + """ + circuit_slices = {} + for slice_key, slice_data in slices.items(): + # Check if the slice has circuit_path and pk_path set from the metadata + has_circuit = slice_data.get("circuit_path") is not None + has_keys = slice_data.get("pk_path") is not None + + # A slice is considered to use a circuit if it has both circuit_path and pk_path + circuit_slices[slice_key] = has_circuit and has_keys + + return circuit_slices + + @staticmethod + def _calculate_security(slices): + if not slices: + return 0.0 + total_slices = len(slices) + circuit_slices = sum(1 for slice_data in slices.values() if slice_data.get("ezkl", False)) + return round((circuit_slices / total_slices) * 100, 1) + +if __name__ == "__main__": + + model_choice = 1 + + base_paths = { + 1: "../models/doom", + 2: "../models/net", + 3: "../models/resnet" + } + + model_dir = base_paths[model_choice] #+ "/model.onnx" + model_path = Path(model_dir).resolve() + print(f"Model path: {model_path}") + metadata = RunnerAnalyzer(model_dir) + metadata.generate_metadata() diff --git a/src/backends/ezkl.py b/src/backends/ezkl.py new file mode 100644 index 0000000..ea4d426 --- /dev/null +++ b/src/backends/ezkl.py @@ -0,0 +1,478 @@ +import json +import os +import random +import subprocess +import torch +import logging +from pathlib import Path +import onnx +from src.utils.utils import Utils +from src.utils.runner_utils.runner_utils import RunnerUtils + +# Configure logger +logger = logging.getLogger(__name__) + +class EZKL: + def __init__(self, model_directory=None): + """ + Initialize the EZKL backend. + + Args: + model_directory (str, optional): Path to the model directory. + + Raises: + RuntimeError: If EZKL is not installed + """ + self.env = os.environ + self.model_directory = model_directory + + if model_directory: + self.base_path = os.path.join(model_directory, "ezkl") + + # Check if ezkl is installed via cli + try: + result = subprocess.run(['ezkl', '--version'], + stdout=subprocess.PIPE, + stderr=subprocess.PIPE) + if result.returncode != 0: + raise RuntimeError("EZKL CLI not found. Please install EZKL first.") + except FileNotFoundError: + raise RuntimeError("EZKL CLI not found. Please install EZKL first.") + + # + # High-level methods that dispatch to specific implementations + # + + def generate_witness(self, input_file: str, model_path: str, output_file: str, vk_path: str): + """ + Generate a witness for the given model and input. + + Args: + input_file (str): Path to the input file + model_path (str): Path to the compiled model + output_file (str): Path where to save the output + vk_path (str): Path to the verification key + + Returns: + tuple: (success, output) where success is a boolean and output is the processed witness output + """ + # Normalize possible Path-like arguments to strings for subprocess and logging clarity + input_file = str(input_file) + model_path = str(model_path) + output_file = str(output_file) + vk_path = str(vk_path) + + # Validate required files exist + if not os.path.exists(input_file): + raise FileNotFoundError(f"Input file not found: {input_file}") + if not os.path.exists(model_path): + raise FileNotFoundError(f"Model file not found: {model_path}") + if not os.path.exists(vk_path): + raise FileNotFoundError(f"Verification key file not found: {vk_path}") + + # Create output directory if it doesn't exist + os.makedirs(os.path.dirname(output_file), exist_ok=True) + + try: + process = subprocess.run( + [ + "ezkl", + "gen-witness", + "--data", input_file, + "--compiled-circuit", model_path, + "--output", output_file, + "--vk-path", vk_path + ], + env=self.env, + check=True, + capture_output=True, + text=True + ) + + if process.returncode != 0: + error_msg = f"Witness generation failed with return code {process.returncode}" + if process.stderr: + error_msg += f"\nError: {process.stderr}" + return False, error_msg + + except subprocess.CalledProcessError as e: + error_msg = f"Witness generation failed: {e}" + if e.stderr: + error_msg += f"\nError output: {e.stderr}" + return False, error_msg + + # return the processed outputs + with open(output_file, "r") as f: + witness_data = json.load(f) + output = self.process_witness_output(witness_data) + + return True, output + + def prove(self, witness_path: str, model_path: str, proof_path: str, pk_path: str, check_mode: str = "unsafe"): + """ + Generate a proof for the given witness and model. + + Args: + witness_path (str): Path to the witness file + model_path (str): Path to the compiled model + proof_path (str): Path where to save the proof + pk_path (str): Path to the proving key + check_mode (str, optional): Check mode for the prover. Defaults to "unsafe". + + Returns: + tuple: (success, results) where success is a boolean and results is the path to the proof + """ + # Normalize path-like args + witness_path = str(witness_path) + model_path = str(model_path) + proof_path = str(proof_path) + pk_path = str(pk_path) + + # Validate required files exist + if not os.path.exists(witness_path): + raise FileNotFoundError(f"Witness file not found: {witness_path}") + if not os.path.exists(model_path): + raise FileNotFoundError(f"Model file not found: {model_path}") + if not os.path.exists(pk_path): + raise FileNotFoundError(f"PK key file not found: {pk_path}") + + # Create output directory if it doesn't exist + os.makedirs(os.path.dirname(proof_path), exist_ok=True) + + try: + process = subprocess.run( + [ + "ezkl", + "prove", + "--check-mode", check_mode, + "--witness", witness_path, + "--compiled-circuit", model_path, + "--proof-path", proof_path, + "--pk-path", pk_path + ], + env=self.env, + check=True, + capture_output=True, + text=True + ) + + if process.returncode != 0: + error_msg = f"Proof generation failed with return code {process.returncode}" + if process.stderr: + error_msg += f"\nError: {process.stderr}" + return False, error_msg + + except subprocess.CalledProcessError as e: + print(f"Error during proof generation: {e}") + return False, e.stderr + + results = proof_path + return True, results + + def verify(self, proof_path: str, settings_path: str, vk_path: str) -> bool: + """ + Verify a proof. + + Args: + proof_path (str): Path to the proof file + settings_path (str): Path to the settings file + vk_path (str): Path to the verification key + + Returns: + bool: True if verification succeeded, False otherwise + """ + # Normalize path-like args + proof_path = str(proof_path) + settings_path = str(settings_path) + vk_path = str(vk_path) + + # Validate required files exist + if not os.path.exists(proof_path): + raise FileNotFoundError(f"Proof file not found: {proof_path}") + if not os.path.exists(settings_path): + raise FileNotFoundError(f"Settings file not found: {settings_path}") + if not os.path.exists(vk_path): + raise FileNotFoundError(f"Verification key file not found: {vk_path}") + + try: + process = subprocess.run( + [ + "ezkl", + "verify", + "--proof-path", proof_path, + "--settings-path", settings_path, + "--vk-path", vk_path + ], + env=self.env, + check=True, + capture_output=True, + text=True + ) + + if process.returncode != 0: + error_msg = f"Verification generation failed with return code {process.returncode}" + if process.stderr: + error_msg += f"\nError: {process.stderr}" + return False + return True + except subprocess.CalledProcessError as e: + print(f"Error verifying proof: {e}") + return False + + + def gen_settings(self, model_path: str, settings_path: str, param_visibility: str = "fixed", input_visibility: str = "public"): + """ + Generate EZKL settings. + Returns (success: bool, error: str|None) + """ + if not os.path.exists(model_path): + raise FileNotFoundError(f"Model file not found: {model_path}") + os.makedirs(os.path.dirname(settings_path) or ".", exist_ok=True) + try: + process = subprocess.run( + [ + "ezkl", + "gen-settings", + "--param-visibility", param_visibility, + "--input-visibility", input_visibility, + "--model", model_path, + "--settings-path", settings_path, + ], + env=self.env, + check=True, + capture_output=True, + text=True, + ) + if process.returncode != 0: + return False, process.stderr or "gen-settings failed" + return True, None + except subprocess.CalledProcessError as e: + return False, getattr(e, "stderr", str(e)) + + def calibrate_settings(self, model_path: str, settings_path: str, data_path: str, target: str = None): + """ + Calibrate EZKL settings using provided data. + Returns (success: bool, error: str|None) + """ + if not os.path.exists(model_path): + raise FileNotFoundError(f"Model file not found: {model_path}") + if not os.path.exists(settings_path): + raise FileNotFoundError(f"Settings file not found: {settings_path}") + if not os.path.exists(data_path): + raise FileNotFoundError(f"Calibration data file not found: {data_path}") + cmd = [ + "ezkl", + "calibrate-settings", + "--model", model_path, + "--settings-path", settings_path, + "--data", data_path, + ] + if target: + cmd += ["--target", target] + try: + process = subprocess.run( + cmd, + env=self.env, + check=True, + capture_output=True, + text=True, + ) + if process.returncode != 0: + return False, process.stderr or "calibrate-settings failed" + return True, None + except subprocess.CalledProcessError as e: + return False, getattr(e, "stderr", str(e)) + + def compile_circuit(self, model_path: str, settings_path: str, compiled_path: str): + """ + Compile EZKL circuit. + Returns (success: bool, error: str|None) + """ + if not os.path.exists(model_path): + raise FileNotFoundError(f"Model file not found: {model_path}") + if not os.path.exists(settings_path): + raise FileNotFoundError(f"Settings file not found: {settings_path}") + os.makedirs(os.path.dirname(compiled_path) or ".", exist_ok=True) + try: + process = subprocess.run( + [ + "ezkl", + "compile-circuit", + "--model", model_path, + "--settings-path", settings_path, + "--compiled-circuit", compiled_path, + ], + env=self.env, + check=True, + capture_output=True, + text=True, + ) + if process.returncode != 0: + return False, process.stderr or "compile-circuit failed" + return True, None + except subprocess.CalledProcessError as e: + return False, getattr(e, "stderr", str(e)) + + def setup(self, compiled_path: str, vk_path: str, pk_path: str): + """ + Generate proving and verification keys (setup). + Returns (success: bool, error: str|None) + """ + if not os.path.exists(compiled_path): + raise FileNotFoundError(f"Compiled circuit file not found: {compiled_path}") + os.makedirs(os.path.dirname(vk_path) or ".", exist_ok=True) + os.makedirs(os.path.dirname(pk_path) or ".", exist_ok=True) + try: + process = subprocess.run( + [ + "ezkl", + "setup", + "--compiled-circuit", compiled_path, + "--vk-path", vk_path, + "--pk-path", pk_path, + ], + env=self.env, + check=True, + capture_output=True, + text=True, + ) + if process.returncode != 0: + return False, process.stderr or "setup failed" + return True, None + except subprocess.CalledProcessError as e: + return False, getattr(e, "stderr", str(e)) + + def circuitization_pipeline(self, model_path, output_path, input_file_path=None, segment_details=None): + """ + Run the EZKL circuitization pipeline: gen-settings, calibrate-settings, compile-circuit, setup. + + Args: + model_path (str): Path to the ONNX model file. + output_path (str): Base path for output files (without extension). + input_file_path (str, optional): Path to input data file for calibration. + segment_details (dict, optional): Details about the segment being processed. + + Returns: + dict: Dictionary containing paths to generated files and any error information. + """ + # Ensure model_path exists + if not os.path.exists(model_path): + raise FileNotFoundError(f"Model file not found: {model_path}") + + # Create output directory + os.makedirs(output_path, exist_ok=True) + + model_name = Path(model_path).stem + + # Define file paths + settings_path = os.path.join(output_path, f"{model_name}_settings.json") + compiled_path = os.path.join(output_path, f"{model_name}_model.compiled") + vk_path = os.path.join(output_path, f"{model_name}_vk.key") + pk_path = os.path.join(output_path, f"{model_name}_pk.key") + + # Initialize circuitization data dictionary + circuitization_data = { + "settings": None, + "compiled": None, + "vk_key": None, + "pk_key": None, + "calibration": None + } + + try: + # Step 1: Generate settings + logger.info(f"Generating settings for {model_name}") + ok, err = self.gen_settings(model_path=model_path, settings_path=settings_path) + if not ok: + logger.warning("Failed to generate settings") + circuitization_data["gen-settings_error"] = err + else: + circuitization_data["settings"] = settings_path + + # Step 2/3: Calibrate settings + if input_file_path and os.path.exists(input_file_path): + logger.info(f"Calibrating settings using {input_file_path}") + ok, err = self.calibrate_settings(model_path=model_path, settings_path=settings_path, data_path=input_file_path, target="accuracy") + circuitization_data["calibration"] = input_file_path + if not ok: + logger.warning("Failed to calibrate settings") + circuitization_data["calibrate-settings_error"] = err + else: + # If no input file, log and skip calibration + logger.info("No input file provided, skipping calibration step") + + # Step 4: Compile circuit + logger.info(f"Compiling circuit for {model_path}") + ok, err = self.compile_circuit(model_path=model_path, settings_path=settings_path, compiled_path=compiled_path) + if not ok: + logger.warning("Failed to compile circuit") + circuitization_data["compile-circuit_error"] = err + else: + circuitization_data["compiled"] = compiled_path + + # Step 5: Setup (generate verification and proving keys) + logger.info("Setting up verification and proving keys") + ok, err = self.setup(compiled_path=compiled_path, vk_path=vk_path, pk_path=pk_path) + if not ok: + logger.warning("Failed to setup (generate keys)") + circuitization_data["setup_error"] = err + else: + circuitization_data["vk_key"] = vk_path + circuitization_data["pk_key"] = pk_path + + logger.info(f"Circuitization pipeline completed for {model_path}") + + except Exception as e: + error_msg = f"Error during circuitization: {str(e)}" + logger.error(error_msg) + circuitization_data["error"] = error_msg + + return circuitization_data + + @staticmethod + def process_witness_output(witness_data): + """ + Process the witness.json data to get prediction results. + """ + try: + rescaled_outputs = witness_data["pretty_elements"]["rescaled_outputs"][0] + except KeyError: + print("Error: Could not find rescaled_outputs in witness data") + return None + + # Convert string values to float and create a tensor + float_values = [float(val) for val in rescaled_outputs] + + # Create a tensor with shape [1, num_classes] to match batch_size, num_classes format + tensor_output = torch.tensor([float_values], dtype=torch.float32) + + # Process the tensor through _process_final_output (simulating one segment) + output = RunnerUtils.process_final_output(tensor_output) + return output + + +if __name__ == "__main__": + # Choose which model to test + model_choice = 1 # Change this to test different models + + base_paths = { + 1: "../models/doom", + 2: "../models/net", + 3: "../models/resnet", + 4: "../models/yolov3" + } + abs_path = os.path.abspath(base_paths[model_choice]) + model_dir = abs_path + slices_dir = os.path.join(abs_path, "slices") + + # Circuitize + model_path = os.path.abspath(model_dir) + EZKL().circuitize(model_path=abs_path) + + # # Generate witness + # input_file = os.path.join(model_dir, "input.json") + # model_path = os.path.join(model_dir, "model.compiled") + # vk_path = os.path.join(model_dir, "vk.json") + # output_file = os.path.join(model_dir, "witness.json") + # result = ezkl.generate_witness(input_file=input_file, model_path=model_path, output_file=output_file, vk_path=vk_path) + # print(result) \ No newline at end of file diff --git a/src/runners/jstprove_runner.py b/src/backends/jstprove.py similarity index 98% rename from src/runners/jstprove_runner.py rename to src/backends/jstprove.py index d1ee1f7..0f72af2 100644 --- a/src/runners/jstprove_runner.py +++ b/src/backends/jstprove.py @@ -6,12 +6,12 @@ from src.utils.model_utils import ModelUtils -class JSTProveRunner: +class JSTProve: def __init__(self, model_directory): self.jstprove_project_path = os.environ.get('JSTPROVE_PATH') if not self.jstprove_project_path: raise ValueError("JSTPROVE_PATH environment variable is not set") - + # check if jstprove cli python file exists, it should be jstprove project path/cli.py cli_path = os.path.join(self.jstprove_project_path, 'cli.py') @@ -28,13 +28,13 @@ def generate_witness(self, mode: str = None, input_file: str = None) -> dict: return self.generate_witness_sliced(input_file=input_file) else: return self.generate_witness_whole(input_file=input_file) - + def prove(self, mode: str = None): if mode == "sliced": return self.prove_slices() else: return self.prove_whole() - + def verify(self, mode: str = None, input_file: str = None) -> dict: if mode == "sliced": return self.verify_slices(input_path=input_file) @@ -47,8 +47,7 @@ def circuitize(self, mode: str = None) -> dict: else: return self.circuitize_whole() - - def generate_witness_whole(self, input_file: str = None, model_path: str = None) -> dict: + def generate_witness_whole(self, input_file: str = None, model_path: str = None) -> dict: # set base params parent_dir = self.src_dir model_folder = model_path or os.path.join(parent_dir, self.base_path, "model") @@ -86,7 +85,6 @@ def generate_witness_whole(self, input_file: str = None, model_path: str = None results = output_data.get('rescaled_output', None)[0] return results - def generate_witness_sliced(self, input_file: str = None, model_path: str = None) -> dict: parent_dir = self.src_dir slices_folder = model_path or os.path.join(parent_dir, self.base_path, "slices") @@ -287,7 +285,7 @@ def verify_slices(self, input_path: str = None) -> dict: json.dump(json.load(src), dst) except Exception as e: print(f"Error copying input file: {e}") - + for segment_idx in range(num_segments): # Get segment model paths segment_name = f"segment_{segment_idx}" @@ -474,9 +472,9 @@ def circuitize_slices(self): } model_dir = base_paths[model_choice] - runner = JSTProveRunner(model_dir) + runner = JSTProve(model_dir) # Test - results = runner.verify(mode="sliced") # change function and mode when needed + results = runner.verify(mode="sliced") # change function and mode when needed print(results) diff --git a/src/backends/onnx_models.py b/src/backends/onnx_models.py new file mode 100644 index 0000000..f391dde --- /dev/null +++ b/src/backends/onnx_models.py @@ -0,0 +1,292 @@ +import logging +import os + +import numpy as np +import onnxruntime as ort +import torch + +from src.utils.runner_utils.runner_utils import RunnerUtils + +logger = logging.getLogger(__name__) +logging.basicConfig(level=logging.INFO) + +class OnnxModels: + def __init__(self): + self.device = torch.device("cpu") + + @staticmethod + def run_inference(input_file: str, model_path: str, output_file: str): + """ + Run inference with the ONNX model and return the logits, probabilities, and predictions. + """ + try: + # Create an ONNX Runtime session + session = ort.InferenceSession(model_path) + + # Convert PyTorch tensor to numpy array for ONNX Runtime + input_tensor = RunnerUtils.preprocess_input(input_file) + + # Apply proper shaping based on the ONNX model's expected input + input_dict = OnnxModels.apply_onnx_shape(model_path, input_tensor) + + # Run inference + raw_output = session.run(None, input_dict) + + # Convert the output back to a PyTorch tensor + output_tensor = torch.tensor(raw_output[0]) + + # Process the output + result = RunnerUtils.process_final_output(output_tensor) + + RunnerUtils.save_to_file_flattened(result['logits'], output_file) + + return True, result + + except Exception as e: + logger.warning(f"Error during inference: {e}") + return False, None + + @staticmethod + def apply_onnx_shape(model_path, input_tensor, is_numpy=False): + """ + Reshapes the input tensor to match the expected input shape of the ONNX model. + + Args: + model_path: Path to the ONNX model + input_tensor: Input tensor (can be a PyTorch tensor or NumPy array) + is_numpy: Boolean indicating if input_tensor is already a NumPy array + + Returns: + Dictionary mapping input names to properly shaped tensors + """ + try: + # Create an ONNX Runtime session to get model metadata + session = ort.InferenceSession(model_path) + + # Get input details from the model + model_inputs = session.get_inputs() + logger.info(f"Model expects {len(model_inputs)} input(s)") + + # Convert input to numpy if it's not already, enforce float32 + if not is_numpy: + if isinstance(input_tensor, torch.Tensor): + input_numpy = input_tensor.detach().cpu().numpy().astype(np.float32, copy=False) + else: + input_numpy = np.array(input_tensor, dtype=np.float32) + else: + input_numpy = np.asarray(input_tensor, dtype=np.float32) + + # Handle multiple inputs + if len(model_inputs) > 1: + # If we have a flattened tensor, we need to split it for each input + result = {} + total_elements_used = 0 + + for i, model_input in enumerate(model_inputs): + input_name = model_input.name + input_shape = model_input.shape + logger.info(f"Input {i + 1}: {input_name} with shape {input_shape}") + + # Calculate number of elements needed for this input + elements_needed = 1 + final_shape = [] + + for dim in input_shape: + if isinstance(dim, int): + elements_needed *= dim + final_shape.append(dim) + elif dim == 'batch_size' or dim.startswith('unk'): + batch_size = 1 # Default batch size + elements_needed *= batch_size + final_shape.append(batch_size) + else: + # For any other symbolic dimension, default to 1 + elements_needed *= 1 + final_shape.append(1) + + # Extract the portion of the flattened tensor for this input using correct bounds + flat = input_numpy.flatten() + end_idx = total_elements_used + elements_needed + if flat.size >= end_idx: + input_portion = flat[total_elements_used:end_idx] + total_elements_used = end_idx + else: + # Use what's left and pad if needed + input_portion = flat[total_elements_used:] + got = input_portion.size + if got < elements_needed: + logger.warning(f"Not enough elements for input {input_name}. Expected {elements_needed}, got {got}") + padding = np.zeros(elements_needed - got, dtype=np.float32) + input_portion = np.concatenate([input_portion, padding]).astype(np.float32, copy=False) + + # Ensure dtype float32 and reshape to match expected shape + input_portion = np.asarray(input_portion, dtype=np.float32).reshape(final_shape) + result[input_name] = input_portion + + return result + else: + # Single input case + input_name = model_inputs[0].name + input_shape = model_inputs[0].shape + logger.info(f"Single input: {input_name} with shape {input_shape}") + + # Check if we need to reshape + if len(input_numpy.shape) != len(input_shape): + # Determine the appropriate shape + final_shape = [] + for dim in input_shape: + if isinstance(dim, int): + final_shape.append(dim) + elif dim == 'batch_size' or dim.startswith('unk'): + final_shape.append(1) # Default batch size to 1 + else: + # For any other symbolic dimension, default to 1 + final_shape.append(1) + + # Calculate total elements needed + elements_needed = np.prod(final_shape) + + # Check if we have enough elements + if input_numpy.size < elements_needed: + logger.warning(f"Not enough elements. Expected {elements_needed}, got {input_numpy.size}") + + # Pad with zeros if necessary + flat = input_numpy.flatten() + padding = np.zeros(elements_needed - flat.size, dtype=np.float32) + input_numpy = np.concatenate([flat, padding]).astype(np.float32, copy=False) + + # Reshape the input + input_numpy = input_numpy.reshape(final_shape) + logger.info(f"Reshaped input to {input_numpy.shape}") + elif not np.array_equal(input_numpy.shape, + [int(dim) if isinstance(dim, int) else 1 for dim in input_shape]): + # If dimensions don't match (after replacing symbolic dims with 1) + expected_shape = [int(dim) if isinstance(dim, int) else 1 for dim in input_shape] + + # Check if total elements match + elements_needed = np.prod(expected_shape) + if input_numpy.size == elements_needed: + # If same number of elements, just reshape + input_numpy = input_numpy.reshape(expected_shape) + logger.info(f"Reshaped input from {input_numpy.shape} to {expected_shape}") + else: + # Try to use what we have + logger.warning(f"Input shape {input_numpy.shape} doesn't match expected shape {expected_shape}") + + # Flatten and reshape, padding if necessary + flat = input_numpy.flatten() + if flat.size < elements_needed: + padding = np.zeros(elements_needed - flat.size, dtype=np.float32) + flat = np.concatenate([flat, padding]).astype(np.float32, copy=False) + elif flat.size > elements_needed: + flat = flat[:elements_needed] + + input_numpy = flat.reshape(expected_shape) + + # Ensure float32 before returning + input_numpy = np.asarray(input_numpy, dtype=np.float32) + return {input_name: input_numpy} + + except Exception as e: + logger.error(f"Error in apply_onnx_shape: {e}") + # In case of error, return the original tensor with the first input name, enforce float32 + safe_np = np.asarray(input_numpy, dtype=np.float32) + try: + inputs = session.get_inputs() + if len(inputs) > 0: + return {inputs[0].name: safe_np} + except Exception: + pass + return {"input": safe_np} + + @staticmethod + def _run_inference_raw(model_path: str, input_file: str): + """ + Internal: Run plain ONNX inference and return full raw outputs. + + Args: + model_path: Path to the .onnx file + input_file: Path to input.json + + Returns: + (success: bool, result: dict) + result on success: + { + "outputs": [ + { + "name": , + "shape": [..], + "dtype": "float32" (etc), + "data": + }, ... + ] + } + result on failure: + { "error": } + """ + try: + # Build ORT session + session = ort.InferenceSession(model_path) + + # Read and prepare input + input_tensor = RunnerUtils.preprocess_input(input_file) + input_dict = OnnxModels.apply_onnx_shape(model_path, input_tensor) + + # Collect all output names to preserve ordering + outputs_meta = session.get_outputs() + output_names = [o.name for o in outputs_meta] + + # Run inference + raw_outputs = session.run(output_names, input_dict) + + # Process output tensor for prediction + output_tensor = torch.tensor(raw_outputs[0]) + prediction_result = RunnerUtils.process_final_output(output_tensor) + + # Normalize to JSON-serializable result + outputs = [] + for meta, arr in zip(outputs_meta, raw_outputs): + np_arr = np.asarray(arr) + outputs.append({ + "name": meta.name, + "shape": list(np_arr.shape), + "dtype": str(np_arr.dtype), + "data": np_arr.tolist(), + }) + + return True, {"prediction": prediction_result, "outputs": outputs} + + except Exception as e: + logger.warning(f"Error during raw ONNX inference: {e}") + return False, {"error": str(e)} + + +# Example usage +if __name__ == "__main__": + + # Choose which model to test + model_choice = 5 # Change this to test different models + + # Model configurations + base_paths = { + 1: "../models/doom", + 2: "../models/net", + 3: "../models/resnet", + 4: "../models/yolov3", + 5: "../models/age", + 6: "../models/version" + } + + # Get model directory + abs_path = os.path.abspath(base_paths[model_choice]) + slices_dir = os.path.join(abs_path, "slices") + # input_json = os.path.join(abs_path, "input.json") + output_json = os.path.join(abs_path, "output.json") + # model_path = os.path.join(abs_path, "model.onnx") + input_json = "/Volumes/SSD/Users/dan/Projects/dsperse/src/models/age/run/run_20250905_154933/segment_12/input.json" + model_path = "/Volumes/SSD/Users/dan/Projects/dsperse/src/models/age/slices/segment_12/segment_12.onnx" + print(f"Running inference on {abs_path}") + + # result = OnnxModels.run_inference(input_file=input_json, model_path=os.path.join(abs_path, "model.onnx"), output_file="output.json") + result = OnnxModels._run_inference_raw(model_path=model_path, input_file=input_json) + print(result) diff --git a/src/runners/model_runner.py b/src/backends/pth_models.py similarity index 99% rename from src/runners/model_runner.py rename to src/backends/pth_models.py index 366d7dd..140e527 100644 --- a/src/runners/model_runner.py +++ b/src/backends/pth_models.py @@ -6,7 +6,7 @@ Conv3Segment as doomConv3, FC1Segment as doomFC1, FC2Segment as doomFC2 from src.models.net.model import Net, Conv1Segment as netConv1, Conv2Segment as netConv2, FC1Segment as netFC1, \ FC2Segment as netFC2, FC3Segment as netFC3 -from src.runners.runner_utils import RunnerUtils +from src.utils.runner_utils.runner_utils import RunnerUtils env = os.environ diff --git a/src/circuitizer.py b/src/circuitizer.py new file mode 100644 index 0000000..6ccaad3 --- /dev/null +++ b/src/circuitizer.py @@ -0,0 +1,273 @@ +""" +Circuitizer orchestrator module. + +This module provides a unified interface for circuitizing models of different types. +It orchestrates the circuitization process by delegating to the appropriate circuitizer implementation +based on the model type. +""" + +import os +import json +import logging +from pathlib import Path +from typing import Optional, Dict, Any + +from src.backends.ezkl import EZKL +from src.utils.utils import Utils +from src.runner import Runner + +logger = logging.getLogger(__name__) + +class Circuitizer: + """ + Orchestrator class for circuitizing models of different types. + + This class provides a unified interface for circuitizing models by delegating + to the appropriate circuitizer implementation based on the model type. + """ + + @staticmethod + def create(model_path: str) -> 'Circuitizer': + """ + Factory method to create a Circuitizer instance based on the model type. + + Args: + model_path: Path to the model file or directory + + Returns: + A Circuitizer instance + + Raises: + ValueError: If the model type is not supported + """ + # Check if the path is a file or directory + if os.path.isfile(model_path): + model_file = model_path + model_dir = os.path.dirname(model_path) + if not model_dir: # If the directory is empty (e.g., just "model.onnx") + model_dir = "." + else: + model_dir = model_path + model_file = None + + # Determine model type + is_onnx = False + + # Check if it's an ONNX model + if model_file and model_file.lower().endswith('.onnx'): + is_onnx = True + elif os.path.exists(os.path.join(model_dir, "model.onnx")): + is_onnx = True + model_file = os.path.join(model_dir, "model.onnx") + # Check if it's a directory with metadata.json (sliced model) + elif os.path.isdir(model_path) and (os.path.exists(os.path.join(model_path, "metadata.json")) or + os.path.exists(os.path.join(model_path, "slices", "metadata.json"))): + is_onnx = True + + # Create appropriate circuitizer + if is_onnx: + logger.info(f"Creating ONNX circuitizer for model: {model_path}") + return Circuitizer(EZKL()) + else: + # For now, we only support ONNX models as per requirements + # In the future, this can be extended to support other model types + raise ValueError(f"Unsupported model type at path: {model_path}") + + def __init__(self, circuitizer_impl): + """ + Initialize the Circuitizer with a specific implementation. + + Args: + circuitizer_impl: The circuitizer implementation to use + """ + self.circuitizer_impl = circuitizer_impl + + def circuitize(self, model_path: str, input_file: Optional[str] = None, layers: Optional[str] = None) -> Dict[str, Any]: + """ + Circuitize the model, deciding between whole-model or sliced-model circuitization. + + Args: + model_path: Path to the ONNX model file or a directory containing slices/metadata + input_file: Optional path to input file for calibration + layers: Optional string specifying which layers to circuitize (e.g., "3, 20-22"). + Only applicable to sliced models. + + Returns: + The path to the directory where circuitization results are saved, or metadata updates path for slices. + """ + logger.info(f"Circuitizing: {model_path}") + if not os.path.exists(model_path): + raise FileNotFoundError(f"Path does not exist: {model_path}") + + layer_indices = self._parse_layers(layers) if layers else None + if layer_indices: + logger.info(f"Will circuitize only layers with indices: {layer_indices}") + elif layers: + logger.info("No valid layer indices parsed. Will circuitize all layers.") + + if os.path.isdir(model_path) and (os.path.exists(os.path.join(model_path, "metadata.json")) or os.path.exists(os.path.join(model_path, "slices", "metadata.json"))): + return self._circuitize_slices(model_path, input_file_path=input_file, layer_indices=layer_indices) + elif os.path.isfile(model_path) and model_path.lower().endswith('.onnx'): + if layer_indices: + logger.warning("Layer selection is only supported for sliced models, not single ONNX files.") + return self._circuitize_model(model_path, input_file_path=input_file) + else: + raise ValueError(f"Invalid model path: {model_path}. Must be either a directory containing metadata.json or an .onnx file") + + @staticmethod + def _parse_layers(layers_str: Optional[str]): + if not layers_str: + return None + layer_indices = [] + parts = [p.strip() for p in layers_str.split(',')] + for part in parts: + if '-' in part: + try: + start, end = map(int, part.split('-')) + layer_indices.extend(range(start, end + 1)) + except ValueError: + logger.warning(f"Invalid layer range: {part}. Skipping.") + else: + try: + layer_indices.append(int(part)) + except ValueError: + logger.warning(f"Invalid layer index: {part}. Skipping.") + return sorted(set(layer_indices)) if layer_indices else None + + def _circuitize_model(self, model_file_path: str, input_file_path: Optional[str] = None) -> str: + if not os.path.isfile(model_file_path): + raise ValueError(f"model_path must be a file: {model_file_path}") + output_path_root = os.path.splitext(model_file_path)[0] + circuit_folder = os.path.join(os.path.dirname(output_path_root), "model") + os.makedirs(circuit_folder, exist_ok=True) + # Call backend pipeline + self.circuitizer_impl.circuitization_pipeline(model_file_path, circuit_folder, input_file_path=input_file_path) + logger.info(f"Circuitization completed. Output saved to {circuit_folder}") + return circuit_folder + + def _circuitize_slices(self, dir_path: str, input_file_path: Optional[str] = None, layer_indices=None) -> str: + if not os.path.isdir(dir_path): + raise ValueError(f"path must be a directory: {dir_path}") + # Find metadata.json + metadata_path = os.path.join(dir_path, "metadata.json") + if not os.path.exists(metadata_path): + alt = os.path.join(dir_path, "slices", "metadata.json") + if os.path.exists(alt): + metadata_path = alt + if not os.path.exists(metadata_path): + raise FileNotFoundError(f"metadata.json not found in {dir_path} or its slices subdirectory") + + # Load metadata + with open(metadata_path, 'r') as f: + metadata = json.load(f) + + segments = metadata.get('segments', []) + segment_output_path = None + circuitized_count = 0 + skipped_count = 0 + + # Phase 1: Run ONNX inference chain if we have input file + current_input = input_file_path + if current_input and os.path.exists(current_input): + logger.info("Running ONNX inference chain to generate calibration files") + for idx, segment in enumerate(segments): + segment_path = segment.get('path') + if not segment_path or not os.path.exists(segment_path): + logger.warning(f"Segment file not found for index {idx}: {segment_path}") + continue + + segment_output_path = os.path.join(os.path.dirname(segment_path), "ezkl_circuitization") + os.makedirs(segment_output_path, exist_ok=True) + + # Run ONNX inference to generate calibration data + output_tensor_path = os.path.join(segment_output_path, f"segment_{idx}_calibration.json") + logger.info(f"Running ONNX inference for segment {idx} with input file {current_input}") + success, tensor, exec_info = Runner._run_onnx_segment( + slice_info={"path": segment_path}, + input_tensor_path=Path(current_input), + output_tensor_path=Path(output_tensor_path) + ) + + if not success: + logger.error(f"ONNX inference failed for segment {idx}: {exec_info.get('error', 'Unknown error')}") + return + + current_input = output_tensor_path + logger.info(f"Generated calibration file: {output_tensor_path}") + else: + logger.warning("No input file provided, skipping ONNX inference chain") + + # Phase 2: Circuitize selected layers + for idx, segment in enumerate(segments): + if layer_indices is not None and idx not in layer_indices: + logger.info(f"Skipping circuitization for segment {idx} as it's not in the specified layers") + skipped_count += 1 + continue + + segment_path = segment.get('path') + if not segment_path or not os.path.exists(segment_path): + logger.warning(f"Segment file not found for index {idx}: {segment_path}") + continue + # Prepare concise progress information similar to slicer output + deps = segment.get('dependencies', {}) if isinstance(segment, dict) else {} + input_names = deps.get('filtered_inputs') or deps.get('input') or [] + output_names = deps.get('output') or [] + try: + logger.info(f"Circuitizing segment {idx}: {input_names} -> {output_names}") + except Exception: + logger.info(f"Circuitizing segment {idx}") + segment_output_path = os.path.join(os.path.dirname(segment_path), "ezkl_circuitization") + os.makedirs(segment_output_path, exist_ok=True) + + calibration_input = input_file_path if idx == 0 else os.path.join( + os.path.dirname(segments[idx-1].get('path')), + "ezkl_circuitization", + f"segment_{idx-1}_calibration.json" + ) + + if calibration_input and os.path.exists(calibration_input): + logger.info(f"Circuitizing segment {idx} with calibration input file {calibration_input}") + circuitization_data = self.circuitizer_impl.circuitization_pipeline( + segment_path, + segment_output_path, + input_file_path=calibration_input, + segment_details=segment + ) + segment['ezkl_circuitization'] = circuitization_data + circuitized_count += 1 + logger.info(f"Completed segment {idx}") + Utils.save_metadata_file(metadata, os.path.dirname(metadata_path), os.path.basename(metadata_path)) + + + if segment_output_path: + output_dir = os.path.dirname(segment_output_path) + else: + output_dir = os.path.dirname(metadata_path) + logger.info(f"Circuitization of slices completed. Circuitized {circuitized_count} segments, skipped {skipped_count} segments.") + logger.info(f"Output saved to {os.path.dirname(output_dir)}") + return output_dir + +if __name__ == "__main__": + # Choose which model to test + model_choice = 5 # Change this to test different models + + base_paths = { + 1: "models/doom", + 2: "models/net", + 3: "models/resnet", + 5: "models/age", + 6: "models/version" + } + abs_path = os.path.abspath(base_paths[model_choice]) + model_dir = abs_path + slices_dir = os.path.join(abs_path, "slices") + + # Circuitize via orchestrator + model_path = os.path.abspath(model_dir) + print(f"Circuitizing {model_path}") + input_file = os.path.join(model_dir, "input.json") + circuitizer = Circuitizer.create(model_path=model_path) + result_dir = circuitizer.circuitize(model_path=model_path, layers="12") + # input_file=input_file if os.path.exists(input_file) else None, + # layers="3, 4") + print(f"Circuitization finished. Output at: {result_dir}") \ No newline at end of file diff --git a/src/cli/__init__.py b/src/cli/__init__.py new file mode 100644 index 0000000..c3cc3d4 --- /dev/null +++ b/src/cli/__init__.py @@ -0,0 +1,29 @@ +""" +Dsperse CLI package. +Contains modules for the Dsperse command-line interface. +""" + +from src.cli.base import DsperseArgumentParser, print_header, print_easter_egg, configure_logging, logger +from src.cli.slice import setup_parser as setup_slice_parser, slice_model +from src.cli.run import setup_parser as setup_run_parser, run_inference +from src.cli.prove import setup_parser as setup_prove_parser, run_proof +from src.cli.verify import setup_parser as setup_verify_parser, verify_proof +from src.cli.circuitize import setup_parser as setup_circuitize_parser, circuitize_model + +__all__ = [ + 'DsperseArgumentParser', + 'print_header', + 'print_easter_egg', + 'configure_logging', + 'logger', + 'setup_slice_parser', + 'slice_model', + 'setup_run_parser', + 'run_inference', + 'setup_prove_parser', + 'run_proof', + 'setup_verify_parser', + 'verify_proof', + 'setup_circuitize_parser', + 'circuitize_model' +] diff --git a/src/cli/base.py b/src/cli/base.py new file mode 100644 index 0000000..f117c6b --- /dev/null +++ b/src/cli/base.py @@ -0,0 +1,281 @@ +""" +Base module for Dsperse CLI functionality. +Contains common utilities and classes used by all CLI commands. +""" + +import argparse +import random +import colorama +import logging +import os +from colorama import Fore, Style + +# Initialize colorama +colorama.init() + +# Configure logging +logger = logging.getLogger('dsperse') + +# --- Path normalization helpers --- + +def normalize_path(p: str) -> str: + """ + Expand ~ and environment variables, then return an absolute, normalized path. + Returns the input unchanged if falsy. + """ + if not p: + return p + try: + expanded = os.path.expanduser(os.path.expandvars(str(p))) + return os.path.normpath(os.path.abspath(expanded)) + except Exception: + return p + + +def _param_name_suggests_path(name: str) -> bool: + if not name: + return False + name = name.lower() + for token in ("path", "dir", "file", "model", "slices", "output", "input", "run"): + if token in name: + return True + return False + + +def _looks_like_path(value: str) -> bool: + if not value: + return False + v = str(value) + if v.startswith("~"): + return True + if v.startswith("/") or v.startswith("./") or v.startswith("../"): + return True + if os.sep in v or (os.altsep and os.altsep in v): + return True + if v.lower().endswith((".json", ".onnx", ".pth")): + return True + return False + + +def _maybe_normalize_from_prompt(param_name: str, prompt_message: str, value: str) -> str: + try: + if _param_name_suggests_path(param_name) or _looks_like_path(value) or ( + prompt_message and any(t in prompt_message.lower() for t in ["path", "directory", "dir", "file"]) # heuristic + ): + return normalize_path(value) + except Exception: + pass + return value + + +def configure_logging(log_level='WARNING'): + """ + Configure the logging system. + + Args: + log_level (str): The logging level to use (DEBUG, INFO, WARNING, ERROR, CRITICAL) + """ + numeric_level = getattr(logging, log_level.upper(), None) + if not isinstance(numeric_level, int): + numeric_level = logging.INFO + + # Configure the root logger + logging.basicConfig( + level=numeric_level, + format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', + datefmt='%Y-%m-%d %H:%M:%S' + ) + + # Set the level for the dsperse logger + logger.setLevel(numeric_level) + +# Easter eggs +EASTER_EGGS = [ + "Did you know? Neural networks are just spicy linear algebra!", + "Fun fact: The first neural network was created in 1943 by Warren McCulloch and Walter Pitts.", + "Pro tip: Always normalize your inputs!", + "Dsperse fact: Slicing models helps with interpretability and verification.", + "ZK fact: Zero-knowledge proofs allow you to prove you know something without revealing what it is.", + "Dsperse makes it easier to reason about model segments.", + "The answer to life, the universe, and everything is... 42 (but you need a neural network to understand why).", + "Neural networks don't actually think. They just do math really fast.", + "If you're reading this, you're awesome! Keep up the great work!", + "Dsperse: Making neural networks more transparent, one slice at a time." +] + +def print_header(): + """Print the Dsperse CLI header with ASCII art.""" + header = f""" +{Fore.CYAN} +8888888b. .d8888b. +888 "Y88b d88P Y88b +888 888 Y88b. +888 888 "Y888b. 88888b. .d88b. 888d888 .d8888b .d88b. +888 888 "Y88b. 888 "88b d8P Y8b 888P" 88K d8P Y8b +888 888 "888 888 888 88888888 888 "Y8888b. 88888888 +888 .d88P Y88b d88P 888 d88P Y8b. 888 X88 Y8b. +8888888P" "Y8888P" 88888P" "Y8888 888 88888P' "Y8888 + 888 + 888 + 888 +{Style.RESET_ALL} +{Fore.YELLOW}Distributed zkML Toolkit{Style.RESET_ALL} +""" + print(header) # Keep print for header as it's visual UI element + logger.info("Dsperse CLI started") + +def print_easter_egg(): + """Print a random easter egg.""" + easter_egg = f"\n{Fore.GREEN}🥚 {random.choice(EASTER_EGGS)}{Style.RESET_ALL}\n" + print(easter_egg) # Keep print for easter egg as it's visual UI element + logger.debug(f"Easter egg displayed: {random.choice(EASTER_EGGS)}") + +# Custom ArgumentParser that shows header and easter egg with help +class DsperseArgumentParser(argparse.ArgumentParser): + def print_help(self, file=None): + # print_header() + if random.random() < 0.2: # 20% chance to show an easter egg + print_easter_egg() + super().print_help(file) + + def add_subparsers(self, **kwargs): + # Ensure that subparsers are also DsperseArgumentParser instances + kwargs.setdefault('parser_class', DsperseArgumentParser) + return super().add_subparsers(**kwargs) + +def check_model_dir(model_dir): + """ + Check if the model directory or file exists. + + Args: + model_dir (str): Path to the model directory or file + + Returns: + bool: True if the path exists, False otherwise + """ + normalized = normalize_path(model_dir) + if not os.path.exists(normalized): + error_msg = f"Path '{model_dir}' does not exist." + print(f"{Fore.RED}Error: {error_msg}{Style.RESET_ALL}") + logger.error(error_msg) + return False + logger.debug(f"Model directory/file exists: {normalized}") + return True + +def detect_model_type(model_path): + """ + Detect the model type (ONNX or PyTorch) based on the file path or files in the directory. + + Args: + model_path (str): Path to the model file or directory + + Returns: + tuple: (is_onnx, error_message) where is_onnx is a boolean and error_message is a string or None + """ + import os + is_onnx = False + error_message = None + + # Check if the path is a file + if os.path.isfile(model_path): + # Determine model type from file extension + if model_path.lower().endswith('.onnx'): + is_onnx = True + logger.debug(f"Detected ONNX model from file extension: {model_path}") + elif model_path.lower().endswith('.pth'): + is_onnx = False + logger.debug(f"Detected PyTorch model from file extension: {model_path}") + else: + error_msg = f"Unsupported model file format. Expected .onnx or .pth file." + error_message = f"{Fore.RED}Error: {error_msg}{Style.RESET_ALL}" + logger.error(error_msg) + else: + # Check for model files in the directory + if os.path.exists(os.path.join(model_path, "model.onnx")): + is_onnx = True + logger.debug(f"Detected ONNX model in directory: {model_path}") + elif os.path.exists(os.path.join(model_path, "model.pth")): + is_onnx = False + logger.debug(f"Detected PyTorch model in directory: {model_path}") + else: + error_msg = f"No model.pth or model.onnx found in '{model_path}'." + error_message = f"{Fore.RED}Error: {error_msg}{Style.RESET_ALL}" + logger.error(error_msg) + + return is_onnx, error_message + +def save_result(result, output_file): + """ + Save the result to a file. + + Args: + result: The result to save + output_file (str): Path to the output file + """ + import json + from pathlib import Path + + def _default(o): + # Safely convert non-serializable objects + if isinstance(o, Path): + return str(o) + # Fallback to string representation + return str(o) + + with open(output_file, 'w') as f: + json.dump(result, f, indent=2, default=_default) + success_msg = f"Results saved to {output_file}" + print(f"{Fore.GREEN}✓ {success_msg}{Style.RESET_ALL}") + logger.info(success_msg) + +def prompt_for_value(param_name, prompt_message, default=None, required=True): + """ + Prompt the user for a value if it's missing. Normalizes path-like inputs. + + Args: + param_name (str): The name of the parameter + prompt_message (str): The message to display when prompting + default (str, optional): The default value to use if the user just presses enter + required (bool, optional): Whether the parameter is required + + Returns: + str: The value provided by the user or the default value + """ + try: + logger.debug(f"Prompting for {param_name} with message: {prompt_message}") + if default is not None: + user_input = input(f"{Fore.YELLOW}{prompt_message} [{default}]: {Style.RESET_ALL}") + if not user_input.strip(): + normalized_default = _maybe_normalize_from_prompt(param_name, prompt_message, str(default)) + logger.debug(f"Using default value for {param_name}: {normalized_default}") + return normalized_default + value = user_input.strip() + value = _maybe_normalize_from_prompt(param_name, prompt_message, value) + logger.debug(f"User provided value for {param_name}: {value}") + return value + else: + while True: + user_input = input(f"{Fore.YELLOW}{prompt_message}: {Style.RESET_ALL}") + if user_input.strip() or not required: + if user_input.strip(): + value = user_input.strip() + value = _maybe_normalize_from_prompt(param_name, prompt_message, value) + logger.debug(f"User provided value for {param_name}: {value}") + return value + else: + logger.debug(f"Empty value provided for non-required parameter {param_name}") + return user_input.strip() + error_msg = f"{param_name} is required." + print(f"{Fore.RED}Error: {error_msg}{Style.RESET_ALL}") + logger.warning(error_msg) + except KeyboardInterrupt: + cancel_msg = "Operation cancelled by user." + print(f"\n{Fore.YELLOW}{cancel_msg}{Style.RESET_ALL}") + logger.info(cancel_msg) + import sys + sys.exit(1) + except Exception as e: + error_msg = f"Error getting input: {e}" + print(f"{Fore.RED}{error_msg}{Style.RESET_ALL}") + logger.error(error_msg) + return default if not required else None diff --git a/src/cli/circuitize.py b/src/cli/circuitize.py new file mode 100644 index 0000000..2952706 --- /dev/null +++ b/src/cli/circuitize.py @@ -0,0 +1,200 @@ +""" +CLI module for circuitizing models using EZKL. +""" + +import traceback +import os +import json +import logging + +from colorama import Fore, Style + +from src.circuitizer import Circuitizer +from src.cli.base import check_model_dir, prompt_for_value, logger, normalize_path + + +def _check_layers(slices_path, layers_str): + """ + Check if the layers provided exist in the metadata.json file within the slices directory. + + Args: + slices_path (str): Path to the slices directory + layers_str (str): String specifying which layers to circuitize (e.g., "3, 20-22") + + Returns: + str: Validated layers string with only existing layers + """ + if not layers_str: + return None + + # Parse the layers string into a list of indices + layer_indices = [] + parts = [p.strip() for p in layers_str.split(',')] + + for part in parts: + if '-' in part: + # Handle range (e.g., "20-22") + try: + start, end = map(int, part.split('-')) + layer_indices.extend(range(start, end + 1)) + except ValueError: + logger.warning(f"Invalid layer range: {part}. Skipping.") + print(f"{Fore.YELLOW}Warning: Invalid layer range: {part}. Skipping.{Style.RESET_ALL}") + else: + # Handle single number + try: + layer_indices.append(int(part)) + except ValueError: + logger.warning(f"Invalid layer index: {part}. Skipping.") + print(f"{Fore.YELLOW}Warning: Invalid layer index: {part}. Skipping.{Style.RESET_ALL}") + + # Remove duplicates and sort + layer_indices = sorted(set(layer_indices)) + + # Find metadata.json file + metadata_path = os.path.join(slices_path, "metadata.json") + if not os.path.exists(metadata_path): + # Check for metadata.json in slices subdirectory + metadata_path = os.path.join(slices_path, "slices", "metadata.json") + if not os.path.exists(metadata_path): + logger.warning(f"metadata.json not found in {slices_path} or {os.path.join(slices_path, 'slices')}") + print(f"{Fore.YELLOW}Warning: metadata.json not found. Cannot validate layers.{Style.RESET_ALL}") + return layers_str + + # Load metadata + try: + with open(metadata_path, 'r') as f: + metadata = json.load(f) + except Exception as e: + logger.warning(f"Failed to load metadata.json: {e}") + print(f"{Fore.YELLOW}Warning: Failed to load metadata.json. Cannot validate layers.{Style.RESET_ALL}") + return layers_str + + # Get available segments + segments = metadata.get('segments', []) + available_indices = [segment.get('index') for segment in segments] + + # Check if each layer exists + valid_indices = [] + for idx in layer_indices: + if idx in available_indices: + valid_indices.append(idx) + else: + logger.warning(f"Layer {idx} not found in metadata.json, skipping circuitization of it") + print(f"{Fore.YELLOW}Warning: Layer {idx} not found, skipping circuitization of it{Style.RESET_ALL}") + + # If no valid indices, return None + if not valid_indices: + logger.warning("No valid layers found") + print(f"{Fore.YELLOW}Warning: No valid layers found. Will circuitize all layers.{Style.RESET_ALL}") + return None + + # Convert valid indices back to a string + # For simplicity, we'll just use comma-separated values + return ','.join(map(str, valid_indices)) + + +def setup_parser(subparsers): + """ + Set up the argument parser for the circuitize command. + + Args: + subparsers: The subparsers object from argparse + + Returns: + The created parser + """ + circuitize_parser = subparsers.add_parser('circuitize', help='Circuitize slices using EZKL') + circuitize_parser.add_argument('--slices-path', help='Path to the slices directory') + circuitize_parser.add_argument('--input-file', help='Path to input file for calibration (optional)') + circuitize_parser.add_argument('--layers', help='Specify which layers to circuitize (e.g., "3, 20-22"). If not provided, all layers will be circuitized.') + + return circuitize_parser + + +def circuitize_model(args): + """ + Circuitize a model based on the provided arguments. + + Args: + args: The parsed command-line arguments + """ + print(f"{Fore.CYAN}Circuitizing slices with EZKL...{Style.RESET_ALL}") + logger.info("Starting slices circuitization") + + # Prompt for slices path if not provided + if not hasattr(args, 'slices_path') or not args.slices_path: + args.slices_path = prompt_for_value('slices-path', 'Enter the path to the slices directory') + else: + args.slices_path = normalize_path(args.slices_path) + + if not check_model_dir(args.slices_path): + return + + # Normalize input file if provided via flag + if hasattr(args, 'input_file') and args.input_file: + args.input_file = normalize_path(args.input_file) + + # Ensure the provided path looks like a slices directory; otherwise guide user to slice first. + try: + if not os.path.isdir(args.slices_path): + msg = ( + "Please provide a slices directory, not a model file. " + "If you have a model, slice it first before circuitizing.\n" + f"Try: dsperse slice --model-dir {args.slices_path}" + ) + print(f"{Fore.YELLOW}Warning: {msg}{Style.RESET_ALL}") + logger.error("Circuitize requires a slices directory (with metadata.json).") + return + has_metadata = ( + os.path.exists(os.path.join(args.slices_path, "metadata.json")) or + os.path.exists(os.path.join(args.slices_path, "slices", "metadata.json")) + ) + if not has_metadata: + msg = ( + "No slices metadata found at the provided path. " + "Please slice the model first before circuitizing slices.\n" + f"Try: dsperse slice --model-dir {args.slices_path}" + ) + print(f"{Fore.YELLOW}Warning: {msg}{Style.RESET_ALL}") + logger.error("Circuitize requires slices metadata. Prompted user to run slice first.") + return + # Initialize the Circuitizer + circuitizer = Circuitizer.create(args.slices_path) + logger.info(f"Circuitizer initialized successfully") + except RuntimeError as e: + error_msg = f"Failed to initialize Circuitizer: {e}" + print(f"{Fore.RED}Error: {error_msg}{Style.RESET_ALL}") + logger.error(error_msg) + return + + # Check if the layers exist in the metadata + if hasattr(args, 'layers') and args.layers: + validated_layers = _check_layers(args.slices_path, args.layers) + else: + validated_layers = None + + # Run the circuitization + ezkl_logger = logging.getLogger('src.backends.ezkl') + prev_ezkl_level = ezkl_logger.level + try: + # Suppress verbose EZKL INFO logs during circuitization + ezkl_logger.setLevel(logging.WARNING) + + output_path = circuitizer.circuitize( + model_path=args.slices_path, + input_file=args.input_file, + layers=validated_layers + ) + success_msg = f"Slices circuitized successfully! Output saved to {os.path.dirname(output_path)}" + print(f"{Fore.GREEN}✓ {success_msg}{Style.RESET_ALL}") + logger.info(success_msg) + except Exception as e: + error_msg = f"Error circuitizing slices: {e}" + print(f"{Fore.RED}Error: {error_msg}{Style.RESET_ALL}") + logger.error(error_msg) + logger.debug("Stack trace:", exc_info=True) + traceback.print_exc() + finally: + # Restore previous EZKL logger level + ezkl_logger.setLevel(prev_ezkl_level) \ No newline at end of file diff --git a/src/cli/prove.py b/src/cli/prove.py new file mode 100644 index 0000000..b7ead7f --- /dev/null +++ b/src/cli/prove.py @@ -0,0 +1,190 @@ +""" +CLI module for generating proofs for models. +""" + +import os +import time +import traceback +import glob +from colorama import Fore, Style + +from src.prover import Prover +from src.cli.base import save_result, prompt_for_value, normalize_path + +def setup_parser(subparsers): + """ + Set up the argument parser for the prove command. + + Args: + subparsers: The subparsers object from argparse + + Returns: + The created parser + """ + prove_parser = subparsers.add_parser('prove', help='Generate a proof for a run') + prove_parser.add_argument('--run-dir', help='Specific run directory to prove') + prove_parser.add_argument('--output-file', help='Path to save output results') + + return prove_parser + +def get_all_runs(run_root_dir): + """ + Get all run directories in the provided runs root directory. + + Args: + run_root_dir (str): Path to the runs root directory (contains metadata.json and run_* subdirs) + + Returns: + list: List of run directories, sorted by name (latest last) + """ + if not os.path.exists(run_root_dir): + return [] + + # Get all run directories sorted by name (which includes timestamp) + run_dirs = sorted(glob.glob(os.path.join(run_root_dir, "run_*"))) + + return run_dirs + +def get_latest_run(run_root_dir): + """ + Get the latest run directory in the provided runs root directory. + + Args: + run_root_dir (str): Path to the runs root directory + + Returns: + str: Path to the latest run directory, or None if no runs found + """ + run_dirs = get_all_runs(run_root_dir) + + if not run_dirs: + return None + + # Return the latest run directory + return run_dirs[-1] + +def run_proof(args): + """ + Generate a proof based on a provided runs root directory or a specific run directory. + + Args: + args: The parsed command-line arguments + """ + print(f"{Fore.CYAN}Generating proof...{Style.RESET_ALL}") + + run_root_dir = None + run_dir = None + + # Helper predicates + def is_run_id_dir(p): + return os.path.exists(os.path.join(p, "run_result.json")) + + def is_run_root_dir(p): + return os.path.exists(os.path.join(p, "metadata.json")) + + # Determine input + if hasattr(args, 'run_dir') and args.run_dir: + candidate = normalize_path(args.run_dir) + else: + candidate = prompt_for_value('run-or-run-id-dir', 'Enter run directory (runs root or a run_* directory)') + + # Ensure candidate is normalized in case prompt returned a path-like + candidate = normalize_path(candidate) + + if not os.path.exists(candidate): + print(f"{Fore.RED}Error: Path {candidate} does not exist{Style.RESET_ALL}") + return + + if is_run_id_dir(candidate): + # Specific run directory selected + run_dir = candidate + run_root_dir = os.path.dirname(candidate) + elif is_run_root_dir(candidate): + # Runs root provided; let user choose run + run_root_dir = candidate + all_runs = get_all_runs(run_root_dir) + if not all_runs: + print(f"{Fore.RED}Error: No runs found in {run_root_dir}{Style.RESET_ALL}") + return + run_names = [os.path.basename(p) for p in all_runs] + default_run = run_names[-1] + run_list = ", ".join(run_names) + print(f"We found {len(all_runs)} runs, {run_list}, enter which run you would like to prove (default {default_run}):") + user_input = input().strip() + if not user_input: + run_dir = all_runs[-1] + else: + try: + idx = int(user_input) - 1 + if 0 <= idx < len(all_runs): + run_dir = all_runs[idx] + else: + print(f"{Fore.RED}Error: Invalid run index{Style.RESET_ALL}") + return + except ValueError: + candidate_run = normalize_path(os.path.join(run_root_dir, user_input)) + if os.path.exists(candidate_run) and is_run_id_dir(candidate_run): + run_dir = candidate_run + else: + print(f"{Fore.RED}Error: Run directory {candidate_run} does not exist or is invalid{Style.RESET_ALL}") + return + else: + # Not a valid runs root or run directory + print(f"{Fore.RED}Error: Provided path is neither a runs root (metadata.json) nor a run directory (run_result.json){Style.RESET_ALL}") + return + + # Validate resolved paths + run_dir = normalize_path(run_dir) + run_root_dir = normalize_path(run_root_dir) + run_result_path = os.path.join(run_dir, "run_result.json") + if not os.path.exists(run_result_path): + print(f"{Fore.RED}Error: run_result.json not found in {run_dir}{Style.RESET_ALL}") + return + + metadata_path = os.path.join(run_root_dir, "metadata.json") + if not os.path.exists(metadata_path): + print(f"{Fore.RED}Error: metadata.json not found in {run_root_dir}{Style.RESET_ALL}") + return + + # Print proving message + print("proving...") + + try: + prover = Prover() + start_time = time.time() + result = prover.prove_run(run_result_path, metadata_path) + elapsed_time = time.time() - start_time + + print(f"{Fore.GREEN}✓ Proof generation completed in {elapsed_time:.2f} seconds!{Style.RESET_ALL}") + print("\nDone!") + + # Prompt for output file if not provided + if not hasattr(args, 'output_file') or not args.output_file: + save_output = prompt_for_value('save-output', 'Save proof results to separate file?', default='n', required=False).lower() + if save_output.startswith('y'): + default_output_file = os.path.join(run_root_dir, "proof_results.json") + args.output_file = prompt_for_value('output-file', 'Enter the output file path', default=default_output_file, required=False) + + # Save the result if output file is specified + if args.output_file: + try: + args.output_file = normalize_path(args.output_file) + save_result(result, args.output_file) + print(f"{Fore.GREEN}Results saved to {args.output_file}{Style.RESET_ALL}") + except Exception as e: + print(f"{Fore.RED}Error saving output file: {e}{Style.RESET_ALL}") + + # Print the proof generation summary + if "execution_chain" in result: + execution_chain = result["execution_chain"] + print(f"\n{Fore.YELLOW}Proof Generation Summary:{Style.RESET_ALL}") + print(f"Proved segments: {execution_chain.get('ezkl_proved_slices', 0)} of {execution_chain.get('ezkl_witness_slices', 0)}") + if execution_chain.get('ezkl_witness_slices', 0) > 0: + proof_percentage = (execution_chain.get('ezkl_proved_slices', 0) / execution_chain.get('ezkl_witness_slices', 0)) * 100 + print(f"Proof generation percentage: {proof_percentage:.1f}%") + else: + print(f"\n{Fore.YELLOW}No proof generation results found{Style.RESET_ALL}") + + except Exception as e: + print(f"{Fore.RED}Error proving run: {e}{Style.RESET_ALL}") + traceback.print_exc() diff --git a/src/cli/run.py b/src/cli/run.py new file mode 100644 index 0000000..e358066 --- /dev/null +++ b/src/cli/run.py @@ -0,0 +1,160 @@ +""" +CLI module for running inference on models. +""" + +import os +import time +import traceback + +from colorama import Fore, Style + +from src.cli.base import check_model_dir, save_result, prompt_for_value, logger, normalize_path +from src.runner import Runner + + +def setup_parser(subparsers): + """ + Set up the argument parser for the run command. + + Args: + subparsers: The subparsers object from argparse + + Returns: + The created parser + """ + run_parser = subparsers.add_parser('run', help='Run inference on a model') + run_parser.add_argument('--slices-dir', help='Directory containing the slices') + run_parser.add_argument('--run-metadata-path', help='Path to run metadata.json (auto-generated if not provided)') + run_parser.add_argument('--input-file', help='Path to input file (default: parent_of_slices/input.json)') + run_parser.add_argument('--output-file', help='Path to save output results (default: parent_of_slices/output.json)') + + return run_parser + +def run_inference(args): + """ + Run inference on a model based on the provided arguments. + + This command requires a slices directory. The parent directory of the slices + is treated as the model directory, which is used for defaults like input/output paths. + + Args: + args: The parsed command-line arguments + """ + print(f"{Fore.CYAN}Running inference...{Style.RESET_ALL}") + logger.info("Starting model inference") + + # Require slices directory + if not hasattr(args, 'slices_dir') or not args.slices_dir: + args.slices_dir = prompt_for_value('slices-dir', 'Enter the slices directory') + else: + args.slices_dir = normalize_path(args.slices_dir) + + if not check_model_dir(args.slices_dir): + return + + # Validate slices directory has metadata and normalize to the actual slices directory + meta_in_dir = os.path.exists(os.path.join(args.slices_dir, 'metadata.json')) + meta_in_sub = os.path.exists(os.path.join(args.slices_dir, 'slices', 'metadata.json')) + + if not (meta_in_dir or meta_in_sub): + print(f"{Fore.YELLOW}Warning: No slices metadata found at the provided path. Please slice the model first.{Style.RESET_ALL}") + logger.error("Run requires a valid slices directory with metadata.json") + return + + if meta_in_dir: + slices_dir_effective = args.slices_dir + model_dir = os.path.dirname(args.slices_dir.rstrip('/')) or '.' + else: + # metadata inside a 'slices' subfolder; treat provided path as model_dir + slices_dir_effective = os.path.join(args.slices_dir, 'slices') + model_dir = args.slices_dir + + # Normalize derived paths + slices_dir_effective = normalize_path(slices_dir_effective) + model_dir = normalize_path(model_dir) + + # Get run metadata path if provided, otherwise None (Runner will auto-generate) + run_metadata_path = args.run_metadata_path if hasattr(args, 'run_metadata_path') and args.run_metadata_path else None + if run_metadata_path: + run_metadata_path = normalize_path(run_metadata_path) + + # Prompt for input file if not provided + if not hasattr(args, 'input_file') or not args.input_file: + # Set default input file path based on model_dir (parent of slices) + default_input_file = os.path.join(model_dir, "input.json") + args.input_file = prompt_for_value('input-file', 'Enter the input file path', default=default_input_file, required=True) + + # Check if input file exists + if args.input_file: + args.input_file = normalize_path(args.input_file) + if args.input_file and not os.path.exists(args.input_file): + print(f"{Fore.YELLOW}Warning: Input file '{args.input_file}' does not exist.{Style.RESET_ALL}") + retry_option = prompt_for_value('retry-option', 'Enter a different file path or "q" to quit', required=False).lower() + if retry_option == 'q': + print(f"{Fore.YELLOW}Operation cancelled by user.{Style.RESET_ALL}") + logger.info("Operation cancelled by user") + return + elif retry_option: + if os.path.exists(retry_option): + args.input_file = retry_option + print(f"{Fore.GREEN}Using input file: {args.input_file}{Style.RESET_ALL}") + logger.info(f"Using input file: {args.input_file}") + else: + print(f"{Fore.RED}Error: File '{retry_option}' does not exist. Aborting.{Style.RESET_ALL}") + logger.error(f"File '{retry_option}' does not exist") + return + else: + args.input_file = None + + # Enforce input file requirement + if not args.input_file: + print(f"{Fore.RED}Error: input-file is required and must exist. Aborting.{Style.RESET_ALL}") + logger.error("Input file missing; aborting run.") + return + + try: + # Use the Runner class for inference + logger.info("Using Runner class for model inference") + logger.info(f"Model path: {model_dir}, Slices path: {slices_dir_effective}") + + start_time = time.time() + runner = Runner( + model_path=model_dir, + slices_path=slices_dir_effective, + run_metadata_path=run_metadata_path + ) + result = runner.run(args.input_file) + elapsed_time = time.time() - start_time + + print(f"{Fore.GREEN}✓ Inference completed in {elapsed_time:.2f} seconds!{Style.RESET_ALL}") + logger.info(f"Inference completed in {elapsed_time:.2f} seconds") + + # Prompt for output file if not provided + if not hasattr(args, 'output_file') or not args.output_file: + save_output = prompt_for_value('save-output', 'Save output to file?', default='y', required=False).lower() + if save_output.startswith('y'): + default_output_file = os.path.join(model_dir, "output.json") + args.output_file = prompt_for_value('output-file', 'Enter the output file path', default=default_output_file, required=False) + + # Save the result if an output file is specified + if args.output_file: + try: + args.output_file = normalize_path(args.output_file) + save_result(result, args.output_file) + logger.info(f"Results saved to {args.output_file}") + except Exception as e: + error_msg = f"Error saving output file: {e}" + print(f"{Fore.RED}{error_msg}{Style.RESET_ALL}") + logger.error(error_msg) + + # Print the result + print(f"\n{Fore.YELLOW}Results:{Style.RESET_ALL}") + print(f"Prediction: {result.get('prediction', 'N/A')}") + print(f"Probabilities: {result.get('probabilities', 'N/A')}") + + except Exception as e: + error_msg = f"Error during inference: {e}" + print(f"{Fore.RED}{error_msg}{Style.RESET_ALL}") + logger.error(error_msg) + logger.debug("Stack trace:", exc_info=True) + traceback.print_exc() diff --git a/src/cli/slice.py b/src/cli/slice.py new file mode 100644 index 0000000..40d0ee5 --- /dev/null +++ b/src/cli/slice.py @@ -0,0 +1,118 @@ +""" +CLI module for slicing models. +""" + +import os +import traceback + +from colorama import Fore, Style + +from src.cli.base import check_model_dir, prompt_for_value, logger, normalize_path +from src.slicer import Slicer + + +def setup_parser(subparsers): + """ + Set up the argument parser for the slice command. + + Args: + subparsers: The subparsers object from argparse + + Returns: + The created parser + """ + slice_parser = subparsers.add_parser('slice', help='Slice a model into segments') + slice_parser.add_argument('--model-dir', help='Path to the model file or directory containing the model') + slice_parser.add_argument('--output-dir', help='Directory to save the sliced model (default: model_dir/slices)') + slice_parser.add_argument('--save-file', nargs='?', const='default', help='(Optional) Save path of the model analysis (default: model_dir/analysis/model_metadata.json)') + + return slice_parser + +def slice_model(args): + """ + Slice a model based on the provided arguments. + + Args: + args: The parsed command-line arguments + """ + print(f"{Fore.CYAN}Slicing model...{Style.RESET_ALL}") + logger.info("Starting model slicing") + + # Prompt for model path if not provided + if not hasattr(args, 'model_dir') or not args.model_dir: + args.model_dir = prompt_for_value('model-dir', 'Enter the path to the model file or directory') + else: + args.model_dir = normalize_path(args.model_dir) + + if not check_model_dir(args.model_dir): + return + + # Check if the provided path is a file or directory + model_dir = args.model_dir + model_file = None + + # If the path is a file, extract the directory and filename + if os.path.isfile(model_dir): + model_file = model_dir + model_dir = os.path.dirname(model_dir) + if not model_dir: # If the directory is empty (e.g., just "model.onnx") + model_dir = "." + print(f"{Fore.YELLOW}Using model file: {model_file}{Style.RESET_ALL}") + logger.info(f"Using model file: {model_file}") + + # Prompt for output directory if not provided + if not hasattr(args, 'output_dir') or not args.output_dir: + default_output_dir = os.path.join(model_dir, "slices") + args.output_dir = prompt_for_value('output-dir', 'Enter the output directory', default=default_output_dir, required=False) + else: + args.output_dir = normalize_path(args.output_dir) + + # Create output directory if specified + output_dir = args.output_dir + if output_dir: + try: + os.makedirs(output_dir, exist_ok=True) + success_msg = f"Output directory created: {output_dir}" + print(f"{Fore.GREEN}{success_msg}{Style.RESET_ALL}") + logger.info(success_msg) + except Exception as e: + error_msg = f"Error creating output directory: {e}" + print(f"{Fore.RED}{error_msg}{Style.RESET_ALL}") + logger.error(error_msg) + return + + if args.save_file == 'default': + # Flag included, no value provided + save_path = os.path.join(model_dir, "analysis", "model_metadata.json") + save_path = normalize_path(save_path) + else: + # Use the provided value or None (if no flag was provided) + save_path = normalize_path(args.save_file) if args.save_file else None + + try: + # Slice ONNX model + if model_file and model_file.lower().endswith('.onnx'): + onnx_path = model_file + else: + onnx_path = os.path.join(model_dir, "model.onnx") + + if not os.path.exists(onnx_path): + error_msg = f"ONNX model file not found at the specified path '{onnx_path}'." + print(f"{Fore.RED}Error: {error_msg}{Style.RESET_ALL}") + logger.error(error_msg) + return + + logger.info(f"Creating slicer for model: {onnx_path}") + slicer = Slicer.create(onnx_path, save_path) + logger.info(f"Slicing ONNX model to output path: {output_dir}") + slicer.slice_model(output_path=output_dir) + success_msg = "ONNX model sliced successfully!" + print(f"{Fore.GREEN}✓ {success_msg}{Style.RESET_ALL}") + logger.info(success_msg) + + except Exception as e: + error_msg = f"Error slicing model: {e}" + print(f"{Fore.RED}{error_msg}{Style.RESET_ALL}") + logger.error(error_msg) + logger.debug("Stack trace:", exc_info=True) + traceback.print_exc() diff --git a/src/cli/verify.py b/src/cli/verify.py new file mode 100644 index 0000000..a2f6a05 --- /dev/null +++ b/src/cli/verify.py @@ -0,0 +1,181 @@ +""" +CLI module for verifying proofs for models. +""" + +import os +import time +import traceback +import glob +from colorama import Fore, Style + +from src.verifier import Verifier +from src.cli.base import save_result, prompt_for_value, normalize_path + +def setup_parser(subparsers): + """ + Set up the argument parser for the verify command. + + Args: + subparsers: The subparsers object from argparse + + Returns: + The created parser + """ + verify_parser = subparsers.add_parser('verify', help='Verify a proof for a run') + verify_parser.add_argument('--run-dir', help='Specific run directory to verify') + verify_parser.add_argument('--output-file', help='Path to save output results') + + return verify_parser + +def get_all_runs(run_root_dir): + """ + Get all run directories in the provided runs root directory. + + Args: + run_root_dir (str): Path to the runs root directory (contains metadata.json and run_* subdirs) + + Returns: + list: List of run directories, sorted by name (latest last) + """ + if not os.path.exists(run_root_dir): + return [] + + # Get all run directories sorted by name (which includes timestamp) + run_dirs = sorted(glob.glob(os.path.join(run_root_dir, "run_*"))) + + return run_dirs + +def get_latest_run(run_root_dir): + """ + Get the latest run directory in the provided runs root directory. + + Args: + run_root_dir (str): Path to the runs root directory + + Returns: + str: Path to the latest run directory, or None if no runs found + """ + run_dirs = get_all_runs(run_root_dir) + + if not run_dirs: + return None + + # Return the latest run directory + return run_dirs[-1] + +def verify_proof(args): + """ + Verify a proof based on a provided runs root directory or a specific run directory. + + Args: + args: The parsed command-line arguments + """ + print(f"{Fore.CYAN}Verifying proof...{Style.RESET_ALL}") + + run_root_dir = None + run_dir = None + + def is_run_id_dir(p): + return os.path.exists(os.path.join(p, "run_result.json")) + + def is_run_root_dir(p): + return os.path.exists(os.path.join(p, "metadata.json")) + + # Determine input + if hasattr(args, 'run_dir') and args.run_dir: + candidate = normalize_path(args.run_dir) + else: + candidate = prompt_for_value('run-or-run-id-dir', 'Enter run directory (runs root or a run_* directory)') + + # Normalize candidate from prompt + candidate = normalize_path(candidate) + + if not os.path.exists(candidate): + print(f"{Fore.RED}Error: Path {candidate} does not exist{Style.RESET_ALL}") + return + + if is_run_id_dir(candidate): + run_dir = candidate + run_root_dir = os.path.dirname(candidate) + elif is_run_root_dir(candidate): + run_root_dir = candidate + all_runs = get_all_runs(run_root_dir) + if not all_runs: + print(f"{Fore.RED}Error: No runs found in {run_root_dir}{Style.RESET_ALL}") + return + run_names = [os.path.basename(p) for p in all_runs] + default_run = run_names[-1] + run_list = ", ".join(run_names) + print(f"We found {len(all_runs)} runs, {run_list}, enter which run you would like to verify (default {default_run})") + user_input = input().strip() + if not user_input: + run_dir = all_runs[-1] + else: + try: + idx = int(user_input) - 1 + if 0 <= idx < len(all_runs): + run_dir = all_runs[idx] + else: + print(f"{Fore.RED}Error: Invalid run index{Style.RESET_ALL}") + return + except ValueError: + candidate_run = normalize_path(os.path.join(run_root_dir, user_input)) + if os.path.exists(candidate_run) and is_run_id_dir(candidate_run): + run_dir = candidate_run + else: + print(f"{Fore.RED}Error: Run directory {candidate_run} does not exist or is invalid{Style.RESET_ALL}") + return + else: + print(f"{Fore.RED}Error: Provided path is neither a runs root (metadata.json) nor a run directory (run_result.json){Style.RESET_ALL}") + return + + # Validate + run_dir = normalize_path(run_dir) + run_root_dir = normalize_path(run_root_dir) + run_result_path = os.path.join(run_dir, "run_result.json") + if not os.path.exists(run_result_path): + print(f"{Fore.RED}Error: run_result.json not found in {run_dir}{Style.RESET_ALL}") + return + + metadata_path = os.path.join(run_root_dir, "metadata.json") + if not os.path.exists(metadata_path): + print(f"{Fore.RED}Error: metadata.json not found in {run_root_dir}{Style.RESET_ALL}") + return + + print("verifying...") + + try: + verifier = Verifier() + start_time = time.time() + result = verifier.verify_run(run_result_path, metadata_path) + elapsed_time = time.time() - start_time + + print(f"{Fore.GREEN}✓ Verification completed in {elapsed_time:.2f} seconds!{Style.RESET_ALL}") + print("\nDone!") + + if not hasattr(args, 'output_file') or not args.output_file: + save_output = prompt_for_value('save-output', 'Save verification results to separate file?', default='n', required=False).lower() + if save_output.startswith('y'): + default_output_file = os.path.join(run_root_dir, "verification_results.json") + args.output_file = prompt_for_value('output-file', 'Enter the output file path', default=default_output_file, required=False) + + if args.output_file: + try: + args.output_file = normalize_path(args.output_file) + save_result(result, args.output_file) + print(f"{Fore.GREEN}Results saved to {args.output_file}{Style.RESET_ALL}") + except Exception as e: + print(f"{Fore.RED}Error saving output file: {e}{Style.RESET_ALL}") + + if "execution_chain" in result: + execution_chain = result["execution_chain"] + print(f"\n{Fore.YELLOW}Verification Summary:{Style.RESET_ALL}") + print(f"Verified segments: {execution_chain.get('ezkl_verified_slices', 0)} of {execution_chain.get('ezkl_proved_slices', 0)}") + denom = execution_chain.get('ezkl_proved_slices', 0) or 1 + print(f"Verification percentage: {(execution_chain.get('ezkl_verified_slices', 0) / denom * 100):.1f}%") + else: + print(f"\n{Fore.YELLOW}No verification results found{Style.RESET_ALL}") + + except Exception as e: + print(f"{Fore.RED}Error verifying run: {e}{Style.RESET_ALL}") + traceback.print_exc() \ No newline at end of file diff --git a/src/model_circuitizer.py b/src/model_circuitizer.py deleted file mode 100644 index 8c7b73f..0000000 --- a/src/model_circuitizer.py +++ /dev/null @@ -1,544 +0,0 @@ -import json -import os -import subprocess - -import torch - -from models.doom.model import DoomAgent, Conv1Segment, Conv2Segment, Conv3Segment, FC1Segment, FC2Segment -from models.net.model import Net, Conv1Segment as NetConv1, Conv2Segment as NetConv2, FC1Segment as NetFC1, \ - FC2Segment as NetFC2, FC3Segment as NetFC3 - - -class ModelCircuitizer: - def __init__(self, model_directory: str = None, input_file_path: str = None, model_path: str = None,): - self.model_path = model_path - self.input_file_path = input_file_path - self.model_dir = model_directory - self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - self.env = os.environ - - - def circuitize_net_model(self, model_directory=None, input_file_path=None, model_path=None): - """Circuitize an unsliced model.""" - self.model_dir = model_directory if model_directory else self.model_dir - self.input_file_path = input_file_path if input_file_path else os.path.join(self.model_dir, "input.json") - self.model_path = model_path if model_path else os.path.join(self.model_dir, "model.pth") - - # TODO: find dummy input params from model --> Make the model instantiation + input shape dynamic - dummy_input = torch.randn(1, 3, 32, 32, device=self.device) - model = Net().to(self.device) - checkpoint = torch.load(self.model_path, map_location=self.device) - # model.load_state_dict(checkpoint["model_state_dict"]) - - # TODO: verify if this is ok -> to load the whole checkpoint as state dict - if "model_state_dict" in checkpoint: - model.load_state_dict(checkpoint["model_state_dict"]) - else: - model.load_state_dict(checkpoint) - - # generate onnx - circuit_folder = os.path.join(model_dir, "ezkl", "model") - os.makedirs(circuit_folder, exist_ok=True) - torch.onnx.export( - model, - dummy_input, - os.path.join(circuit_folder, "network.onnx"), - input_names=["input"], - output_names=["output"], - dynamic_axes={"input": {0: "batch_size"}, "output": {0: "batch_size"}}, - opset_version=18, - ) - print("Model exported to network.onnx") - - # generate settings.json - subprocess.run( - [ - "ezkl", - "gen-settings", - "--param-visibility", - "fixed", - "--input-visibility", - "public", - "--model", - os.path.join(circuit_folder, "network.onnx"), - "--settings-path", - os.path.join(circuit_folder, "settings.json"), - ], - env=self.env, - check=True, - ) - - # generate calibration.json - use input.json for this - input_data = os.path.join(self.model_dir, "input.json") - subprocess.run( - [ - "ezkl", - "calibrate-settings", - "--model", - os.path.join(circuit_folder, "network.onnx"), - "--settings-path", - os.path.join(circuit_folder, "settings.json"), - "--data", - os.path.join(input_data), - "--target", "accuracy" - ], - env=self.env, - check=True, - ) - - # generate model.compiled - subprocess.run( - [ - "ezkl", - "compile-circuit", - "--model", - os.path.join(circuit_folder, "network.onnx"), - "--settings-path", - os.path.join(circuit_folder, "settings.json"), - "--compiled-circuit", - os.path.join(circuit_folder, "model.compiled"), - ], - env=self.env, - check=True - ) - - # generate pk and vk - subprocess.run( - [ - "ezkl", - "setup", - "--compiled-circuit", - os.path.join(circuit_folder, "model.compiled"), - "--vk-path", - os.path.join(circuit_folder, "vk.key"), - "--pk-path", - os.path.join(circuit_folder, "pk.key") - ], - env=self.env, - check=True - ) - - def circuitize_doom_model(self): - """Circuitize a model.""" - dummy_input = torch.randn(1, 4, 28, 28, device=self.device) - model = DoomAgent().to(self.device) - checkpoint = torch.load("models/doom/model.pth", map_location=self.device) - model.load_state_dict(checkpoint["model_state_dict"]) - - # generate onnx - circuit_folder = "models/doom/ezkl/model" - os.makedirs(circuit_folder, exist_ok=True) - torch.onnx.export( - model, - dummy_input, - os.path.join(circuit_folder, "network.onnx"), - input_names=["input"], - output_names=["output"], - dynamic_axes={"input": {0: "batch_size"}, "output": {0: "batch_size"}}, - opset_version=18, - ) - print("Model exported to network.onnx") - - # generate settings.json - subprocess.run( - [ - "ezkl", - "gen-settings", - "--param-visibility", - "fixed", - "--input-visibility", - "public", - "--model", - os.path.join(circuit_folder, "network.onnx"), - "--settings-path", - os.path.join(circuit_folder, "settings.json"), - ], - env=self.env, - check=True, - ) - - # generate calibration.json - use input.json for this - input_data = os.path.join(self.model_dir, "input.json") - subprocess.run( - [ - "ezkl", - "calibrate-settings", - "--model", - os.path.join(circuit_folder, "network.onnx"), - "--settings-path", - os.path.join(circuit_folder, "settings.json"), - "--data", - os.path.join(input_data), - "--target", "accuracy" - ], - env=self.env, - check=True, - ) - - # generate model.compiled - subprocess.run( - [ - "ezkl", - "compile-circuit", - "--model", - os.path.join(circuit_folder, "network.onnx"), - "--settings-path", - os.path.join(circuit_folder, "settings.json"), - "--compiled-circuit", - os.path.join(circuit_folder, "model.compiled"), - ], - env=self.env, - check=True - ) - - # generate pk and vk - subprocess.run( - [ - "ezkl", - "setup", - "--compiled-circuit", - os.path.join(circuit_folder, "model.compiled"), - "--vk-path", - os.path.join(circuit_folder, "vk.key"), - "--pk-path", - os.path.join(circuit_folder, "pk.key") - ], - env=self.env, - check=True - ) - - @staticmethod - def _get_net_segment_class(idx): - mapping = { - 0: NetConv1, - 1: NetConv2, - 2: NetFC1, - 3: NetFC2, - 4: NetFC3 - } - segment_class = mapping.get(idx) - if segment_class is None: - raise ValueError(f"No corresponding class found for segment index {idx}") - return segment_class - - @staticmethod - def _get_doom_segment_class(idx): - mapping = { - 0: Conv1Segment, - 1: Conv2Segment, - 2: Conv3Segment, - 3: FC1Segment, - 4: FC2Segment - } - segment_class = mapping.get(idx) - if segment_class is None: - raise ValueError(f"No corresponding class found for segment index {idx}") - return segment_class - - def _create_dummy_calibration(self, json_filepath, input_tensor): - """ - Creates a calibration file with properly normalized input in EZKL's expected format. - - Args: - json_filepath: Path where to save the calibration file - input_tensor: Either a tensor or shape tuple - """ - # Check if input_tensor is already a tensor or just a shape tuple - if not isinstance(input_tensor, torch.Tensor): - # It's a shape tuple, create a tensor - input_tensor = torch.randn(*input_tensor, device=self.device) - - # Normalize to [0,1] range - min_val = float(input_tensor.min()) - max_val = float(input_tensor.max()) - normalized_tensor = (input_tensor - min_val) / (max_val - min_val) - - # Completely flatten the tensor - this is what EZKL expects - flat_data = normalized_tensor.cpu().view(-1).tolist() - - # Create proper JSON structure with EXACTLY one level of nesting - # This creates: {"input_data": [[val1, val2, val3, ...]]} - calibration_data = {"input_data": [flat_data]} - - # Write with proper formatting - try: - with open(json_filepath, 'w') as f: - json_str = json.dumps(calibration_data) - f.write(json_str) - - # Verify the first part of file - with open(json_filepath, 'r') as f: - start = f.read(min(100, os.path.getsize(json_filepath))) - print(f"Generated JSON starts with: {start}...") - - print(f"✅ Calibration file created successfully: {json_filepath}") - except Exception as e: - print(f"❌ Error writing calibration JSON: {e}") - raise - - def circuitize_sliced_doom_model(self, model_directory: str = None): - """circuitize each slice found in the provided directory.""" - model_directory = model_directory if model_directory else self.model_dir - metadata_path = os.path.join(model_directory, "model_slices/metadata.json") - segments_path = os.path.join(model_directory, "model_slices") - # Explicitly load metadata details - with open(metadata_path, 'r') as f: - metadata = json.load(f) - - segments = metadata['segments'] - circuit_folder = os.path.join(model_directory, "ezkl/slices") - os.makedirs(circuit_folder, exist_ok=True) - - # Iterate distinctly through segments - for idx, segment_meta in enumerate(segments): - segment_type = segment_meta['type'] - segment_filename = segment_meta.get('filename') - segment_path = os.path.join(segments_path, segment_filename) - slice_output_path = os.path.join(circuit_folder, f"segment_{idx}") - os.makedirs(slice_output_path, exist_ok=True) - - # instantiate the specific segment class - SegmentClass = self._get_doom_segment_class(idx) - segment_model = SegmentClass() - segment_model.load_state_dict(torch.load(segment_path, map_location=self.device)) - segment_model.to(self.device) - segment_model.eval() - - # Generate dummy input matching segment - if idx == 0: - dummy_input_shape = (1, 4, 28, 28) - elif idx == 1: - dummy_input_shape = (1, 16, 28, 28) - elif idx == 2: - dummy_input_shape = (1, 32, 14, 14) - elif idx == 3: - dummy_input_shape = (1, 32, 7, 7) - elif idx == 4: - dummy_input_shape = (1, 256) - else: - raise ValueError(f"Unknown index: {idx}") - - dummy_input = torch.randn(dummy_input_shape, device=self.device) - - # Export to ONNX - onnx_filename = os.path.join(slice_output_path, f"segment_{idx}.onnx") - torch.onnx.export( - segment_model, - dummy_input, - onnx_filename, - input_names=["input"], - output_names=["output"], - dynamic_axes={"input": {0: "batch_size"}, "output": {0: "batch_size"}}, - opset_version=12 - ) - - # generate settings.json - subprocess.run( - [ - "ezkl", - "gen-settings", - "--param-visibility", "fixed", - "--input-visibility", "public", - "--model", onnx_filename, - "--settings-path", os.path.join(slice_output_path, f"segment_{idx}_settings.json") - ], - env=self.env, - check=True - ) - - calibration_json_filepath = os.path.join(slice_output_path, f"segment_{idx}_calibration.json") - self._create_dummy_calibration(calibration_json_filepath, dummy_input) - - # generate calibration.json - subprocess.run( - [ - "ezkl", - "calibrate-settings", - "--model", onnx_filename, - "--settings-path", os.path.join(slice_output_path, f"segment_{idx}_settings.json"), - "--data", os.path.join(slice_output_path, f"segment_{idx}_calibration.json"), - ], - env=self.env, - check=True - ) - - # change settings - # json load setting file and change 'decomp_legs' to 2 - # settings_path = os.path.join(slice_output_path, f"segment_{idx}_settings.json") - # with open(settings_path, 'r') as f: - # settings = json.load(f) - # - # settings["run_args"]['decomp_legs'] = 2 - # - # with open(settings_path, 'w') as f: - # json.dump(settings, f, indent=4) - - # generate model.compiled - subprocess.run( - [ - "ezkl", - "compile-circuit", - "--model", onnx_filename, - "--settings-path", os.path.join(slice_output_path, f"segment_{idx}_settings.json"), - "--compiled-circuit", os.path.join(slice_output_path, f"segment_{idx}_model.compiled") - ], - env=self.env, - check=True - ) - - # generate pk and vk - subprocess.run( - [ - "ezkl", - "setup", - "--compiled-circuit", os.path.join(slice_output_path, f"segment_{idx}_model.compiled"), - "--vk-path", os.path.join(slice_output_path, f"segment_{idx}_vk.key"), - "--pk-path", os.path.join(slice_output_path, f"segment_{idx}_pk.key") - ], - env=self.env, - check=True - ) - - def circuitize_sliced_net_model(self, model_directory: str = None): - """circuitize each slice found in the provided directory.""" - model_directory = model_directory if model_directory else self.model_dir - metadata_path = os.path.join(model_directory, "slices/metadata.json") - segments_path = os.path.join(model_directory, "slices") - - with open(metadata_path, 'r') as f: - metadata = json.load(f) - - segments = metadata['segments'] - circuit_folder = os.path.join(model_directory, "ezkl/slices") - os.makedirs(circuit_folder, exist_ok=True) - - - for idx, segment_meta in enumerate(segments): - segment_type = segment_meta['type'] - segment_filename = segment_meta.get('filename') - segment_path = os.path.join(segments_path, segment_filename) - slice_output_path = os.path.join(circuit_folder, f"segment_{idx}") - os.makedirs(slice_output_path, exist_ok=True) - - - # instantiate the specific segment class - SegmentClass = self._get_net_segment_class(idx) - segment_model = SegmentClass() - segment_model.load_state_dict(torch.load(segment_path, map_location=self.device)) - segment_model.to(self.device) - segment_model.eval() - - # dummy inputs for layers - if idx == 0: - dummy_input_shape = (1, 3, 32, 32) - elif idx == 1: - dummy_input_shape = (1, 6, 14, 14) - elif idx == 2: - dummy_input_shape = (1, 16, 5, 5) - elif idx == 3: - dummy_input_shape = (1, 120) - elif idx == 4: - dummy_input_shape = (1, 84) - else: - raise ValueError(f"Unknown index: {idx}") - - dummy_input = torch.randn(dummy_input_shape, device=self.device) - - # Export to ONNX - onnx_filename = os.path.join(slice_output_path, f"segment_{idx}.onnx") - torch.onnx.export( - segment_model, - dummy_input, - onnx_filename, - input_names=["input"], - output_names=["output"], - dynamic_axes={"input": {0: "batch_size"}, "output": {0: "batch_size"}}, - opset_version=12 - ) - - # generate settings.json - subprocess.run( - [ - "ezkl", - "gen-settings", - "--param-visibility", "fixed", - "--input-visibility", "public", - "--model", onnx_filename, - "--settings-path", os.path.join(slice_output_path, f"segment_{idx}_settings.json") - ], - env=self.env, - check=True - ) - - calibration_json_filepath = os.path.join(slice_output_path, f"segment_{idx}_calibration.json") - self._create_dummy_calibration(calibration_json_filepath, dummy_input) - - subprocess.run( - [ - "ezkl", - "calibrate-settings", - "--model", onnx_filename, - "--settings-path", os.path.join(slice_output_path, f"segment_{idx}_settings.json"), - "--data", os.path.join(slice_output_path, f"segment_{idx}_calibration.json"), - ], - env=self.env, - check=True - ) - - # change settings - # json load setting file and change 'decomp_legs' to 4 - settings_path = os.path.join(slice_output_path, f"segment_{idx}_settings.json") - with open(settings_path, 'r') as f: - settings = json.load(f) - - settings["run_args"]['decomp_legs'] = 4 - - with open(settings_path, 'w') as f: - json.dump(settings, f, indent=4) - - subprocess.run( - [ - "ezkl", - "compile-circuit", - "--model", onnx_filename, - "--settings-path", os.path.join(slice_output_path, f"segment_{idx}_settings.json"), - "--compiled-circuit", os.path.join(slice_output_path, f"segment_{idx}_model.compiled") - ], - env=self.env, - check=True - ) - - subprocess.run( - [ - "ezkl", - "setup", - "--compiled-circuit", os.path.join(slice_output_path, f"segment_{idx}_model.compiled"), - "--vk-path", os.path.join(slice_output_path, f"segment_{idx}_vk.key"), - "--pk-path", os.path.join(slice_output_path, f"segment_{idx}_pk.key") - ], - env=self.env, - check=True - ) - - -# Example usage -# TODO: Integrate into Ezkl runner -if __name__ == "__main__": - # Choose which model to test - model_choice = 1 # Change this to test different models - - base_paths = { - 1: "models/doom", - 2: "models/net" - } - - model_dir = base_paths[model_choice] - model_circuitizer = ModelCircuitizer(model_directory=model_dir) - - if model_choice == 1: - # model_circuitizer.circuitize_doom_model() - model_circuitizer.circuitize_sliced_doom_model() - - elif model_choice == 2: - model_circuitizer.circuitize_net_model() - model_circuitizer.circuitize_sliced_net_model() diff --git a/src/model_tester.py b/src/model_tester.py deleted file mode 100644 index 9fe50d5..0000000 --- a/src/model_tester.py +++ /dev/null @@ -1,627 +0,0 @@ -import os -import random -import re -import subprocess -import threading -import time -from pathlib import Path -import pickle -import numpy as np -import json -import torch - -import psutil - -from src.runners.ezkl_runner import EzklRunner -from src.runners.jstprove_runner import JSTProveRunner -from src.runners.model_runner import ModelRunner -from src.runners.runner_utils import RunnerUtils - - -class ModelTester: - def __init__(self, model_directory): - self.model_directory = model_directory - self.model_runner = ModelRunner(model_directory) - self.ezkl_runner = EzklRunner(model_directory) - self.jstprove_runner = JSTProveRunner(model_directory) - - - def test_model_accuracy(self, num_runs=10, input_path: str=None): - """Run normal/ezkl inference on the sliced and whole and compare output from both.""" - # Check if input file path exists - input_path = input_path if input_path else os.path.join(self.model_directory, "input.json") - - # dict of {int run, final output from [original whole model (0-7), original sliced model(0-7), circuitized_whole_model(0-7), circuitized_sliced model(0-7)]} - results = {} - - # get original input from path, find out its shape so we can generate more inputs - generated_inputs_directory = os.path.join(self.model_directory, "generated_inputs") - os.makedirs(generated_inputs_directory, exist_ok=True) - - # for num_runs - for i in range(num_runs): - - # run inference on an original whole model - original_output = self.model_runner.infer(input_path=input_path) - original_output = original_output['logits'] - - # run inference on an original sliced model - sliced_output = self.model_runner.infer(input_path=input_path, mode="sliced") - sliced_output = sliced_output['logits'] - - # run generate_witness on a whole model - witness_output = self.model_runner.generate_witness(input_file=input_path) - circuitized_output = self.model_runner.process_witness_output(witness_output) - circuitized_output = circuitized_output['logits'] - - # run generate_witness_sliced --> get the output from last witness.json - # use helper method to fetch final result and run through a softmax - sliced_witness_output = self.model_runner.generate_witness_sliced(input_path=input_path) - sliced_witness_output = self.model_runner.process_sliced_witness_output(sliced_witness_output) - sliced_circuitized_output = sliced_witness_output['logits'] - - # add to results - results[i] = { - "original_output": original_output, - "sliced_output": sliced_output, - "circuitized_output": circuitized_output, - "sliced_circuitized_output": sliced_circuitized_output, - } - - # mutate, or randomly generate new input for the next round - input_path = self._generate_random_input_file(input_path, generated_inputs_directory) - - # For the results - accuracies = { - "original_vs_sliced": [], - "original_vs_circuitized": [], - "original_vs_sliced_circuitized": [], - "circuitized_vs_sliced_circuitized": [], - } - - for i, result in results.items(): - original_output = result["original_output"].detach().cpu().numpy() - sliced_output = result["sliced_output"].detach().cpu().numpy() - circuitized_output = result["circuitized_output"].detach().cpu().numpy() - sliced_circuitized_output = result["sliced_circuitized_output"].detach().cpu().numpy() - - max_error = 1.0 - - # Calculate accuracy as (1 - normalized_error) and convert to percentage - accuracies["original_vs_sliced"].append( - float(100 * (1 - np.mean(np.abs(original_output - sliced_output)) / max_error)) - ) - accuracies["original_vs_circuitized"].append( - float(100 * (1 - np.mean(np.abs(original_output - circuitized_output)) / max_error)) - ) - accuracies["original_vs_sliced_circuitized"].append( - float(100 * (1 - np.mean(np.abs(original_output - sliced_circuitized_output)) / max_error)) - ) - accuracies["circuitized_vs_sliced_circuitized"].append( - float(100 * (1 - np.mean(np.abs(circuitized_output - sliced_circuitized_output)) / max_error)) - ) - - # Calculate the average accuracies over all runs - average_accuracies = {key: max(0.0, min(100.0, float(np.mean(value)))) for key, value in accuracies.items()} - - # Return the results - return { - "results": results, - "accuracies": average_accuracies - } - - - - - def test_jst_prove(self, mode: str ="whole"): - """ - Runs the doom_model circuit command and extracts performance metrics. - - Executes 'Python -m python_testing.circuit_models.doom_model' in the - '../GravyTesting-Internal' directory relative to the project root. - Parses the command output to find 'Peak Memory used Overall' and - 'Time elapsed' values. - - Returns: - dict: A dictionary containing 'peak_memory' and 'time_elapsed' as floats. - Returns {'peak_memory': None, 'time_elapsed': None} if values - cannot be found or an error occurs. - Raises: - FileNotFoundError: If the '../GravyTesting-Internal' directory doesn't exist. - subprocess.CalledProcessError: If the command execution fails. - RuntimeError: If parsing fails to find the required metrics. - """ - # Get the directory of the current script (src/) - script_dir = Path(__file__).resolve().parent - # Get the project root directory (kubz/) by going one level up - project_root = script_dir.parent - # Construct the absolute path to the target directory - target_directory_path = project_root.parent / "GravyTesting-Internal" # Go one more level up for GravyTesting-Internal - - - # Determine the command based on the mode - if mode == "whole": - # module_name = "python_testing.circuit_models.doom_model" - # Define the two commands for 'whole' mode - compile_command = [ - "python", "cli.py", "--circuit", "doom_model", "--class", "Doom", - "--compile", "--circuit_path", "doom_circuit.txt" - ] - witness_command = [ - "python", "cli.py", "--circuit", "doom_model", "--class", "Doom", - "--gen_witness", "--input", "inputs/doom_input.json", - "--output", "output/doom_output.json", "--circuit_path", "doom_circuit.txt" - ] - - elif mode == "sliced": - module_name = "python_testing.circuit_models.doom_slices" - else: - raise ValueError(f"Invalid mode '{mode}'. Choose 'whole' or 'sliced'.") - - command = ["Python", "-m", module_name] - results = {'peak_memory': None, 'time_elapsed': None} - - try: - # Ensure the target directory exists before trying to run the command - if not target_directory_path.is_dir(): - raise FileNotFoundError(f"Calculated directory '{target_directory_path}' does not exist or is not a directory.") - - print(f"Running command: {' '.join(command)} in {target_directory_path} (mode: {mode})") - # Execute the command using the calculated absolute path - process = subprocess.run( - command, - cwd=target_directory_path, # Use the calculated absolute path - capture_output=True, - text=True, - check=True # Raise an exception if the command fails - ) - - output = process.stdout - # print("Command output:") - # print(output) # Print output for debugging - - # Define regex patterns - # memory in MB - memory_pattern = r"Peak Memory used Overall : (\d+(\.\d+)?)" - time_pattern = r"Time elapsed: (\d+(\.\d+)?) seconds" - - if mode == "whole": - # Parse single values for 'whole' mode - memory_match = re.search(memory_pattern, output) - time_match = re.search(time_pattern, output) - - if memory_match: - results['peak_memory'] = float(memory_match.group(1)) - else: - print("Warning: Could not find 'Peak Memory used Overall' in output.") - - if time_match: - results['time_elapsed'] = float(time_match.group(1)) - else: - print("Warning: Could not find 'Time elapsed' in output.") - - elif mode == "sliced": - # Parse and aggregate multiple values for 'sliced' mode - memory_matches = re.findall(memory_pattern, output) - time_matches = re.findall(time_pattern, output) - - if memory_matches: - # Find the maximum peak memory across all slices - peak_memory_values = [float(match[0]) for match in memory_matches] - results['peak_memory'] = max(peak_memory_values) if peak_memory_values else None - print(f"Found {len(peak_memory_values)} memory values. Max: {results['peak_memory']}") - else: - print("Warning: Could not find any 'Peak Memory used Overall' in output for sliced mode.") - - if time_matches: - # Store individual times and calculate the total time - time_elapsed_values = [float(match[0]) for match in time_matches] - results['slice_times'] = time_elapsed_values - results['total_time_elapsed'] = sum(time_elapsed_values) if time_elapsed_values else None - print(f"Found {len(time_elapsed_values)} time values. Individual: {results['slice_times']}. Total: {results['total_time_elapsed']}") - else: - print("Warning: Could not find any 'Time elapsed' in output for sliced mode.") - results['slice_times'] = [] - - except FileNotFoundError as e: - print(f"Error: Directory not found. {e}") - raise # Re-raise the exception - except subprocess.CalledProcessError as e: - print(f"Error running command: {e}") - print(f"Stderr: {e.stderr}") - print(f"Stdout: {e.stdout}") - raise # Re-raise the exception - except Exception as e: - print(f"An unexpected error occurred: {e}") - raise # Re-raise the exception - - print(f"Extracted results (mode: {mode}): {results}") - return results - - - def test_deep_prove(self, deep_prove_path: Path, verbose=False, num_samples:int = 10, output_csv_path:str = None, onnx_model_path:str =None, input_path:str = None): - """ - Run DeepProve benchmark tool on an ONNX model. - - Args: - input_path (str or Path): Path to the input JSON file - onnx_model_path (str or Path): Path to the ONNX model file - output_csv_path (str or Path): Path where to save benchmark results - num_samples (int): Number of samples to process - verbose (bool): Whether to enable verbose logging - - Returns: - Path: Path to the output CSV file with benchmark results - """ - csv_name = "deepprove_benchmark_results.csv" - input_path = input_path if input_path else os.path.join(self.model_directory, "deepprove", "input.json") - onnx_model_path = onnx_model_path if onnx_model_path else os.path.join(self.model_directory, "deepprove", "model.onnx") - output_csv_path = output_csv_path if output_csv_path else os.path.join(self.model_directory, "deepprove", csv_name) - - # TODO: Ensure output/deep prove directory exists - # output_csv_path.parent.mkdir(parents=True, exist_ok=True) - - # Base command - cmd = [ - "cargo", "run", "--release", "--", - "-i", str(Path(input_path).resolve()), - "-o", str(Path(onnx_model_path).resolve()), - "-b", str(Path(output_csv_path).resolve()), - "-n", str(num_samples) - ] - - # Set up environment variables for verbose output if needed - env = os.environ.copy() - if verbose: - env["RUST_LOG"] = "trace" - env["RUST_BACKTRACE"] = "1" - - # Print command if verbose - if verbose: - print(f"Running command: {' '.join(cmd)}") - print( - f"With environment: RUST_LOG={env.get('RUST_LOG', '')}, RUST_BACKTRACE={env.get('RUST_BACKTRACE', '')}") - - # Run the command - print(f"Running DeepProve benchmark...") - try: - # Check if the deep-prove directory exists - if not deep_prove_path.exists(): - raise FileNotFoundError(f"DeepProve directory not found at {deep_prove_path}") - - result = subprocess.run( - cmd, - cwd=str(deep_prove_path), - env=env, - check=True, - text=True, - capture_output=True - ) - if verbose: - print("Command output:") - print(result.stdout) - print("DeepProve benchmark completed successfully") - return output_csv_path - except subprocess.CalledProcessError as e: - print(f"Error running DeepProve benchmark: {e}") - print(f"Command output: {e.stdout}") - print(f"Command error: {e.stderr}") - raise RuntimeError("DeepProve benchmark failed") from e - - - def _generate_random_input_file(self, input_file, save_path): - """Helper method to generate random values in the input file""" - # Generate random data with the specified shape - if "doom" in self.model_directory.lower(): - dummy_input_shape = (1, 4, 28, 28) - elif "net" in self.model_directory.lower(): - dummy_input_shape = (1, 3, 32, 32) - else: - raise ValueError("Unknown input file shape. Please specify the shape manually in the code. ") - - random_data = np.random.rand(*dummy_input_shape) - - # Normalize to the range [0, 1] (similar to the example data) - random_data = random_data.astype(np.float32) - - # Flatten the data to match the expected format - flattened_data = random_data.flatten().tolist() - - # Create the JSON structure - input_json = {"input_data": [flattened_data]} - - # Save to the specified path - output_path = os.path.join(save_path, os.path.basename(input_file)) - with open(output_path, 'w') as f: - json.dump(input_json, f) - - return output_path - - @staticmethod - def generate_multiple_random_inputs(model_directory, num_inputs, save_path, output_filename="input.json"): - """Generates multiple random inputs and saves them to a single JSON file.""" - - # Determine the input shape based on the model directory - if "doom" in model_directory.lower(): - dummy_input_shape = (1, 4, 28, 28) - elif "net" in model_directory.lower(): - dummy_input_shape = (1, 3, 32, 32) - else: - raise ValueError("Unknown input file shape. Please specify the shape manually or update the logic.") - - all_inputs = [] - for _ in range(num_inputs): - # Generate random data with the specified shape - random_data = np.random.rand(*dummy_input_shape) - # Normalize to the range [0, 1] and set type to float32 - random_data = random_data.astype(np.float32) - # Flatten the data - flattened_data = random_data.flatten().tolist() - all_inputs.append(flattened_data) - - # Create the final JSON structure - output_json = {"input_data": all_inputs} - - # Save to the specified output file - # Ensure the output directory exists if output_filename includes a path - output_dir = os.path.join(model_dir, save_path) - if output_dir and not os.path.exists(output_dir): - os.makedirs(output_dir) - - with open(Path(output_dir, output_filename), 'w') as f: - json.dump(output_json, f) - - print(f"Successfully generated {num_inputs} inputs and saved to {output_filename}") - return output_filename - - def run_inference_test(self, input_tensor, mode: str = None): - """Run inferences and track performance metrics for each input""" - - # Get current process - process = psutil.Process() - - input_tensor = RunnerUtils.reshape(input_tensor, self.model_directory) - - # Start memory tracker - start_mem = process.memory_info().rss / 1024 / 1024 # Memory in MB - - # Start timer - start_time = time.time() - - # Run inference - if mode == "sliced": - inference_result = self.model_runner.run_layered_inference(input_tensor=input_tensor) - else: - inference_result = self.model_runner.run_inference(input_tensor=input_tensor) - - # Stop timer - end_time = time.time() - - # Get peak memory - end_mem = process.memory_info().rss / 1024 / 1024 - mem_used = end_mem - start_mem - - print(f"Memory used: {mem_used} MB") - print(f"Time elapsed: {end_time - start_time} seconds") - print(f"Output: {inference_result}") - - # Record results - result = { - 'time_seconds': end_time - start_time, - 'memory_mb': mem_used, - 'output': inference_result['logits'].detach().cpu().numpy().tolist() - } - - return result - - def run_ezkl_test(self, input_file: str, mode: str = None): - """Run EZKL witness test""" - testing_dir = Path(self.model_directory, "testing") - with open(Path(self.model_directory, "testing", input_file), 'r') as f: - input_data = json.load(f) - - input_array = np.array(input_data['input_data']) - input_tensor = torch.from_numpy(input_array) - input_tensor = RunnerUtils.reshape(input_tensor, self.model_directory) - - torch_input_file = Path(testing_dir, "test_run.json") - RunnerUtils.save_to_file_flattened(input_tensor=input_tensor, file_path=str(torch_input_file)) - - results = {} - - # Run inference - if mode == "sliced": - inference_result = self.ezkl_runner.generate_witness(mode="sliced", input_file=str(torch_input_file)) - proof_result = self.ezkl_runner.prove(mode="sliced") - verification_result = self.ezkl_runner.verify(mode="sliced") - else: - inference_result = self.ezkl_runner.generate_witness(input_file=str(torch_input_file)) - proof_result = self.ezkl_runner.prove() - verification_result = self.ezkl_runner.verify() - - total_time = inference_result['total_time'] - - # Record results - results['witness'] = { - 'memory_mb': inference_result['memory'], - 'total_time': total_time, - 'layer_times': inference_result.get('segment_times', "N/A"), - 'output': inference_result['result']['logits'].detach().cpu().numpy().tolist() - } - - results['proof'] = { - 'memory_mb': proof_result['memory'], - 'total_time': proof_result['total_time'], - 'layer_times': proof_result.get('segment_times', "N/A") - } - - results['verification'] = { - 'memory_mb': verification_result['memory'], - 'total_time': verification_result['total_time'], - 'layer_times': verification_result.get('segment_times', "N/A") - } - - return results - - - def run_jstprove_test(self, input_file: str, mode: str = None): - testing_dir = Path(self.model_directory, "testing") - file_path = Path(testing_dir, input_file) - with open(Path(self.model_directory, "testing", input_file), 'r') as f: - input_data = json.load(f) - - input_array = np.array(input_data['input_data']) - input_tensor = torch.from_numpy(input_array) - input_tensor = RunnerUtils.reshape(input_tensor, self.model_directory) - - torch_input_file = Path(testing_dir, "test_run.json") - RunnerUtils.save_to_file_flattened(input_tensor=input_tensor, file_path=str(torch_input_file)) - - results = {} - - # Run inference - if mode == "sliced": - inference_result = self.jstprove_runner.generate_witness(mode="sliced", input_file=str(torch_input_file.absolute())) - proof_result = self.jstprove_runner.prove(mode="sliced") - verification_result = self.jstprove_runner.verify(mode="sliced", input_file=str(torch_input_file.absolute())) - else: - inference_result = self.jstprove_runner.generate_witness(input_file=str(torch_input_file.absolute())) - proof_result = self.jstprove_runner.prove() - verification_result = self.jstprove_runner.verify(input_file=str(torch_input_file.absolute())) - - # Record results - results['witness'] = { - 'memory_mb': inference_result['memory'], - 'total_time': inference_result['total_time'], - 'layer_times': inference_result.get('segment_times', "N/A"), - 'output': inference_result['result'] - } - - results['proof'] = { - 'memory_mb': proof_result['memory'], - 'total_time': proof_result['total_time'], - 'layer_times': proof_result.get('segment_times', "N/A") - } - - results['verification'] = { - 'memory_mb': verification_result['memory'], - 'total_time': verification_result['total_time'], - 'layer_times': verification_result.get('segment_times', "N/A") - } - - return results - - @staticmethod - def get_cifar_data(input_file): - """Loads and unpickles CIFAR dataset from input file. - - Args: - input_file (str): Path to the CIFAR pickle file. - - Returns: - dict: Dictionary containing 'data' (10000x3072 numpy array) and 'labels' (list of 10000 numbers) - """ - try: - # Unpickle data file - with open(input_file, 'rb') as fo: - data_dict = pickle.load(fo, encoding='bytes') - return data_dict - except FileNotFoundError: - print(f"Error: Input file '{input_file}' not found. Please make sure you have the CIFAR dataset and are pointing to the correct file.") - return None - - def test_all(self, num_runs=10, cifar_data_file: str = None): - """Run all test variations""" - - # data prep - cifar_data_file = cifar_data_file or os.path.join(self.model_directory, "testing", "cifar-10-batches-py", "data_batch_1") - cifar_data = self.get_cifar_data(cifar_data_file) - - # Ensure testing directory exists - testing_dir = os.path.join(self.model_directory, "testing") - os.makedirs(testing_dir, exist_ok=True) - results_file = os.path.join(testing_dir, "results.json") - - # Initialize results structure - results = {} - - # for each num run - for i in range(num_runs): - # get random cifir image - random_index = random.randint(0, len(cifar_data[b'data']) - 1) - random_input = np.frombuffer(cifar_data[b'data'][random_index], dtype=np.uint8) - true_label = cifar_data[b'labels'][random_index] # get the correct answer for this image - normalized_input = (random_input.astype(np.float32) / 127.5) - 1.0 # make bytes into floats - - input_tensor = torch.from_numpy(normalized_input).float().unsqueeze(0) - - RunnerUtils.save_to_file_flattened(input_tensor=input_tensor, file_path=os.path.join(testing_dir, "random_input.json")) - - # run .pth - pytorch_results = self.run_inference_test(input_tensor, mode="whole") - sliced_pytorch_results = self.run_inference_test(input_tensor, mode="sliced") - original_pytorch_results = {"whole": pytorch_results, "sliced": sliced_pytorch_results} - - # run jstprove - jstprove_whole_results = self.run_jstprove_test("random_input.json", mode="whole") - jstprove_sliced_results = self.run_jstprove_test("random_input.json", mode="sliced") - jstprove_results = {"whole": jstprove_whole_results, "sliced": jstprove_sliced_results} - - # run ezkl - ezkl_whole_results = self.run_ezkl_test("random_input.json", mode="whole") - ezkl_sliced_results = self.run_ezkl_test("random_input.json", mode="sliced") - ezkl_results = {"whole": ezkl_whole_results, "sliced": ezkl_sliced_results} - - - - # add data to dict (read and add/dump) - results[i] = { - "input_data": str(input_tensor.tolist()), - "input_data_label": true_label, - "original_pytorch_results": original_pytorch_results, - "jstprove_results": jstprove_results, - "ezkl_results": ezkl_results, - } - - # save results file (overwrite) - with open(results_file, 'w') as f: - json.dump(results, f) - - - # close things? - -if __name__ == "__main__": - # Choose which model to test - model_choice = 2 # Change this to test different models - - base_paths = { - 1: "models/doom", - 2: "models/net" - } - - model_dir = base_paths[model_choice] - model_tester = ModelTester(model_dir) - - # TODO: modify this to install path. Maybe env var? - deep_prove_zkml_path = Path("/Volumes/SSD/projects/deep-prove/zkml") - - # TODO: Add test for ezkl time and memory - if model_choice == 1: - # model_tester.run_inference_test("10k_inputs.json", "10k_outputs_sliced.json", mode="sliced") - # ModelTester.generate_multiple_random_inputs(model_dir, 100, "testing", "100_inputs.json") - # accuracy = model_tester.test_model_accuracy(num_runs=10) - # print(f"Model accuracy: {accuracy}") - # result = model_tester.test_jst_prove() - result = model_tester.test_jst_prove(mode="sliced") - # result = model_tester.test_deep_prove(deep_prove_path=deep_prove_zkml_path, verbose=True) - print(f"Model deep prove result: {result}") - - elif model_choice == 2: - start_time = time.time() - # ModelTester.generate_multiple_random_inputs(model_dir, 1, "testing", "1_inputs.json") - # result = model_tester.run_inference_test("input_data.json", "output_sliced.json", mode="sliced") - # result = model_tester.run_ezkl_test("1_inputs.json", "1_outputs.json", mode="whole") - # result = model_tester.run_ezkl_test("100_inputs.json", "100_outputs_sliced.json", mode="sliced") - model_tester.test_all() - print(f"Total execution time: {time.time() - start_time:.2f} seconds") - # print(f"sliced: {result}") diff --git a/src/model_visualizer.py b/src/model_visualizer.py deleted file mode 100644 index 2795e49..0000000 --- a/src/model_visualizer.py +++ /dev/null @@ -1,1611 +0,0 @@ -import base64 -import io -import json -from datetime import datetime - -import matplotlib -import matplotlib.pyplot as plt -import numpy as np -from jinja2 import Environment, FileSystemLoader - - -class ModelVisualizer: - """ - Provides functionality to visualize a neural network model based on its architecture and the metadata provided. - - The ModelVisualizer class enables visualization of different types of neural networks, - such as convolutional neural networks (CNNs), fully connected neural networks (FCNNs), - and hybrid models combining CNN layers and FCNN layers. The visualization is generated - using matplotlib and includes support for selective rendering of specific layers or segments - of the network. Metadata describing the model's structure is loaded from a JSON file, and the - visualization can optionally be saved to a file or returned as a matplotlib.figure object. - """ - - def __init__(self, metadata_path): - """ - Initialize the visualizer with a path to metadata.json. - - Args: - metadata_path (str): Path to the metadata.json file - """ - if not os.path.exists(metadata_path): - raise ValueError(f"Metadata file not found: {metadata_path}") - - with open(metadata_path, 'r') as f: - self.metadata = json.load(f) - - self.model_type = self.metadata.get("model_type", "unknown") - self.segments = self.metadata.get("segments", []) - self.fig = None - self.ax = None - - def visualize(self, output_path=None, start_segment=None, end_segment=None): - """ - Visualizes the neural network model based on its type and selected segment range - - Args: - output_path: Optional path to save the visualization image to - start_segment: First segment index to visualize (0-indexed), None for start from beginning - end_segment: Last segment index to visualize (inclusive), None for all remaining segments - - Note: - Segment indices correspond to the "index" field in the metadata file. - For example, segment index 2 will visualize the segment with "index": 2 in the metadata. - The output layer is NOT automatically added - only the specified segments are shown. - - Returns: - Path to the saved visualization file or the matplotlib figure - """ - if not self.metadata: - print("No metadata available. Cannot visualize.") - return None - - # Get model type from metadata and clean it - model_type_raw = self.metadata.get('model_type', '') - if '.' in model_type_raw: - # Extract just the type name without the enum prefix - self.model_type = model_type_raw.split('.')[-1] - else: - self.model_type = model_type_raw - - # Extract all layers from the model - all_layers = self._extract_all_layers() - - if not all_layers: - print("No layers found in model metadata.") - return None - - # Filter layers by segment index - if start_segment is not None or end_segment is not None: - # Default values if not specified - start_segment_idx = start_segment if start_segment is not None else 0 - # Get the last segment index from the metadata - max_segment_idx = max([layer.get('segment_idx', 0) for layer in all_layers]) if all_layers else 0 - end_segment_idx = end_segment if end_segment is not None else max_segment_idx - - # Validate indices - start_segment_idx = max(0, min(start_segment_idx, max_segment_idx)) - end_segment_idx = max(start_segment_idx, min(end_segment_idx, max_segment_idx)) - - # Filter layers by segment_idx - selected_layers = [layer for layer in all_layers - if start_segment_idx <= layer.get('segment_idx', 0) <= end_segment_idx] - else: - # If no filtering, use all layers - selected_layers = all_layers - - # Check the model type and visualize accordingly - if 'HYBRID' == self.model_type: - print("This is Hybrid model type.") - self.fig = self._visualize_hybrid_layers(selected_layers) - elif 'CNN' == self.model_type: - print("This is CNN model type.") - self.fig = self._visualize_cnn_layers(selected_layers) - elif 'FCNN' == self.model_type: - print("This is FCNN model type.") - self.fig = self._visualize_fcnn_layers(selected_layers) - else: - print(f"Unsupported model type: {self.model_type}") - return None - - # Save the visualization if output_path is provided - if output_path and self.fig: - try: - self.fig.savefig(output_path, dpi=300, bbox_inches='tight') - print(f"Visualization saved to: {output_path}") - except Exception as e: - print(f"Error saving visualization: {e}") - - return output_path if output_path else self.fig - - def _visualize_hybrid_layers(self, selected_layers: list[dict] | None = None): - """ - Visualizes the hybrid layer structure combining CNN and FCNN components. - - This method produces a detailed 2D visual representation of the layers in a - neural network, including both CNN and FCNN components. CNN layers are - visualized using fixed dimensions while FCNN layers follow a dynamic strategy - based on the number of neurons. Connections between layers and transitions - (e.g., Flatten layer, CNN to FCNN transition) are also visualized. Options - are provided to customize the visualization based on specific layers or - sections to highlight. - """ - fig, ax = plt.subplots(figsize=(20, 12)) - - # Visual settings - layer_spacing = 4.5 # Spacing between layers (matching FCNN visualization) - neuron_radius = 0.25 # Size of neuron nodes - node_color = 'lightblue' # Color for nodes - - # Set fixed sizes for CNN layers - cnn_height = 3.0 # Fixed height for CNN layers - cnn_center_y = 0.5 # Center position - - # Track current position and previous layer info for connections - x_pos = 0.5 - prev_layer_info = None - cnn_end_x = 0 - - # Get all segments in order - all_segments = self.metadata.get('segments', []) - - # Separate CNN and FCNN segments - cnn_segments = [seg for seg in all_segments if seg.get('type') == 'conv'] - fc_segments = [seg for seg in all_segments if seg.get('type') == 'fc'] - - # Process all CNN segments with fixed height - cnn_layer_counter = 0 - for seg_idx, segment in enumerate(cnn_segments): - if 'layers' in segment: - for layer_idx, layer in enumerate(segment['layers']): - x_pos, layer_info = self._draw_conv_layer_2d( - ax, layer, x_pos, seg_idx, len(cnn_segments), - plt.cm.viridis, cnn_center_y, cnn_height - ) - - # Add layer name at the bottom - layer_name = layer.get('name', f"CNN{cnn_layer_counter}") - y_pos_label = cnn_center_y - (cnn_height / 2) - 0.3 # Position below the layer - ax.text(x_pos - 0.25, y_pos_label, layer_name, - ha='center', va='top', fontsize=10, zorder=4) - - if prev_layer_info: - self._draw_arrow_between_layers(ax, prev_layer_info, layer_info) - - prev_layer_info = layer_info - cnn_layer_counter += 1 - x_pos += 0.5 # Add some spacing - - # Add pooling if this isn't the last CNN segment - if seg_idx < len(cnn_segments) - 1: - x_pos, layer_info = self._draw_pooling_layer_2d( - ax, x_pos, plt.cm.viridis, cnn_center_y, cnn_height - ) - - # Label the pooling layer - y_pos_label = cnn_center_y - (cnn_height / 2) - 0.3 - ax.text(x_pos - 0.25, y_pos_label, "MaxPool", - ha='center', va='top', fontsize=10, zorder=4) - - if prev_layer_info: - self._draw_arrow_between_layers(ax, prev_layer_info, layer_info) - - prev_layer_info = layer_info - x_pos += 0.5 # Add some spacing - - # Store CNN ending position to calculate proper transition - cnn_end_x = x_pos - - # Add flatten transition if we have both CNN and FC segments - if cnn_segments and fc_segments: - # Add extra spacing before flatten layer - x_pos += 1.0 - - # Look for reshape information in the first FC segment - reshape_info = None - if fc_segments and 'input_reshape' in fc_segments[0]: - reshape_info = fc_segments[0]['input_reshape'] - - x_pos, layer_info = self._draw_flatten_transition_2d( - ax, x_pos, plt.cm.viridis, cnn_center_y, cnn_height, - reshape_info=reshape_info - ) - - # Label the flatten layer - y_pos_label = cnn_center_y - (cnn_height / 2) - 0.3 - ax.text(x_pos - 0.25, y_pos_label, "Flatten", - ha='center', va='top', fontsize=10, zorder=4) - - if prev_layer_info: - self._draw_arrow_between_layers(ax, prev_layer_info, layer_info) - - prev_layer_info = layer_info - - # Add more spacing after flatten layer to separate CNN and FC sections - x_pos += 1.5 - - # Now handle FC segments using the FCNN style visualization - if fc_segments: - # Extract all FC layers - fc_layers = [] - for segment in fc_segments: - if 'layers' in segment: - for layer in segment['layers']: - fc_layers.append(layer) - - if not fc_layers: - # No FC layers to visualize - self._format_plot_2d(ax, x_pos) - return fig - - # Calculate neuron counts for each layer - neuron_counts = [] - weights = [] - - # First collect input features for each layer - for layer in fc_layers: - if 'in_features' in layer: - neuron_counts.append(layer['in_features']) - else: - segment = next((seg for seg in fc_segments if seg.get('layers') - and layer in seg['layers']), None) - if segment and 'in_features' in segment: - neuron_counts.append(segment['in_features']) - else: - print(f"Warning: Missing in_features for layer: {layer}") - neuron_counts.append(1) - - # Extract weights if available - if 'parameters' in layer and 'weight' in layer.get('parameters', {}): - weights.append(layer['parameters']['weight']) - else: - weights.append(None) - - # Add output features of the last layer - if fc_layers: - last_layer = fc_layers[-1] - if 'out_features' in last_layer: - neuron_counts.append(last_layer['out_features']) - else: - segment = next((seg for seg in fc_segments if seg.get('layers') - and last_layer in seg['layers']), None) - if segment and 'out_features' in segment: - neuron_counts.append(segment['out_features']) - else: - print(f"Warning: Missing out_features for last layer: {last_layer}") - neuron_counts.append(1) - - # Dynamic visualization strategy for FCNN - max_neurons = max(neuron_counts) if neuron_counts else 1 - - # Adjust the threshold based on maximum neurons - neuron_threshold = 20 # Base threshold for small networks - - # Scale threshold based on max_neurons but with limits - if max_neurons > 100: - neuron_threshold = 30 - elif max_neurons > 500: - neuron_threshold = 50 - - # Sampling strategy for different size ranges - def get_sampling_strategy(count): - if count <= neuron_threshold: - return {'show_all': True, 'sample': count} # Show all - elif count <= 100: - return {'show_all': False, 'sample': min(count, neuron_threshold)} # Sample some - elif count <= 1000: - return {'show_all': False, 'sample': min(count, neuron_threshold)} # Sample fewer - else: - return {'show_all': False, 'sample': min(count, neuron_threshold)} # Sample even fewer - - # Add a visual separator between CNN and FCNN parts - if cnn_segments: - # Draw a vertical dashed line between CNN and FCNN - separator_x = x_pos - 1.0 - ax.axvline(x=separator_x, ymin=0.1, ymax=0.9, color='gray', - linestyle='--', linewidth=1.5, alpha=0.7, zorder=2) - - # Add a transition label - ax.text(separator_x, 0.05, "CNN → FCNN Transition", - ha='center', va='bottom', fontsize=10, color='gray', - bbox=dict(boxstyle="round,pad=0.3", fc="whitesmoke", ec="gray", alpha=0.8), - zorder=4) - - # Store layer positions for connections - layer_infos = [] - - # Draw each FC layer - for i, neurons_count in enumerate(neuron_counts): - layer_x = x_pos + i * layer_spacing - - # Apply sampling strategy - strategy = get_sampling_strategy(neurons_count) - neurons_to_draw = strategy['sample'] - draw_ellipsis = not strategy['show_all'] - - neuron_positions = [] - - # Calculate total height needed for neurons - total_height = neurons_to_draw * 1.5 * neuron_radius * 2 - start_y = 0.5 - (total_height / 2) - - # Draw neurons - for j in range(neurons_to_draw): - # Calculate neuron position - neuron_y = start_y + j * 1.5 * neuron_radius * 2 - - # Draw neuron - circle = plt.Circle((layer_x, neuron_y), neuron_radius, color=node_color, zorder=4) - ax.add_patch(circle) - neuron_positions.append((layer_x, neuron_y)) - - # Draw ellipsis if needed - if draw_ellipsis: - ellipsis_y = start_y + neurons_to_draw * 1.5 * neuron_radius * 2 + neuron_radius * 2 - ax.text(layer_x, ellipsis_y, "...", ha='center', va='center', fontsize=12, zorder=4) - - # Draw the actual count - ax.text(layer_x, ellipsis_y + neuron_radius * 3, - f"({neurons_count} total)", - ha='center', va='center', fontsize=8, zorder=4) - - # Draw layer label - Use proper index for FC layers to avoid numbering issues - if i < len(fc_layers): - # Get the actual name from the layer or use FC{i} for correct sequential numbering - layer_name = fc_layers[i].get('name', f"FC{i}") - ax.text(layer_x, start_y - neuron_radius * 3, - layer_name, ha='center', va='center', fontsize=10, zorder=4) - - # Save layer info - layer_info = { - 'x': layer_x, - 'positions': neuron_positions, - 'name': f"FC{i}" if i < len(fc_layers) else "Output", - 'type': 'fc' - } - layer_infos.append(layer_info) - - # Draw connections between FC layers - for i in range(len(layer_infos) - 1): - from_layer = layer_infos[i] - to_layer = layer_infos[i + 1] - - # Get weight values if available - weight_values = None - if i < len(weights) and weights[i] is not None: - if 'data' in weights[i]: - weight_values = weights[i]['data'] - elif 'tensor' in weights[i]: - weight_values = weights[i]['tensor'] - elif 'shape' in weights[i]: - shape = weights[i]['shape'] - if len(shape) == 2: - weight_values = np.random.randn(shape[0], shape[1]) - - # Draw connections - for from_idx, from_pos in enumerate(from_layer['positions']): - for to_idx, to_pos in enumerate(to_layer['positions']): - color = 'gray' # Default color - - if weight_values is not None and from_idx < weight_values.shape[1] and to_idx < \ - weight_values.shape[ - 0]: - weight = weight_values[to_idx, from_idx] - cmap = plt.cm.bwr - norm_weight = max(-1, min(1, weight)) - color = cmap((norm_weight + 1) / 2) - - ax.plot([from_pos[0], to_pos[0]], [from_pos[1], to_pos[1]], - color=color, linewidth=0.5, alpha=0.6, zorder=3) - - # Update final x_pos - if layer_infos: - x_pos = layer_infos[-1]['x'] + layer_spacing / 2 - - # Format the plot - self._format_plot_2d(ax, x_pos) - return fig - - def _visualize_cnn_layers(self, layers: list[dict]) -> matplotlib.figure.Figure: - - """ - Visualizes the structure of convolutional neural network layers, including - convolution, pooling, and flatten layers. The function dynamically arranges - and annotates components based on the provided layer configuration for - visual clarity. - - The visualization will adapt to the structure, spacing, and styles based on - the layers provided in the `layers` parameter. The output is a matplotlib - figure detailing the sequence and connectivity of the layers. - - This function is intended for debug and presentation purposes. - - :return: A matplotlib figure object representing the visualized CNN layers. - :rtype: matplotlib.figure.Figure - """ - # Debug check - if not layers: - print("Warning: No layers provided to visualize") - # Return empty figure - fig, ax = plt.subplots(figsize=(12, 8)) - ax.text(0.5, 0.5, "No layers to visualize", ha='center', va='center') - ax.axis('off') - return fig - - # Increase figure size and use 16:9 aspect ratio - fig, ax = plt.subplots(figsize=(20, 12)) - - # Visual settings - layer_spacing = 3.0 # Increased from 1.5 for better visibility - color_map = plt.cm.viridis - standard_center_y = 0.5 - standard_height = 0.8 - - # Track position for layer placement and connections - x_pos = 0 - prev_layer_info = None - - # Add a label list to track layer labels - layer_labels = [] - - # Iterate through layers - for i, layer in enumerate(layers): - layer_type = layer.get('type', '') - segment_idx = layer.get('segment_idx', i) - - # Store the starting x-position for this layer - layer_start_x = x_pos - - if layer_type in ['conv2d', 'conv1d']: - # Draw convolutional layer - x_pos, layer_info = self._draw_conv_layer_2d( - ax, layer, x_pos, i, len(layers), - color_map, standard_center_y, standard_height - ) - - # Draw arrow if not first layer - if prev_layer_info: - self._draw_arrow_between_layers(ax, prev_layer_info, layer_info) - - prev_layer_info = layer_info - - # Add layer label - activation = layer.get('segment_activation', '') - layer_labels.append({ - 'x': (layer_start_x + x_pos) / 2, # Center point of the layer - 'text': f"Layer {segment_idx}", - 'activation': activation - }) - - # Check if we need to add pooling after this layer - if i < len(layers) - 1: - next_layer = layers[i + 1] - next_type = next_layer.get('type', '') - - if next_type in ['maxpool2d', 'avgpool2d']: - # Store the starting x-position for pooling layer - pooling_start_x = x_pos - - # Draw pooling and get its position info - x_pos, pooling_info = self._draw_pooling_layer_2d( - ax, x_pos, color_map, standard_center_y, standard_height - ) - - # Draw arrow to connect - if layer_info: - self._draw_arrow_between_layers(ax, layer_info, pooling_info) - - prev_layer_info = pooling_info - - # Add pooling label (using the next layer's segment index) - pool_segment_idx = next_layer.get('segment_idx', i + 1) - pool_activation = next_layer.get('segment_activation', '') - layer_labels.append({ - 'x': (pooling_start_x + x_pos) / 2, # Center point of the pooling layer - 'text': f"Layer {pool_segment_idx}", - 'activation': pool_activation, - 'subtext': f"({next_type})" - }) - - elif layer_type in ['maxpool2d', 'avgpool2d']: - # Draw pooling layer if not handled in the previous iteration - x_pos, pooling_info = self._draw_pooling_layer_2d( - ax, x_pos, color_map, standard_center_y, standard_height - ) - - # Draw arrow if not first layer - if prev_layer_info: - self._draw_arrow_between_layers(ax, prev_layer_info, pooling_info) - - prev_layer_info = pooling_info - - # Add layer label - activation = layer.get('segment_activation', '') - layer_labels.append({ - 'x': (layer_start_x + x_pos) / 2, # Center point of the layer - 'text': f"Layer {segment_idx}", - 'activation': activation, - 'subtext': f"({layer_type})" - }) - - elif layer_type == 'flatten': - # Draw flatten transition - x_pos, layer_info = self._draw_flatten_transition_2d( - ax, x_pos, color_map, standard_center_y, standard_height - ) - - # Draw arrow if not first layer - if prev_layer_info: - self._draw_arrow_between_layers(ax, prev_layer_info, layer_info) - - prev_layer_info = layer_info - - # Add layer label - activation = layer.get('segment_activation', '') - layer_labels.append({ - 'x': (layer_start_x + x_pos) / 2, # Center point of the layer - 'text': f"Layer {segment_idx}", - 'activation': activation, - 'subtext': "(flatten)" - }) - - # Calculate the bottom position for labels - y_min, y_max = ax.get_ylim() - label_y = y_min - 0.3 # Position below the visualization - - # Add layer labels - for label_info in layer_labels: - # Layer number text - ax.text( - label_info['x'], - label_y, - label_info['text'], - ha='center', - va='top', - fontsize=10 - ) - - # Add subtext if available (layer type) - if 'subtext' in label_info and label_info['subtext']: - ax.text( - label_info['x'], - label_y - 0.2, - label_info['subtext'], - ha='center', - va='top', - fontsize=9, - color='gray' - ) - - # Activation function text (on a new line) - if label_info['activation']: - ax.text( - label_info['x'], - label_y - 0.4 if 'subtext' not in label_info else label_y - 0.6, - f"({label_info['activation']})", - ha='center', - va='top', - fontsize=9 - ) - - # Format the plot - pass label_y to adjust bottom margin - self._format_plot_2d(ax, x_pos + layer_spacing, True, label_y) - - # Add title - ax.set_title('Convolutional Neural Network Architecture', fontsize=14) - - # Use 16:9 aspect ratio - ax.set_aspect(16 / 9) - - return fig - - def _visualize_fcnn_layers(self, layers): - """ - Visualizes the Fully Connected Neural Network (FCNN) layer structure, including neuron - nodes and inter-layer connections. This visualization provides an overview of the - network architecture, connection relationships, and possibly the weight distribution - (if weights are provided). - - The function is capable of handling layers with missing specifications (e.g., - `in_features` or `out_features`) by adopting default settings or fallbacks. For - layers with large numbers of neurons, scaling and ellipsis are applied to keep the - visualization interpretable. Additionally, random or default weights can be used if - none are provided for inter-layer connections. - - This visualization makes use of matplotlib for drawing the network diagram, including - customized settings for layer spacing, node size, and connection intensity based on - weights. The connections between layers are color-encoded to depict strength (weak - connections in blue and strong connections in red) when weight information is available. - """ - # Debug check - if not layers: - print("Warning: No layers provided to visualize") - # Return empty figure - fig, ax = plt.subplots(figsize=(12, 8)) - ax.text(0.5, 0.5, "No layers to visualize", ha='center', va='center') - ax.axis('off') - return fig - - # Increase figure width and height for better spacing - fig, ax = plt.subplots(figsize=(20, 12)) # Wider and taller - - # Calculate neuron counts for each layer - neuron_counts = [] - weights = [] - using_random_weights = False - - # First collect all input features for nodes - for layer in layers: - if 'in_features' in layer: - neuron_counts.append(layer['in_features']) - elif hasattr(layer, 'in_features'): - neuron_counts.append(layer.in_features) - else: - print(f"Warning: Layer missing in_features: {layer}") - # Fallback to a default value - neuron_counts.append(1) - - # Extract weights if available - if 'parameters' in layer and 'weight' in layer['parameters']: - weights.append(layer['parameters']['weight']) - else: - weights.append(None) - using_random_weights = True # Mark that we'll need to use random weights - - # Check if the last layer is the final layer of the model - is_final_layer = False - if layers: - last_layer = layers[-1] - # Check if this is the last layer in the model by checking its segment index - all_layers = self._extract_all_layers() if hasattr(self, '_extract_all_layers') else [] - if all_layers: - max_segment_idx = max([layer.get('segment_idx', 0) for layer in all_layers]) - is_final_layer = last_layer.get('segment_idx', -1) == max_segment_idx - - # If this is the final layer OR if we're specifically asked to show the output layer, - # add the output features as the last column - if is_final_layer: - if 'out_features' in last_layer: - neuron_counts.append(last_layer['out_features']) - elif hasattr(last_layer, 'out_features'): - neuron_counts.append(last_layer.out_features) - else: - print(f"Warning: Last layer missing out_features: {last_layer}") - neuron_counts.append(1) # Default fallback - - # Visual settings - layer_spacing = 4.5 # Further increased spacing between layers - neuron_radius = 0.25 # Slightly smaller nodes - node_color = 'lightblue' # Consistent color for all nodes - - # Calculate scale factor for dense layers - max_neurons = max(neuron_counts) if neuron_counts else 1 - # Only show ellipsis for layers with more than 100 neurons - neuron_threshold = 100 - scale_factor = 1.0 - if max_neurons > neuron_threshold: - scale_factor = neuron_threshold / max_neurons - - # Store layer positions for connections - layer_infos = [] - - # Draw each FC layer - for i, neurons_count in enumerate(neuron_counts): - layer_x = i * layer_spacing - - # Determine how many neurons to draw - draw_ellipsis = neurons_count > neuron_threshold - neurons_to_draw = neurons_count if not draw_ellipsis else int(neurons_count * scale_factor) - - neuron_positions = [] - - # Calculate total height needed for neurons, but use a more compact layout - total_height = neurons_to_draw * 1.8 * neuron_radius # Reduced vertical spacing - if draw_ellipsis: - total_height += 1.8 * neuron_radius # Space for ellipsis - - # Calculate starting y-position to center neurons vertically - start_y = -total_height / 2 - - # Draw neurons in this layer - for j in range(neurons_to_draw): - neuron_y = start_y + j * 1.8 * neuron_radius + neuron_radius - circle = plt.Circle( - (layer_x, neuron_y), - neuron_radius, - color=node_color if i < len(neuron_counts) - 1 or not is_final_layer else 'lightgreen', - # Highlight output layer - ec='black', - lw=0.5 - ) - ax.add_patch(circle) - neuron_positions.append(neuron_y) - - # Add ellipsis for large layers - if draw_ellipsis: - ellipsis_y = start_y + neurons_to_draw * 1.8 * neuron_radius + neuron_radius - ax.text(layer_x, ellipsis_y, "...", ha='center', va='center', fontsize=10) - - layer_infos.append({ - 'x': layer_x, - 'positions': neuron_positions, - 'count': neurons_count - }) - - # Import ColorMap for weight coloring - from matplotlib.colors import LinearSegmentedColormap - - # Create custom colormap: blue (weak) -> white (medium) -> red (strong) - weight_cmap = LinearSegmentedColormap.from_list( - 'weight_cmap', - ['#2050C0', '#FFFFFF', '#C02050'] - ) - - # Draw connections between layers - but only between actual layers - # and to output layer if it's the final layer - connections_to_draw = len(layer_infos) - 1 - for i in range(connections_to_draw): - source_layer = layer_infos[i] - target_layer = layer_infos[i + 1] - - # Get weights for this layer if available - weight_matrix = None - if i < len(weights) and weights[i] is not None: - if isinstance(weights[i], dict) and 'shape' in weights[i]: - # Try to create a mock weight matrix for visualization - out_features = weights[i]['shape'][0] if len(weights[i]['shape']) >= 1 else target_layer['count'] - in_features = weights[i]['shape'][1] if len(weights[i]['shape']) >= 2 else source_layer['count'] - # Create a normalized random weight matrix for visualization if actual values aren't available - weight_matrix = np.random.randn(out_features, in_features) - # Normalize to [-1, 1] range for color mapping - weight_matrix = 2 * (weight_matrix - weight_matrix.min()) / ( - weight_matrix.max() - weight_matrix.min()) - 1 - else: - # Use random weights with a moderate distribution - out_features = target_layer['count'] - in_features = source_layer['count'] - np.random.seed(i) # Set seed for reproducibility - weight_matrix = np.random.randn(out_features, in_features) - # Normalize to [-1, 1] range for color mapping - weight_matrix = 2 * (weight_matrix - weight_matrix.min()) / ( - weight_matrix.max() - weight_matrix.min()) - 1 - - # Draw connections between neurons - src_indices = list(range(min(len(source_layer['positions']), source_layer['count']))) - tgt_indices = list(range(min(len(target_layer['positions']), target_layer['count']))) - - # Scale down the number of connections if there are too many - if len(src_indices) * len(tgt_indices) > 1000: - src_indices = src_indices[:20] # Limit to first 20 source neurons - tgt_indices = tgt_indices[:20] # Limit to first 20 target neurons - - for src_idx in src_indices: - src_y = source_layer['positions'][src_idx] - for tgt_idx in tgt_indices: - tgt_y = target_layer['positions'][tgt_idx] - - # Get weight value for color and alpha - weight_val = 0 - alpha = 0.2 # Base opacity - if weight_matrix is not None and src_idx < weight_matrix.shape[1] and tgt_idx < weight_matrix.shape[ - 0]: - weight_val = weight_matrix[tgt_idx, src_idx] - # Adjust alpha based on absolute weight value (stronger connections are more visible) - alpha = 0.1 + 0.4 * abs(weight_val) - - # Map weight value to color - line_color = weight_cmap((weight_val + 1) / 2) # Map from [-1, 1] to [0, 1] for colormap - - line = plt.Line2D( - [source_layer['x'], target_layer['x']], - [src_y, tgt_y], - color=line_color, - alpha=alpha, - linewidth=0.5 - ) - ax.add_line(line) - - # Add labels at the bottom - bottom_margin = 0.8 - - # Find the lowest point of the graph - min_y = min([min(layer['positions']) if layer['positions'] else 0 for layer in layer_infos]) - label_y = min_y - bottom_margin - - # Add labels for each layer - for i in range(len(layers)): - if i >= len(layer_infos): - break - - layer_x = layer_infos[i]['x'] - segment_idx = layers[i].get('segment_idx', i) - activation = layers[i].get('segment_activation', '') - - # Layer number text - ax.text(layer_x, label_y, f"Layer {segment_idx}", ha='center', va='top', fontsize=10) - - # Activation function text (on a new line) - if activation: - ax.text(layer_x, label_y - 0.4, f"({activation})", ha='center', va='top', fontsize=9) - - # Add a label for the output layer if we're showing it - if is_final_layer and len(layer_infos) > len(layers): - output_layer_x = layer_infos[-1]['x'] - ax.text(output_layer_x, label_y, "Output", ha='center', va='top', fontsize=10) - - # Set equal aspect ratio and limits - ax.autoscale_view() - - # Add a bit of padding - x_min, x_max = ax.get_xlim() - y_min, y_max = ax.get_ylim() - ax.set_xlim(x_min - 1, x_max + 1) - # Add extra space at the bottom for labels and potentially the random weights message - ax.set_ylim(min(y_min, label_y - 2.5), y_max + 0.5) - - # If using random weights, add a note at the bottom - if using_random_weights: - ax.text( - (x_min + x_max) / 2, # Center horizontally - label_y - 1.8, # Position below layer labels - "Note: Connection colors based on randomly generated weights (blue=weak, red=strong)", - ha='center', - va='top', - fontsize=9, - style='italic', - alpha=0.7 - ) - - # Add a small colorbar to show weight legend - from matplotlib.cm import ScalarMappable - from matplotlib.colors import Normalize - - # Create a small axes for the colorbar - cax = fig.add_axes([0.92, 0.3, 0.015, 0.4]) # [left, bottom, width, height] - norm = Normalize(vmin=-1, vmax=1) - sm = ScalarMappable(cmap=weight_cmap, norm=norm) - sm.set_array([]) - cbar = fig.colorbar(sm, cax=cax) - cbar.set_label('Weight Strength', fontsize=9) - cbar.set_ticks([-1, 0, 1]) - cbar.set_ticklabels(['Negative', 'Neutral', 'Positive']) - - ax.axis('off') - ax.set_title('Fully Connected Neural Network Architecture', fontsize=14) - - # Use a 16:9 aspect ratio - ax.set_aspect(16 / 9) - - return fig - - def _extract_all_layers(self): - """ - Extracts all layers from the metadata, augmenting each layer with - additional information such as the type and activation of its containing - segment, as well as its index within that segment. - - The method iterates through the metadata's 'segments' entry, if it exists, - and processes the contained 'layers' entries. Each layer is enriched with - metadata about its parent segment and added to the returned collection. - """ - all_layers = [] - - if 'segments' in self.metadata: - for segment_idx, segment in enumerate(self.metadata['segments']): - segment_type = segment.get('type', '') - segment_activation = segment.get('activation', '') - - if 'layers' in segment: - for layer_idx, layer in enumerate(segment['layers']): - # Create a copy with segment information - layer_with_segment = layer.copy() - layer_with_segment['segment_type'] = segment_type - layer_with_segment['segment_activation'] = segment_activation - layer_with_segment['segment_idx'] = segment_idx - layer_with_segment['layer_idx_in_segment'] = layer_idx - all_layers.append(layer_with_segment) - - return all_layers - - def _draw_conv_layer_2d(self, ax, layer, x_pos, seg_idx, total_segments, color_map, - center_y=0.5, standard_height=0.8): - """ - Draws a visual representation of a 2D convolutional layer segment on the specified - matplotlib axis. This method is designed to render both the visual dimensions - of the convolutional layer and its descriptive annotations, such as in/out channels - and kernel size. Elements of the visualization include stacked layers, grid lines, - text details, and color gradients to represent depth. - """ - from matplotlib import patheffects - - # Skip if missing required info - if 'in_channels' not in layer or 'out_channels' not in layer: - return x_pos, None - - # Get output dimensions - out_channels = layer.get('out_channels', 1) - - # Get shape information - if 'shape' in layer: - shape = layer['shape'] - kernel_h, kernel_w = shape[-2], shape[-1] - else: - kernel_h, kernel_w = 3, 3 - - # Calculate visual dimensions (maintain aspect ratio but with standard height) - aspect_ratio = kernel_w / kernel_h - visual_height = standard_height - visual_width = visual_height * aspect_ratio - - # Calculate y_offset to center the layer at center_y - y_offset = center_y - (visual_height / 2) - - # Determine number of matrices to stack - num_matrices = min(5, out_channels) - - # Base color for this segment - base_color = color_map(0.3 + 0.5 * seg_idx / max(1, total_segments - 1)) - - # Draw stacked matrices - from back to front - for i in range(num_matrices): - # Small offset for stacked effect (only horizontal, not vertical) - x_offset = x_pos + 0.015 * (num_matrices - i - 1) - - # Darker matrices at the back, brighter at front - matrix_color = color_map(0.2 + 0.6 * i / max(1, num_matrices - 1)) - alpha = 0.5 + 0.4 * i / max(1, num_matrices - 1) - - # Draw the matrix as a rectangle - rect = plt.Rectangle( - (x_offset, y_offset), - visual_width, - visual_height, - facecolor=matrix_color, - edgecolor='black', - alpha=alpha, - linewidth=0.7 - ) - ax.add_patch(rect) - - # Add grid lines for the front matrix only - if i == num_matrices - 1: - self._add_grid_lines_2d(ax, x_offset, y_offset, visual_width, visual_height) - - # Add layer information above the layer - layer_name = layer.get('name', f"Conv{seg_idx}") - in_channels = layer.get('in_channels', 1) - activation = layer.get('activation', "") - - # Layer info text (positioned above the layer) - ax.text( - x_pos + visual_width / 2, - y_offset + visual_height + 0.05, - f"{layer_name} • {in_channels}→{out_channels}\n{kernel_h}×{kernel_w} {activation}", - ha='center', va='bottom', - fontsize=8, - color='black', - weight='bold', - path_effects=[ - patheffects.withStroke(linewidth=2, foreground='white') - ] - ) - - # Store layer position information for arrow connections - layer_info = { - 'start_x': x_pos, - 'end_x': x_pos + visual_width, - 'height': visual_height, - 'y_offset': y_offset, - 'center_y': center_y, # Use the exact center_y passed in - 'width': visual_width - } - - # Update position for next layer with reduced spacing - return x_pos + 0.5 + visual_width, layer_info - - def _draw_pooling_layer_2d(self, ax, x_pos, color_map, center_y=0.5, standard_height=0.8): - """ - Draws a 2D pooling layer representation on a matplotlib axis. - - This function visualizes a 2D pooling layer typically used in - convolutional neural networks by drawing stacked rectangles - (horizontally offset) to represent the pooling operation. - It adds annotations ("MaxPool") to label the pooling layer - and returns the updated x position after the layer and its related - information. - """ - from matplotlib import patheffects - - # Use a smaller height for pooling layers but maintain the center position - pool_height = standard_height * 0.7 - pool_width = pool_height # Make it square - - # Calculate y_offset to center at center_y - y_offset = center_y - (pool_height / 2) - - pooling_color = color_map(0.2) - - # Draw stacked pooling layers (horizontal stacking only) - for i in range(3): - # Small offset (horizontal only) - x_offset = x_pos + 0.01 * (3 - i - 1) - - # Alpha and color - alpha = 0.5 + 0.4 * i / 2 - - # Draw the pooling layer as a rectangle - rect = plt.Rectangle( - (x_offset, y_offset), - pool_width, - pool_height, - facecolor=pooling_color, - edgecolor='black', - alpha=alpha, - linewidth=0.7 - ) - ax.add_patch(rect) - - # Add pooling label above the layer - ax.text( - x_pos + pool_width / 2, - y_offset + pool_height + 0.05, - "MaxPool", - ha='center', va='bottom', - fontsize=7, - color='black', - weight='bold', - path_effects=[ - patheffects.withStroke(linewidth=2, foreground='white') - ] - ) - - # Store layer position information for arrow connections - layer_info = { - 'start_x': x_pos, - 'end_x': x_pos + pool_width, - 'height': pool_height, - 'y_offset': y_offset, - 'center_y': center_y, # Use the exact center_y passed in - 'width': pool_width - } - - # Update position - return x_pos + 0.5 + pool_width, layer_info - - def _draw_flatten_transition_2d(self, ax, x_pos, color_map, center_y=0.5, standard_height=0.8, reshape_info=None): - - """ - Draws a visual representation of a 2D flatten transition for neural network diagrams. - - The method draws stacked progressively narrower rectangles to depict the flattening - layer transition in a neural network. It adds the representation of the flattened layer - to the provided axis, and updates the layer's positional information for further diagram - connections. The color and style of the transition are defined by the provided color - map, and users can adjust the layer's height and center position. - """ - from matplotlib import patheffects - - # Use standard dimensions but maintain center position - flatten_height = standard_height * 0.75 - flatten_width = flatten_height - - # Calculate y_offset to center at center_y - y_offset = center_y - (flatten_height / 2) - - transition_color = color_map(0.5) - - # Draw flatten transition with progressive shapes (horizontal stacking only) - for i in range(4): - # Small horizontal offset - x_offset = x_pos + 0.01 * (4 - i - 1) - - # Width narrows as we go higher in stack - width_factor = 1.0 - 0.15 * i / 3 - height_factor = 1.0 # Keep height constant for horizontal alignment - - # Alpha and color - alpha = 0.5 + 0.4 * i / 3 - - # Draw the transition as a rectangle - rect = plt.Rectangle( - (x_offset, y_offset), - flatten_width * width_factor, - flatten_height * height_factor, - facecolor=transition_color, - edgecolor='black', - alpha=alpha, - linewidth=0.7 - ) - ax.add_patch(rect) - - # Add flattening effect with horizontal lines - for i in range(3): - y_level = y_offset + 0.2 * i * flatten_height - ax.plot( - [x_pos + 0.1, x_pos + flatten_width - 0.1], - [y_level, center_y], - color='black', - alpha=0.6, - linewidth=0.5 - ) - - # Create label with reshape information if provided - label_text = "FLATTEN\n→ FC NETWORK" - - # Add shape information if available - if reshape_info and 'from_shape' in reshape_info and 'to_shape' in reshape_info: - from_shape = reshape_info['from_shape'] - to_shape = reshape_info['to_shape'] - - # Handle batch dimension (could be None/null) - if len(from_shape) >= 4: # NCHW format - channels = from_shape[1] - height = from_shape[2] - width = from_shape[3] - label_text += f"\n{channels}×{height}×{width} → {to_shape[-1]}" - - # Add label above the layer - ax.text( - x_pos + flatten_width / 2, - y_offset + flatten_height + 0.05, - label_text, - ha='center', va='bottom', - fontsize=8, - color='black', - weight='bold', - path_effects=[ - patheffects.withStroke(linewidth=2, foreground='white') - ] - ) - - # Store layer position information for arrow connections - layer_info = { - 'start_x': x_pos, - 'end_x': x_pos + flatten_width, - 'height': flatten_height, - 'y_offset': y_offset, - 'center_y': center_y, # Use the exact center_y passed in - 'width': flatten_width - } - - # Update position - return x_pos + 0.5 + flatten_width, layer_info - - def _draw_arrow_between_layers(self, ax, prev_layer, curr_layer): - """ - Draws a straight horizontal arrow between two layers on a given axis using matplotlib's FancyArrowPatch tool. The - arrow starts from the center of the previous layer's right side and ends at the center of the current layer's left side. - """ - from matplotlib.patches import FancyArrowPatch - - # Calculate arrow start and end points - start_x = prev_layer['end_x'] - start_y = prev_layer['center_y'] # Use center_y from previous layer - - end_x = curr_layer['start_x'] - end_y = prev_layer['center_y'] # Use same y as start for horizontal arrow - - # Create a straight arrow - arrow = FancyArrowPatch( - (start_x, start_y), - (end_x, end_y), - arrowstyle="fancy,head_width=6,head_length=8,tail_width=0.5", - facecolor='gray', - edgecolor='black', - linewidth=0.8, - alpha=0.7, - zorder=1 - ) - - # Add the arrow to the plot - ax.add_patch(arrow) - - def _add_grid_lines_2d(self, ax, x, y, width, height, num_lines=4): - """ - Add grid lines to a 2D space within the given rectangular area on the `ax` plot. - - This function generates evenly spaced horizontal and vertical grid lines based on - the specified number of divisions (`num_lines`) within the provided rectangle defined - by its top-left corner (`x`, `y`) and dimensions (`width`, `height`). The grid lines - are styled using black color, semi-transparency, and a thin line width. - """ - # Horizontal grid lines - for j in range(1, num_lines): - y_line = y + height * j / num_lines - ax.plot( - [x, x + width], - [y_line, y_line], - color='black', - alpha=0.3, - linewidth=0.5 - ) - - # Vertical grid lines - for j in range(1, num_lines): - x_line = x + width * j / num_lines - ax.plot( - [x_line, x_line], - [y, y + height], - color='black', - alpha=0.3, - linewidth=0.5 - ) - - def _format_plot_2d(self, ax, max_x, is_cnn=False, label_y=None): - """ - Formats a 2D plot with specific layout settings. This method adjusts axis limits to include margins, hides axis ticks - and spines, and sets the title based on the type of network. - """ - # Set the limits with some margins - margin = 0.5 - ax.set_xlim(-margin, max_x + margin) - - # Get current y limits - y_min, y_max = ax.get_ylim() - - # If we have a label_y value, make sure there's enough space below - if label_y is not None: - bottom_margin = abs(label_y - y_min) + 1.0 # Extra space below labels - ax.set_ylim(y_min - bottom_margin, y_max + margin) - else: - ax.set_ylim(y_min - margin, y_max + margin) - - # Remove axis ticks and labels - ax.set_xticks([]) - ax.set_yticks([]) - ax.spines['top'].set_visible(False) - ax.spines['right'].set_visible(False) - ax.spines['bottom'].set_visible(False) - ax.spines['left'].set_visible(False) - - # Add type-specific title - title = "Convolutional Neural Network" if is_cnn else "Neural Network Architecture" - ax.set_title(title, fontsize=14) - - def get_model_summary(self): - """ - Generates a comprehensive summary of the model, including its type, total parameters, - input size dimensions, segment count, and a breakdown of segment types. This method - extracts information based on the model structure and metadata defined in the class. - """ - segment_types = {} - for segment in self.segments: - segment_type = segment.get("type", "unknown") - segment_types[segment_type] = segment_types.get(segment_type, 0) + 1 - - # Determine input size based on model type - input_size = 0 - if self.segments: - first_segment = self.segments[0] - if first_segment.get("type") == "conv" and "layers" in first_segment and first_segment["layers"]: - first_layer = first_segment["layers"][0] - # For CNN, get input dimensions correctly - if "in_channels" in first_layer and "height" in first_layer and "width" in first_layer: - # Format input size as [channels, height, width] - input_size = [ - first_layer.get("in_channels", 0), - first_layer.get("height", 0), - first_layer.get("width", 0) - ] - elif first_segment.get("type") == "fc" and "in_features" in first_segment: - # For FCNN, use in_features - input_size = first_segment.get("in_features", 0) - - return { - "model_type": self.model_type, - "total_parameters": self.metadata.get("total_parameters", 0), - "input_size": input_size, - "segment_count": len(self.segments), - "segment_types": segment_types - } - - def create_html_report(self, output_path=None): - """ - Generates an HTML report summarizing the structure, layers, and parameters of the - neural network model. This report includes detailed layer-level statistics, - visualizations, and model metadata presented in a structured format. - """ - if self.fig is None: - print("No visualization available. Run visualize() first.") - return None - - # Get model summary - model_summary = self.get_model_summary() - - # Calculate model stats - total_params = 0 - total_layers = len(self.segments) - input_size = 0 - output_size = 0 - - # Determine model type based on segments - model_type_display = "Fully Connected Neural Network" # Default - - # Check if we have convolutional layers - has_conv = any(segment.get("type") == "conv" for segment in self.segments) - has_fc = any(segment.get("type") == "fc" for segment in self.segments) - - if has_conv and has_fc: - model_type_display = "Hybrid Neural Network" - elif has_conv: - model_type_display = "Convolutional Neural Network" - - # More robust input size detection - input_size_display = "0" # Default if detection fails - - # Try multiple approaches to get the input size - if self.segments: - first_segment = self.segments[0] - - # For CNN, check in_channels in the first layer, not in the segment - if first_segment["type"] == "conv": - # Check if there are layers with input dimensions - if "layers" in first_segment and first_segment["layers"]: - first_layer = first_segment["layers"][0] - - # Look for in_channels in the first_layer - if "in_channels" in first_layer: - in_channels = first_layer["in_channels"] - # For CNN, the input size should be formatted properly - input_size = in_channels - - # Try to find input dimensions - input_height = first_layer.get("height", None) - input_width = first_layer.get("width", None) - - # Check if we have dimensions - if input_height is not None and input_width is not None: - input_size_display = f"{in_channels} × {input_height} × {input_width}" - input_size = in_channels * input_height * input_width - else: - # Check if we can infer dimensions from weights shape - if "parameters" in first_layer and "weight" in first_layer["parameters"]: - weight_shape = first_layer["parameters"]["weight"]["shape"] - if len(weight_shape) == 4: # [out_channels, in_channels, kernel_h, kernel_w] - # We at least know the number of input channels - input_size_display = f"{in_channels} channels" - - # If we have more information from metadata, use that format - if "input_shape" in self.metadata: - input_shape = self.metadata["input_shape"] - if isinstance(input_shape, (list, tuple)) and len(input_shape) >= 3: - input_size_display = f"{input_shape[0]} × {input_shape[1]} × {input_shape[2]}" - input_size = input_shape[0] * input_shape[1] * input_shape[2] - else: - # If we don't have full dimensions, just show channels - input_size_display = f"{in_channels} channels" - input_size = in_channels - else: - # Fallback if in_channels not found in first_layer - input_size_display = "Unknown" - input_size = 0 - - # For FC networks, look at the first layer's in_features - elif first_segment["type"] == "fc" and "in_features" in first_segment: - input_size = first_segment["in_features"] - input_size_display = str(input_size) - - layer_stats = [] - activation_functions = set() - output_neurons = 0 - - for i, segment in enumerate(self.segments): - seg_type = segment.get("type", "unknown") - - # Track activations - activation = segment.get("activation", "None") - if activation: - activation_functions.add(activation) - - # Generate layer name - layer_name = f"Layer {i + 1}: {seg_type.upper()}" - if "name" in segment: - layer_name = segment["name"] - - # Calculate parameters - weights_count = 0 - biases_count = 0 - params_count = 0 - - if "parameters" in segment and isinstance(segment["parameters"], dict): - for param_name, param_info in segment["parameters"].items(): - if isinstance(param_info, dict) and "size" in param_info: - if "weight" in param_name.lower(): - weights_count = param_info["size"] - elif "bias" in param_name.lower(): - biases_count = param_info["size"] - params_count += param_info["size"] - elif "parameters" in segment: - params_count = segment["parameters"] - - # Create layer info matching your template structure - layer_info = {"name": layer_name, "type": seg_type.upper(), - "in_features": segment.get("in_features", segment.get("in_channels", "-")), - "out_features": segment.get("out_features", segment.get("out_channels", "-")), - "activation": activation.upper() if activation else "None", - "weights_count": f"{weights_count:,}", "biases_count": f"{biases_count:,}", - "params_count": f"{params_count:,}", "details": {}} - - # Add all additional details from your original code - - # Add layer-specific details based on type - if seg_type == "conv": - if "kernel_size" in segment: - layer_info["details"]["kernel_size"] = segment["kernel_size"] - if "stride" in segment: - layer_info["details"]["stride"] = segment["stride"] - if "padding" in segment: - layer_info["details"]["padding"] = segment["padding"] - if "out_channels" in segment: - layer_info["details"]["out_channels"] = segment["out_channels"] - - elif seg_type == "fc": - if "in_features" in segment: - layer_info["details"]["in_features"] = segment["in_features"] - if "out_features" in segment: - layer_info["details"]["out_features"] = segment["out_features"] - - elif seg_type == "pool": - if "pool_type" in segment: - layer_info["details"]["pool_type"] = segment["pool_type"] - if "kernel_size" in segment: - layer_info["details"]["kernel_size"] = segment["kernel_size"] - if "stride" in segment: - layer_info["details"]["stride"] = segment["stride"] - - elif seg_type == "dropout": - if "p" in segment: - layer_info["details"]["probability"] = segment["p"] - - # Add input and output shapes if available - if "input_shape" in segment: - layer_info["details"]["input_shape"] = segment["input_shape"] - if "output_shape" in segment: - layer_info["details"]["output_shape"] = segment["output_shape"] - - layer_stats.append(layer_info) - - # Track output size - if segment == self.segments[-1]: # Last segment - if seg_type == "fc": - output_neurons = segment.get("out_features", 0) - elif seg_type == "conv": - output_neurons = segment.get("out_channels", 0) - - # Get image data as base64 for embedding in HTML - # Save the current figure to a BytesIO object - img_data = io.BytesIO() - self.fig.savefig(img_data, format="png", dpi=150, bbox_inches='tight') - img_data.seek(0) - img_base64 = base64.b64encode(img_data.read()).decode("utf-8") - - # Get path to templates directory - template_dir = os.path.join(os.path.dirname(__file__), "templates") - env = Environment(loader=FileSystemLoader(template_dir)) - template = env.get_template("report_template.html") - - - # Calculate if visualization needs horizontal scrolling - fig_size_inches = self.fig.get_size_inches() - actual_width = fig_size_inches[0] * 100 # approximate pixels - visible_width = min(actual_width, 900) # max width for display - is_scrollable = actual_width > visible_width - - # Render the template with our data - html_content = template.render( - img_data=img_base64, - summary=model_summary, - model_type=self.model_type, - model_type_display=model_type_display, - input_size_display=input_size_display, - date=datetime.now().strftime("%Y-%m-%d %H:%M:%S"), - is_scrollable=is_scrollable, - visible_width=int(visible_width), - actual_width=int(actual_width), - total_params=self.format_number(total_params), - total_layers=total_layers, - input_size=input_size, - output_size=output_size, - layer_stats=layer_stats, - activation_functions=list(activation_functions) if activation_functions else ["None"] - ) - - # Save the HTML file - if output_path is None: - output_path = "model_report.html" - - with open(output_path, "w") as f: - f.write(html_content) - - print(f"HTML report saved to: {output_path}") - return output_path - - @staticmethod - def format_number(num): - """Format large numbers for readability""" - if num >= 1_000_000: - return f"{num / 1_000_000:.2f}M" - elif num >= 1_000: - return f"{num / 1_000:.1f}K" - else: - return str(num) - - -def try_model_visualizer(metadata_path, output_dir=None, report_name=None, visualization_name=None): - """ - Function to test the ModelVisualizer on a given model. - """ - import os - - print(f"Loading model metadata from: {metadata_path}") - - # Create the visualizer - visualizer = ModelVisualizer(metadata_path) - - # Determine output directory - if output_dir is None: - output_dir = os.path.dirname(metadata_path) - - os.makedirs(output_dir, exist_ok=True) - - # Determine file names - if visualization_name is None: - visualization_name = "model_visualization.png" - - if report_name is None: - report_name = "model_report.html" - - visualization_path = os.path.join(output_dir, visualization_name) - report_path = os.path.join(output_dir, report_name) - - # Create visualization - print(f"Creating visualization: {visualization_path}") - vis_path = visualizer.visualize(visualization_path) - print(f"Visualization saved to: {vis_path}") - - # Create HTML report - print(f"Generating HTML report: {report_path}") - report_path = visualizer.create_html_report(report_path) - print(f"HTML report saved to: {report_path}") - - # Print model summary - summary = visualizer.get_model_summary() - print("\nModel Summary:") - print(f" - Model Type: {summary['model_type']}") - print(f" - Total Parameters: {summary['total_parameters']:,}") - print(f" - Segment Count: {summary['segment_count']}") - print(f" - Segment Types: {', '.join([f'{k} ({v})' for k, v in summary['segment_types'].items()])}") - - # Try to open the visualization if we're in an environment that supports it - try: - import webbrowser - webbrowser.open(f"file://{os.path.abspath(report_path)}") - except: - print("Could not automatically open the HTML report.") - - -# Example usage: -if __name__ == "__main__": - import os - - # Choose which model to visualize - model_choice = 2 # Change this to test different models - - if model_choice == 1: - model_dir = "models/net" - metadata_path = os.path.join(model_dir, "slices/metadata.json") - output_dir = os.path.join(model_dir, "visualization") - - elif model_choice == 2: - # Hybrid model (CNN + FCNN) - model_dir = "models/doom" - metadata_path = os.path.join(model_dir, "slices/metadata.json") - output_dir = os.path.join(model_dir, "visualization") - - # Run the visualizer with the selected model - try_model_visualizer( - metadata_path=metadata_path, - output_dir=output_dir, - report_name="model_report.html", - visualization_name="model_visualization.png" - ) diff --git a/src/models/doom/deepprove/input.json b/src/models/doom/deepprove/input.json deleted file mode 100644 index 78effd3..0000000 --- a/src/models/doom/deepprove/input.json +++ /dev/null @@ -1 +0,0 @@ -{"input_data": [[0.13333334028720856, 0.13333334028720856, 0.13725490868091583, 0.12941177189350128, 0.1411764770746231, 0.09803921729326248, 0.09803921729326248, 0.10588235408067703, 0.1568627506494522, 0.15294118225574493, 0.16078431904315948, 0.16078431904315948, 0.16078431904315948, 0.15294118225574493, 0.1568627506494522, 0.16470588743686676, 0.1568627506494522, 0.1568627506494522, 0.1568627506494522, 0.16078431904315948, 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Neural Network Visualizer

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Interactive model architecture visualization and analysis

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Model Summary

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Total Parameters
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5
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Model Structure

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Architecture TypeHybrid Neural Network
Number of Layers5
Activation FunctionsSoftmax, ReLU
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Network Architecture Visualization

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Scroll horizontally to view the complete network →

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Layer Details

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TypeCONV
Input Features4
Output Features16
ActivationRELU
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Total Parameters592
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TypeCONV
Input Features16
Output Features32
ActivationRELU
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Total Parameters4,640
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TypeCONV
Input Features32
Output Features32
ActivationRELU
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Total Parameters9,248
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TypeFC
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Total Parameters401,664
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TypeFC
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ActivationSOFTMAX
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Total Parameters1,799
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Model Details

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{'model_type': 'HYBRID', 'total_parameters': 417943, 'input_size': 0, 'segment_count': 5, 'segment_types': {'conv': 3, 'fc': 2}}
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- - - - - Generated with ModelVisualizer | 2025-03-19 18:09:42 -

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-0.3176470398902893, -0.3176470398902893, -0.3176470398902893, -0.30980390310287476]]} \ No newline at end of file diff --git a/src/models/yolov3/model.onnx b/src/models/yolov3/model.onnx deleted file mode 100644 index ff0081f..0000000 Binary files a/src/models/yolov3/model.onnx and /dev/null differ diff --git a/src/prover.py b/src/prover.py new file mode 100644 index 0000000..c4df1ec --- /dev/null +++ b/src/prover.py @@ -0,0 +1,192 @@ +""" +Orchestration for various provers. +""" + +import os +import json +import time +from pathlib import Path +from src.backends.ezkl import EZKL + +class Prover: + """ + Orchestrator for proving model execution segments. + """ + + def __init__(self): + """ + Initialize the prover. + """ + self.ezkl_runner = EZKL() + + def prove_run(self, run_results_path, metadata_path): + """ + Prove the segments in a run. + + Args: + run_results_path (str): Path to the run_results.json file + metadata_path (str): Path to the metadata.json file + + Returns: + dict: Updated run results with proof information + """ + # Load the run results and metadata + with open(run_results_path, 'r') as f: + run_results = json.load(f) + + with open(metadata_path, 'r') as f: + metadata = json.load(f) + + # Get the run directory from the run_results_path + run_dir = os.path.dirname(run_results_path) + + # Initialize counters + proved_segments = 0 + total_ezkl_segments = 0 + + # Process each segment in the execution results + for segment in run_results["execution_chain"]["execution_results"]: + segment_id = segment["segment_id"] + + # Create a witness_execution object from the existing segment data + witness_execution = segment["witness_execution"] + # Only process segments with method "ezkl_gen_witness" and success=true + if witness_execution["method"] == "ezkl_gen_witness" and witness_execution["success"]: + total_ezkl_segments += 1 + + # Get the segment metadata + segment_metadata = metadata["slices"].get(segment_id) + if not segment_metadata: + print(f"Warning: Metadata for segment {segment_id} not found") + continue + + # Get the paths for verification + witness_path = witness_execution["output_file"] + model_path = segment_metadata["circuit_path"] + pk_path = segment_metadata["pk_path"] + + # Create proof directory and path + proof_dir = os.path.join(run_dir, segment_id) + os.makedirs(proof_dir, exist_ok=True) + proof_path = os.path.join(proof_dir, "proof.json") + + # Generate proof + print(f"Generating proof for {segment_id}...") + start_time = time.time() + prove_success, prove_result = self.ezkl_runner.prove( + witness_path=witness_path, + model_path=model_path, + proof_path=proof_path, + pk_path=pk_path + ) + prove_time = time.time() - start_time + + # Create proof_execution object + proof_execution = { + "proof_file": proof_path, + "success": prove_success, + "proof_generation_time": prove_time + } + + # Add error message if proof generation failed + if not prove_success: + print(f"Failed to generate proof for {segment_id}: {prove_result}") + proof_execution["error"] = f"Proof generation failed: {prove_result}" + else: + # If proof generation was successful, increment proved_segments + proved_segments += 1 + + # Update the segment with the new structure + segment.clear() + segment.update({ + "segment_id": segment_id, + "witness_execution": witness_execution, + "proof_execution": proof_execution + }) + else: + # For segments that are not ezkl_gen_witness or not successful, + # just restructure them to use the new format + segment.clear() + segment.update({ + "segment_id": segment_id, + "witness_execution": witness_execution + }) + + # Update the execution_chain with the new counters + run_results["execution_chain"]["ezkl_witness_slices"] = total_ezkl_segments + run_results["execution_chain"]["ezkl_proved_slices"] = proved_segments + # Set verified_slices to 0 as verification is not performed in the prove command + run_results["execution_chain"]["ezkl_verified_slices"] = 0 + + # Remove the old verification section if it exists + if "verification" in run_results: + del run_results["verification"] + + # Save the updated run results + with open(run_results_path, 'w') as f: + json.dump(run_results, f, indent=2) + + return run_results + + +if __name__ == "__main__": + # Choose which model to test + model_choice = 2 # Change this to test different models + + # Model configurations + base_paths = { + 1: "models/doom", + 2: "models/net", + 3: "models/resnet" + } + + # Get model directory + model_dir = os.path.abspath(base_paths[model_choice]) + + # Get run directory - use the latest run in the model's run directory + run_dir = os.path.join(model_dir, "run") + if not os.path.exists(run_dir): + print(f"Error: Run directory not found at {run_dir}") + exit(1) + + # Find the latest run + run_dirs = sorted([d for d in os.listdir(run_dir) if d.startswith("run_")]) + if not run_dirs: + print(f"Error: No runs found in {run_dir}") + exit(1) + + latest_run = run_dirs[-1] + run_path = os.path.join(run_dir, latest_run) + + # Construct paths for run_results.json and metadata.json + run_results_path = os.path.join(run_path, "run_result.json") + metadata_path = os.path.join(run_dir, "metadata.json") + + if not os.path.exists(run_results_path): + print(f"Error: run_result.json not found at {run_results_path}") + exit(1) + + if not os.path.exists(metadata_path): + print(f"Error: metadata.json not found at {metadata_path}") + exit(1) + + # Initialize prover + prover = Prover() + + # Run proving + print(f"Proving run {latest_run} for model {base_paths[model_choice]}...") + results = prover.prove_run(run_results_path, metadata_path) + + # Display results + print(f"\nProving completed!") + print(f"Proved segments: {results['execution_chain']['ezkl_proved_slices']} of {results['execution_chain']['ezkl_witness_slices']}") + + # Print details for each segment + print("\nSegment details:") + for segment in results["execution_chain"]["execution_results"]: + segment_id = segment["segment_id"] + if "proof_execution" in segment: + success = segment["proof_execution"]["success"] + status = "Success" if success else "Failed" + time_taken = segment["proof_execution"]["proof_generation_time"] + print(f" {segment_id}: {status} (Time: {time_taken:.2f}s)") \ No newline at end of file diff --git a/src/runner.py b/src/runner.py new file mode 100644 index 0000000..7a500d3 --- /dev/null +++ b/src/runner.py @@ -0,0 +1,337 @@ +""" +Runner for EzKL Circuit and ONNX Inference +""" + +import json +import logging +import os +import time +from pathlib import Path +import torch +import torch.nn.functional as F +from src.backends.onnx_models import OnnxModels +from src.backends.ezkl import EZKL +from src.analyzers.runner_analyzer import RunnerAnalyzer +from src.utils.utils import Utils + +logger = logging.getLogger(__name__) +logging.basicConfig(level=logging.INFO) + +class Runner: + def __init__(self, model_path, slices_path=None, metadata_path=None, run_metadata_path=None): + if not model_path: + raise ValueError("Please provide a model_path (parent of slices dir) to initialize the runner.") + self.model_path = model_path + self.slices_path = Path(slices_path) if slices_path else Path(model_path) / "slices" + self.metadata_path = Path(metadata_path) if metadata_path else self.slices_path / "metadata.json" + self.run_metadata_path = Path(run_metadata_path) if run_metadata_path else None + if not self.metadata_path.exists(): + raise FileNotFoundError(f"metadata.json not found at {self.metadata_path}. Please run slicing first.") + + if self.run_metadata_path is None or not self.run_metadata_path.exists(): + logger.info("run metadata not found. Generating...") + print(f"Generating run metadata at {self.run_metadata_path}") + runner_metadata = RunnerAnalyzer(self.model_path) + self.run_metadata_path = runner_metadata.generate_metadata(save_path=self.run_metadata_path) + + with open(self.run_metadata_path, 'r') as f: + self.metadata = json.load(f) + + self.ezkl_runner = EZKL() + + def run(self, input_json_path) -> dict: + """Run inference through the chain of segments.""" + # input_tensor = Utils.read_input(str(input_json_path)) + execution_chain = self.metadata.get("execution_chain", {}) + current_slice_id = execution_chain.get("head") + current_input = input_json_path + slice_results = {} + + run_directory = self.metadata.get("run_directory") + timestamp = time.strftime("%Y%m%d_%H%M%S") + run_id = f"run_{timestamp}" + run_dir = Path(run_directory) / run_id + + current_tensor = Utils.read_input(current_input) + + # Chain execution + while current_slice_id: + slice_node = execution_chain["nodes"][current_slice_id] + segment_dir = self.slices_path / current_slice_id + segment_dir.mkdir(exist_ok=True) + + seg_run_dir = run_dir / current_slice_id + seg_run_dir.mkdir(parents=True, exist_ok=True) + + # Write input for this segment + input_file = seg_run_dir / "input.json" + output_file = seg_run_dir / "output.json" + Utils.write_input(current_tensor, str(input_file)) + + current_slice_metadata = self.metadata["slices"][current_slice_id] + # Execute segment based on circuit availability + if slice_node.get("use_circuit"): + try: + success, tensor, ezkl_exec_info = self._run_ezkl_segment( + current_slice_metadata, input_file, output_file + ) + if success: + slice_results[current_slice_id] = ezkl_exec_info + else: + raise Exception("EZKL execution failed") + except Exception as e: + ezkl_error = str(e) + success, tensor, onnx_exec_info = self._run_onnx_segment(current_slice_metadata, input_file, + output_file) + # mark as fallback and that EZKL was attempted + onnx_exec_info["method"] = "ezkl_fallback_onnx" + onnx_exec_info["attempted_ezkl"] = True + onnx_exec_info["error"] = ezkl_error + slice_results[current_slice_id] = onnx_exec_info + + if not success: + raise Exception( + "EzKL fallback to ONNX failed for segment: " + current_slice_id + " with error: " + onnx_exec_info.get( + "error", "Unknown error. Check logs for details.")) + else: + success, tensor, execution_info = self._run_onnx_segment(current_slice_metadata, input_file, + output_file) + execution_info["attempted_ezkl"] = False + slice_results[current_slice_id] = execution_info + + if not success: + raise Exception("ONNX inference failed for segment: " + current_slice_id) + + # filter tensor and make tensor next input.json file + current_tensor = self._filter_tensor(current_slice_metadata, tensor) + current_slice_id = slice_node.get("next") + + # Final processing + probabilities = F.softmax(current_tensor, dim=1) + prediction = torch.argmax(probabilities, dim=1).item() + + results = { + "prediction": prediction, + "probabilities": probabilities.tolist(), + "tensor_shape": list(current_tensor.shape), + "slice_results": slice_results + } + + # Save inference output + self._save_inference_output(results, run_dir / "run_result.json") + + return results + + @staticmethod + def _run_onnx_segment(slice_info: dict, input_tensor_path, output_tensor_path): + """Run ONNX inference for a segment.""" + onnx_path = slice_info.get("path") + start_time = time.time() + success, result = OnnxModels.run_inference(model_path=onnx_path, input_file=input_tensor_path, output_file=output_tensor_path) + + end_time = time.time() + exec_info = {'success': success, 'method': 'onnx_only', 'execution_time': end_time - start_time, 'output_tensor_path': str(output_tensor_path)} + + if success: + exec_info['input_file'] = str(input_tensor_path.resolve()) + exec_info['output_file'] = str(output_tensor_path.resolve()) + + return success, result, exec_info + + def _run_ezkl_segment(self, slice_info: dict, input_tensor_path, output_witness_path): + """Run EZKL inference for a segment with fallback to ONNX.""" + model_path = slice_info.get("circuit_path") + vk_path = slice_info.get("vk_path") + start_time = time.time() + # Attempt EZKL execution + success, output_tensor = self.ezkl_runner.generate_witness(input_file=input_tensor_path, model_path=model_path, output_file=output_witness_path, vk_path=vk_path) + + end_time = time.time() + exec_info = {'success': success, 'method': 'ezkl_gen_witness', 'execution_time': end_time - start_time, 'witness_path': str(output_witness_path), 'attempted_ezkl': True} + + if success: + exec_info['input_file'] = str(input_tensor_path.resolve()) + exec_info['output_file'] = str(output_witness_path.resolve()) + else: + # When EZKL fails, output_tensor contains the error string per EZKL.generate_witness + exec_info['error'] = output_tensor if isinstance(output_tensor, str) else "Unknown EZKL error" + + return success, output_tensor, exec_info + + def _save_inference_output(self, results, output_path): + """Save inference_output.json with execution details.""" + model_path = self.metadata.get("model_path", "unknown") + slice_results = results.get("slice_results", {}) + + # Count execution methods + ezkl_complete = sum(1 for r in slice_results.values() + if r.get("method") == "ezkl_gen_witness") + total_slices = len(slice_results) + + # Build execution results + execution_results = [] + for slice_id, exec_info in slice_results.items(): + # Create witness_execution object to nest execution data + witness_execution = { + "method": exec_info.get("method", "unknown"), + "execution_time": exec_info.get("execution_time", 0), + "attempted_ezkl": exec_info.get("attempted_ezkl", True), + "success": exec_info.get("success", False), + "input_file": exec_info.get("input_file", "unknown"), + "output_file": exec_info.get("output_file", "unknown"), + } + # Propagate error message if present (e.g., EZKL failure reason before fallback) + if "error" in exec_info and exec_info["error"]: + witness_execution["error"] = exec_info["error"] + + # Create result_entry with segment_id and witness_execution + result_entry = { + "segment_id": slice_id, + "witness_execution": witness_execution + } + + execution_results.append(result_entry) + + # Calculate security percentage + security_percent = (ezkl_complete / total_slices * 100) if total_slices > 0 else 0 + + # Build output structure + inference_output = { + "model_path": model_path, + "prediction": results["prediction"], + "probabilities": results["probabilities"], + "execution_chain": { + "total_slices": total_slices, + "ezkl_witness_slices": ezkl_complete, + "overall_security": f"{security_percent:.1f}%", + "execution_results": execution_results + }, + "performance_comparison": { + "note": "Full ONNX vs verified chain comparison would require separate pure ONNX run" + } + } + + # Save to file + with open(output_path, 'w') as f: + json.dump(inference_output, f, indent=2) + + + @staticmethod + def _filter_tensor(current_slice_metadata, tensor): + # take the tensor object, and extract the output that is relevant to the next segment + logits = tensor["logits"] + probabilities = tensor["probabilities"] + predicted_action = tensor["predicted_action"] + + # Check the shape using our new function + expected_shape = current_slice_metadata["output_shape"] + Runner.check_expected_shape(logits, expected_shape, tensor_name="logits") + + return logits + + @staticmethod + def check_expected_shape(tensor, expected_shape_data, tensor_name="tensor"): + """ + Check if the tensor shape matches the expected shape from metadata. + + Args: + tensor: The PyTorch tensor to check + expected_shape_data: The shape data from metadata (usually a nested list with possible string placeholders) + tensor_name: Name of the tensor for logging purposes + + Returns: + bool: True if shapes match, False otherwise + """ + # Handle the case where output_shape is a nested list + if isinstance(expected_shape_data, list) and len(expected_shape_data) > 0: + # Extract the inner shape list - the first element of output_shape + shape_values = expected_shape_data[0] + + # Replace string placeholders with actual values from tensor + expected_elements = 1 + shape_dict = { + "batch_size": tensor.shape[0] if tensor.dim() > 0 else 1, + "unk__0": tensor.shape[0] if tensor.dim() > 0 else 1 + } + + # Build the expected shape with placeholders replaced + expected_shape = [] + for dim in shape_values: + if isinstance(dim, str): + if dim in shape_dict: + expected_shape.append(shape_dict[dim]) + expected_elements *= shape_dict[dim] + else: + logger.warning(f"Unknown dimension placeholder: {dim}") + # Default to using 1 for unknown dimensions + expected_shape.append(1) + expected_elements *= 1 + else: + expected_shape.append(dim) + expected_elements *= dim + + # Check total elements + tensor_elements = torch.numel(tensor) + if tensor_elements != expected_elements: + logger.warning( + f"{tensor_name} shape {list(tensor.shape)} has {tensor_elements} elements, " + f"but expected shape {expected_shape} has {expected_elements} elements" + ) + return False + + # If the tensor is flattened but should be multidimensional + if len(tensor.shape) == 1 and len(expected_shape) > 1: + logger.info( + f"{tensor_name} is flattened ({tensor.shape[0]} elements), " + f"but expected shape is {expected_shape}" + ) + return True + + # Check actual dimensions if tensor is not flattened + if len(tensor.shape) == len(expected_shape): + for i, (actual, expected) in enumerate(zip(tensor.shape, expected_shape)): + if actual != expected: + logger.warning( + f"Dimension mismatch at index {i}: {tensor_name} has size {actual}, " + f"expected {expected}" + ) + return False + return True + + # If we can't determine expected shape, just return True + logger.debug(f"Could not determine precise expected shape for {tensor_name}") + return True + + +if __name__ == "__main__": + # Choose which model to test + model_choice = 5 # Change this to test different models + + # Model configurations + base_paths = { + 1: "models/doom", + 2: "models/net", + 3: "models/resnet", + 5: "models/age", + 6: "models/version" + } + + # Get model directory + abs_path = os.path.abspath(base_paths[model_choice]) + slices_dir = os.path.join(abs_path, "slices") + input_json = os.path.join(abs_path, "input.json") + run_metadata_path = os.path.join(abs_path, "run", "metadata.json") if os.path.exists( + os.path.join(abs_path, "run", "metadata.json")) else None + + # Initialize runner (auto-generates run metadata if needed) + runner = Runner(model_path=abs_path, slices_path=slices_dir, run_metadata_path=run_metadata_path) + + # Run inference + print(f"Running inference on model {base_paths[model_choice]}...") + results = runner.run(input_json) + + # Display results + print(f"\nPrediction: {results['prediction']}") + print("Execution summary:") + for slice_id, info in results["slice_results"].items(): + print(f" {slice_id}: {info['method']}") diff --git a/src/runners/deepprove_runner.py b/src/runners/deepprove_runner.py deleted file mode 100644 index a0ebaab..0000000 --- a/src/runners/deepprove_runner.py +++ /dev/null @@ -1,58 +0,0 @@ -class DeepProveRunner: - def __init__(self, model_path): - # check env var for deepprove project path - # check if deepprove cli exists - - #if not installed, throw error - - # extract model info that is needed for running - pass - - def run_inference(self, model_path, input_path, output_path): - pass - - def run_inference_for_slice(self, model_path, layer_name, input_path, output_path): - pass - - def run_proof(self, model_path, proof_path): - pass - - def run_proof_for_slice(self, model_path, layer_name, proof_path): - pass - - def run_verification(self, model_path, proof_path, input_path, output_path): - pass - - def run_verification_for_slice(self, model_path, layer_name, proof_path, input_path, output_path): - pass - - def circuitize_model(self, model_path, output_path): - pass - - def circuitize_model_for_slice(self, model_path, layer_name, output_path): - pass - -if __name__ == "__main__": - # Choose which model to test - model_choice = 1 # Change this to test different models - - base_paths = { - 1: "models/doom", - 2: "models/net" - } - - model_dir = base_paths[model_choice] - runner = DeepProveRunner(model_dir) - - if model_choice == 1: - # run inference (mode = slice) - # run proof(mode = slice) - # run verification (mode = slice) - # circuitize model(mode = slice) - pass - elif model_choice == 2: - # run inference (mode = slice) - # run proof(mode = slice) - # run verification (mode = slice) - # circuitize model(mode = slice) - pass \ No newline at end of file diff --git a/src/runners/ezkl_runner.py b/src/runners/ezkl_runner.py deleted file mode 100644 index f99e7b3..0000000 --- a/src/runners/ezkl_runner.py +++ /dev/null @@ -1,411 +0,0 @@ -import json -import os -import subprocess -import threading -import time -from typing import Any - -import torch -from pathlib import Path - -from torch import Tensor - -from src.runners import runner_utils -from src.runners.runner_utils import RunnerUtils -from src.utils.model_utils import ModelUtils - -env = os.environ - - -class EzklRunner: - def __init__(self, model_directory: str): - # ezkl_project_path = os.environ.get('EZKL_PATH') - - # check if ezkl is installed via cli - try: - result = subprocess.run(['ezkl', '--version'], - stdout=subprocess.PIPE, - stderr=subprocess.PIPE) - if result.returncode != 0: - raise Exception("EZKL CLI not found. Please install EZKL first.") - except FileNotFoundError: - raise Exception("EZKL CLI not found. Please install EZKL first.") - - self.model_directory = model_directory - self.base_path = os.path.join(model_directory, "ezkl") - self.src_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) - - - def generate_witness(self, mode: str = None, input_file: str = None, model_path: str = None) -> dict: - if mode == "sliced": - return self.generate_witness_sliced(input_file) - else: - return self.generate_witness_whole(input_file, model_path) - - - def generate_witness_sliced(self, input_file: str = None) -> dict: - parent_dir = self.src_dir - input_path = input_file or os.path.join(parent_dir, self.model_directory, "input.json") - metadata_dir = os.path.join(parent_dir, self.model_directory, "model_slices") - - slices_dir = os.path.join(self.base_path, "slices") - - # Ensure metadata is loaded - try: - metadata = ModelUtils.load_metadata(metadata_dir) - num_segments = len(metadata['segments']) - - if num_segments == 0: - print("No segments found to process!") - return {"error": "No segments found"} - - except Exception as e: - print(f"Error loading metadata: {e}") - return {"error": str(e)} - - # Load initial input - try: - with open(Path(parent_dir, input_path), 'r') as f: - current_input = json.load(f) - except Exception as e: - print(f"Error loading input: {e}") - return {"error": str(e)} - - # Process each segment - witness_paths = {} - - for segment_idx in range(num_segments): - segment_start_time = time.time() - - # Get segment model paths - segment_name = f"segment_{segment_idx}" - segment_model_name = f"{segment_name}_model.compiled" - segment_model_path = os.path.join(parent_dir, slices_dir, segment_name, segment_model_name) - - # Create segment-specific input file - segment_input_path = os.path.join(parent_dir, slices_dir, segment_name, f"{segment_name}_input.json") - try: - with open(segment_input_path, 'w') as f: - json.dump(current_input, f) - - except Exception as e: - print(f"Error creating input file: {e}") - continue - - # Create segment-specific witness output path - segment_witness_path = os.path.join(parent_dir, slices_dir, segment_name, f"{segment_name}_witness.json") - - # Run EZKL witness generation command - cmd = [ - "ezkl", - "gen-witness", - "--compiled-circuit", segment_model_path, - "--data", segment_input_path, - "--output", segment_witness_path, - ] - - try: - process = subprocess.run( - cmd, - # capture_output=True, - cwd=str(parent_dir), - text=True, - check=True - ) - - # If successful, prepare for next segment - if segment_idx < num_segments - 1: - try: - with open(segment_witness_path, 'r') as f: - witness_data = json.load(f) - except Exception as e: - print(f"Error loading witness file: {e}") - continue - - # Extract the output data to use as input for next segment - if 'outputs' in witness_data: - # Format the outputs as input for the next segment - output_data = [[float(val) for val in - witness_data['pretty_elements']['rescaled_outputs'][0]]] - - current_input = {"input_data": output_data} - - # Print summary information about the outputs - outputs = output_data - else: - raise ValueError(f"Witness file for segment {segment_idx} does not contain rescaled_output") - - # Store witness path and timing - witness_paths[segment_idx] = segment_witness_path - - except subprocess.CalledProcessError as e: - error_msg = f"Error: {e}\nSTDOUT: {e.stdout}\nSTDERR: {e.stderr}" - raise RuntimeError(error_msg) - - final_logits = self.process_sliced_witness_output({"witness_paths": witness_paths}) - return final_logits - - def generate_witness_whole(self, input_file: str = None, model_path: str = None) -> dict: - input_file = input_file or os.path.join(self.model_directory, "input.json") - model_path = model_path or os.path.join(self.base_path, "model", "model.compiled") - witness_path = os.path.join(self.base_path, "model", "witness.json") - vk_path = os.path.join(self.base_path, "model", "vk.key") - parent_dir = self.src_dir - - subprocess.run( - [ - "ezkl", - "gen-witness", - "--data", input_file, - "--compiled-circuit", model_path, - "--output", witness_path, - "--vk-path", vk_path - ], - env=env, - cwd=str(parent_dir), - check=True - ) - - # return the processed outputs - with open(Path(parent_dir, witness_path), "r") as f: - witness_data = json.load(f) - output = self.process_witness_output(witness_data) - - return output - - - def prove(self, mode: str = None): - if mode == "sliced": - return self.prove_sliced() - else: - return self.prove_whole() - - def prove_sliced(self): - parent_dir = self.src_dir - slices_directory = os.path.join(parent_dir, self.model_directory, "ezkl", "slices") - metadata_directory = os.path.join(parent_dir, self.model_directory, "model_slices") - - segments = RunnerUtils.get_segments(metadata_directory) - num_segments = len(segments) - proof_paths = {} - - for segment_idx in range(num_segments): - segment_start_time = time.time() - - segment_name = f"segment_{segment_idx}" - segment_model_path = os.path.join(slices_directory, segment_name, f"{segment_name}_model.compiled") - segment_witness_path = os.path.join(slices_directory, segment_name, f"{segment_name}_witness.json") - segment_proof_path = os.path.join(slices_directory, segment_name, f"{segment_name}_proof.json") - segment_pk_path = os.path.join(slices_directory, segment_name, f"{segment_name}_pk.key") - - if not os.path.exists(segment_model_path): - print(f"ERROR: Model file not found at {segment_model_path}") - continue - - if not os.path.exists(segment_witness_path): - print(f"ERROR: Witness file not found at {segment_witness_path}") - continue - - # Run EZKL proof generation command - cmd = [ - "ezkl", - "prove", - "--compiled-circuit", segment_model_path, - "--witness", segment_witness_path, - "--proof-path", segment_proof_path, - "--pk-path", segment_pk_path, - ] - - try: - process = subprocess.run( - cmd, - # capture_output=True, - cwd=str(parent_dir), - text=True, - check=True - ) - - # Store proof path and timing - proof_paths[segment_idx] = segment_proof_path - - except subprocess.CalledProcessError as e: - error_msg = f"Error: {e}\nSTDOUT: {e.stdout}\nSTDERR: {e.stderr}" - raise RuntimeError(error_msg) - - results = proof_paths - return results - - def prove_whole(self): - parent_dir = self.src_dir - witness_path = os.path.join(self.base_path, "model", "witness.json") - model_path = os.path.join(self.base_path, "model", "model.compiled") - pk_path = os.path.join(self.base_path, "model", "pk.key") - proof_path = os.path.join(self.base_path, "model", "proof.json") - - subprocess.run( - [ - "ezkl", - "prove", - "--check-mode", "unsafe", - "--witness", witness_path, - "--compiled-circuit", model_path, - "--proof-path", proof_path, - "--pk-path", pk_path - ], - env=env, - cwd=str(parent_dir), - check=True - ) - - results = proof_path - return results - - def verify(self, mode: str = None) -> dict: - if mode == "sliced": - return self.verify_slices() - else: - return self.verify_whole() - - def verify_slices(self) -> dict: - parent_dir = self.src_dir - slices_directory = os.path.join(parent_dir, self.model_directory, "ezkl", "slices") - metadata_directory = os.path.join(parent_dir, self.model_directory, "model_slices") - - segments = RunnerUtils.get_segments(metadata_directory) - num_segments = len(segments) - verify_paths = {} - - for segment_idx in range(num_segments): - segment_start_time = time.time() - - segment_name = f"segment_{segment_idx}" - segment_settings_path = os.path.join(slices_directory, segment_name, f"{segment_name}_settings.json") - segment_proof_path = os.path.join(slices_directory, segment_name, f"{segment_name}_proof.json") - segment_vk_path = os.path.join(slices_directory, segment_name, f"{segment_name}_vk.key") - - - # Run EZKL proof generation command - cmd = [ - "ezkl", - "verify", - "--proof-path", segment_proof_path, - "--settings-path", segment_settings_path, - "--vk-path", segment_vk_path - ] - - try: - process = subprocess.run( - cmd, - # capture_output=True, - cwd=str(parent_dir), - text=True, - check=True - ) - - # Store proof path and timing - verify_paths[segment_idx] = segment_proof_path - - except subprocess.CalledProcessError as e: - error_msg = f"Error: {e}\nSTDOUT: {e.stdout}\nSTDERR: {e.stderr}" - raise RuntimeError(error_msg) - - results = verify_paths - return results - - def verify_whole(self) -> bool: - parent_dir = self.src_dir - settings_path = os.path.join(self.base_path, "model", "settings.json") - vk_path = os.path.join(self.base_path, "model", "vk.key") - proof_path = os.path.join(self.base_path, "model", "proof.json") - - try: - process = subprocess.run( - [ - "ezkl", - "verify", - "--proof-path", proof_path, - "--settings-path", settings_path, - "--vk-path", vk_path - ], - env=os.environ, # Use os.environ for environment variables - cwd=str(parent_dir), - check=True, - capture_output=True, - text=True - ) - - # print("✓ Proof verified successfully") - return True - except subprocess.CalledProcessError as e: - print(f"Error verifying proof: {e}") - return False - - - - def circuitize_model(self, model_path, output_path): - # TODO: Implement this - pass - - def circuitize_slices(self, model_path, layer_name, output_path): - # TODO: Implement this - pass - - @staticmethod - def process_witness_output(witness_data): - """ - Process the witness.json data to get prediction results. - """ - try: - rescaled_outputs = witness_data["pretty_elements"]["rescaled_outputs"][0] - except KeyError: - print("Error: Could not find rescaled_outputs in witness data") - return None - - # Convert string values to float and create a tensor - float_values = [float(val) for val in rescaled_outputs] - - # Create a tensor with shape [1, num_classes] to match batch_size, num_classes format - tensor_output = torch.tensor([float_values], dtype=torch.float32) - - # Process the tensor through _process_final_output (simulating one segment) - output = RunnerUtils.process_final_output(tensor_output) - return output - - @staticmethod - def process_sliced_witness_output(sliced_witness_result): - """ - Process the output from generate_witness_sliced to get prediction results. - """ - witness_paths = sliced_witness_result.get('witness_paths', {}) - - if not witness_paths: - print("Error: No witness paths found in the sliced witness result") - return None - - last_segment = max(witness_paths.keys()) - final_witness_path = witness_paths[last_segment] - - try: - with open(final_witness_path, "r") as f: - witness_data = json.load(f) - except (FileNotFoundError, json.JSONDecodeError) as e: - print(f"Error loading witness file: {e}") - return None - - return EzklRunner.process_witness_output(witness_data) - -if __name__ == "__main__": - # Choose which model to test - model_choice = 1 # Change this to test different models - - base_paths = { - 1: "models/doom", - 2: "models/net" - } - - model_dir = base_paths[model_choice] - runner = EzklRunner(model_dir) - - # run Test - result = runner.generate_witness() # change function and mode when needed - print(result) diff --git a/src/runners/onnx_runner.py b/src/runners/onnx_runner.py deleted file mode 100644 index f4c2f6c..0000000 --- a/src/runners/onnx_runner.py +++ /dev/null @@ -1,273 +0,0 @@ -import os - -import onnx -import onnxruntime as ort -import torch - -from src.runners.runner_utils import RunnerUtils -from src.utils.model_utils import ModelUtils -from onnxruntime.tools.symbolic_shape_infer import SymbolicShapeInference -from onnxruntime.tools.onnx_model_utils import optimize_model, ModelProtoWithShapeInfo -# from onnxruntime.tools.remove_initializer_from_input import remove_initializer_from_input - - -class OnnxRunner: - def __init__(self, model_directory: str, model_path: str = None): - self.device = torch.device("cpu") - self.model_directory = os.path.join(OnnxRunner._get_file_path(), model_directory) - self.model_path = os.path.join(OnnxRunner._get_file_path(), model_path) if model_path else None - - @staticmethod - def _get_file_path() -> str: - """Get the parent directory path of the current file.""" - return os.path.dirname(os.path.dirname(os.path.abspath(__file__))) - - - def preprocess_onnx_model_slices(self): - """Remove initializers from inputs in an ONNX model""" - print("Preprocessing ONNX model...") - path = self.model_directory + "/onnx_slices/model.onnx" - - model = onnx.load(path) - # model = optimize_model(self.model_path, output_path=model_path) - model = ModelProtoWithShapeInfo(path).model_with_shape_info - # model = remove_initializer_from_input(model) - onnx.save(model, path) - - def infer(self, mode: str = None, input_path: str = None) -> dict: - """ - Run inference with the ONNX model. - Args: - mode: "sliced" to run layered inference, None or any other value for whole model inference - input_path: path to the input JSON file, if None uses default input.json in model directory - Returns: - dict with inference results - """ - input_path = input_path if input_path else os.path.join(self.model_directory, "input.json") - input_tensor = RunnerUtils.preprocess_input(input_path, self.model_directory) - - if mode == "sliced": - result = self.run_layered_inference(input_tensor) - else: - result = self.run_inference(input_tensor) - - return result - - def run_layered_inference(self, input_tensor): - """ - Run inference with sliced ONNX models using a computational graph approach. - """ - try: - # Get the directory containing the sliced models - slices_directory = os.path.join(self.model_directory, "onnx_slices") - - # Load metadata - metadata = ModelUtils.load_metadata(slices_directory) - if metadata is None: - return None - - # Build computational graph - comp_graph = self.build_computational_graph(metadata) - - # Dictionary to store all intermediate outputs - intermediate_outputs = {} - - # Get segments - segments = metadata.get('segments', []) - - # Process each segment in sequence - for segment in segments: - segment_idx = segment['index'] - segment_path = segment['path'] - - # Create an ONNX Runtime session for this segment - session = ort.InferenceSession(segment_path) - - # Prepare inputs for this segment - input_feed = {} - - # Get required inputs from computational graph - for input_info in session.get_inputs(): - input_name = input_info.name - - # Skip constants/initializers - they're already in the model - if input_name in comp_graph[segment_idx]['constants']: - continue - - # Handle original input - if comp_graph[segment_idx]['inputs'].get(input_name) == "original_input": - input_feed[input_name] = input_tensor.numpy() - - # Handle intermediate outputs from previous segments - elif input_name in intermediate_outputs: - input_feed[input_name] = intermediate_outputs[input_name] - else: - print(f"Warning: Required input '{input_name}' not found in intermediate outputs") - - # Run inference on this segment - outputs = session.run(None, input_feed) - - # Store all outputs in our intermediate outputs dictionary - for i, output_info in enumerate(session.get_outputs()): - output_name = output_info.name - intermediate_outputs[output_name] = outputs[i] - - # Get the final output (from the last segment's last output) - final_segment = segments[-1] - final_output_name = final_segment['dependencies']['output'][-1] - final_output = intermediate_outputs[final_output_name] - - # Convert to PyTorch tensor and process - output_tensor = torch.tensor(final_output) - result = RunnerUtils.process_final_output(output_tensor) - return result - - except Exception as e: - print(f"Error during layered ONNX inference: {e}") - import traceback - traceback.print_exc() - return None - - # def run_layered_inference(self, input_tensor): - # """ - # Run inference with sliced ONNX models and return the logits, probabilities, and predictions. - # """ - # try: - # # Get the directory containing the sliced models - # # First try the new structure (onnx_slices) - # slices_directory = os.path.join(self.model_directory, "onnx_slices") - # - # # If that doesn't exist, try the old structure (onnx_slices directly) - # if not os.path.exists(slices_directory) or not os.path.exists(os.path.join(slices_directory, "metadata.json")): - # slices_directory = os.path.join(self.model_directory, "onnx_slices") - # - # # Get the segments this model was split into - # segments = RunnerUtils.get_segments(slices_directory) - # if segments is None: - # return None - # - # # Process each segment in sequence - # for idx, segment in enumerate(segments): - # segment_path = segment["path"] - # - # # Create an ONNX Runtime session for this segment - # session = ort.InferenceSession(segment_path) - # - # # Get the input name for the ONNX model - # input_name = session.get_inputs()[0].name - # - # # Convert PyTorch tensor to numpy array for ONNX Runtime - # input_numpy = input_tensor.numpy() - # - # # Run inference on this segment - # raw_output = session.run(None, {input_name: input_numpy}) - # - # # Convert the output back to a PyTorch tensor and use as input for next layer - # input_tensor = torch.tensor(raw_output[0]) - # - # # Process the final output - # result = RunnerUtils.process_final_output(input_tensor) - # return result - # - # except Exception as e: - # print(f"Error during layered ONNX inference: {e}") - # import traceback - # traceback.print_exc() - # return None - - def run_inference(self, input_tensor): - """ - Run inference with the ONNX model and return the logits, probabilities, and predictions. - """ - try: - # Load the ONNX model - model_path = os.path.join(self.model_directory, "model.onnx") - - # Create an ONNX Runtime session - session = ort.InferenceSession(model_path) - - # Get the input name for the ONNX model - input_name = session.get_inputs()[0].name - - # Convert PyTorch tensor to numpy array for ONNX Runtime - input_numpy = input_tensor.numpy() - - # Run inference - raw_output = session.run(None, {input_name: input_numpy}) - - # Convert the output back to a PyTorch tensor - output_tensor = torch.tensor(raw_output[0]) - - # Process the output - result = RunnerUtils.process_final_output(output_tensor) - return result - - except Exception as e: - print(f"Error during ONNX inference: {e}") - import traceback - traceback.print_exc() - return None - - @staticmethod - def build_computational_graph(metadata): - """ - Build a computational graph dictionary from metadata.json - """ - segments = metadata.get('segments', []) - comp_graph = {} - - # Dictionary to track where each tensor comes from - tensor_sources = {} - - # Process each segment - for segment in segments: - segment_idx = segment['index'] - comp_graph[segment_idx] = { - 'inputs': {}, - 'outputs': [], - 'constants': {} - } - - # Record all outputs from this segment - for output in segment['dependencies']['output']: - tensor_sources[output] = segment_idx - comp_graph[segment_idx]['outputs'].append(output) - - # Process inputs for this segment - for input_name in segment['dependencies']['input']: - # Check if this is a constant/initializer (starts with "onnx::") - if input_name.startswith("onnx::"): - # This is a constant weight/bias - comp_graph[segment_idx]['constants'][input_name] = True - # Check if this is the original model input - elif input_name == "x" or input_name == "input": - comp_graph[segment_idx]['inputs'][input_name] = "original_input" - # Otherwise, it's an intermediate tensor from a previous segment - elif input_name in tensor_sources: - source_segment = tensor_sources[input_name] - comp_graph[segment_idx]['inputs'][input_name] = source_segment - else: - print(f"Warning: Input {input_name} for segment {segment_idx} has unknown source") - - return comp_graph - - -# Example usage -if __name__ == "__main__": - - # Choose which model to test - model_choice = 1 # Change this to test different models - - base_paths = { - 1: "models/doom", - 2: "models/net", - 3: "models/resnet", - 4: "models/yolov3" - } - - model_dir = base_paths[model_choice] - model_runner = OnnxRunner(model_directory=model_dir) - model_runner.preprocess_onnx_model() - - # result = model_runner.infer(mode="sliced") # change function and mode when needed - # print(result) diff --git a/src/runners/runner_utils.py b/src/runners/runner_utils.py deleted file mode 100644 index a95e6e5..0000000 --- a/src/runners/runner_utils.py +++ /dev/null @@ -1,341 +0,0 @@ -import json -import os -import re -import subprocess -import sys -import time -from pathlib import Path -from typing import Set - -import psutil -import torch -import torch.nn.functional as F - -from src.utils.model_utils import ModelUtils - - -def get_mem_usage_cli(pid: int) -> int: - """Get memory usage (resident set size in KB) from ps.""" - if sys.platform != 'darwin': - # ps -o rss= might work on Linux, but vmmap won't - return 0 - try: - # Use full path to be sure we get the system's ps - command = ['/bin/ps', '-p', str(pid), '-o', 'rss='] - output = subprocess.check_output(command, stderr=subprocess.DEVNULL) - # Output might be empty if process died; strip whitespace before int conversion - rss_kb_str = output.decode().strip() - return int(rss_kb_str) if rss_kb_str else 0 - except subprocess.CalledProcessError: - # Process likely doesn't exist anymore - return 0 - - except Exception as e: - print(f"Error getting RSS via CLI for PID {pid}: {e}") - return 0 - - -def get_swap_usage_cli(pid: int) -> int: - """Estimate swap usage (in KB) using vmmap. Supports both legacy and modern vmmap output.""" - if sys.platform != 'darwin': - return 0 - try: - command = ['/usr/bin/sudo', '/usr/bin/vmmap', str(pid)] - - process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True) - stdout, stderr = process.communicate(timeout=5) - - if process.returncode != 0: - # print(f"[Debug Swap PID {pid}] vmmap exited with code {process.returncode}") - if stderr: - pass - # print(f"[Debug Swap PID {pid}] vmmap stderr:\n{stderr.strip()}") - return 0 - - output = stdout - - # First attempt: legacy "Swap used" - match_old = re.search(r'Swap used:\s+([\d,]+)\s*(K|KB)', output, re.IGNORECASE) - if match_old: - val = int(match_old.group(1).replace(',', '')) - return val - - # Second attempt: modern "swapped_out=" or "swapped_out =" - match_new = re.search(r'swapped[_ ]?out\s*=\s*([\d.]+)\s*([KMG])', output, re.IGNORECASE) - if match_new: - val = float(match_new.group(1)) - unit = match_new.group(2).upper() - multiplier = {'K': 1, 'M': 1024, 'G': 1024 * 1024}.get(unit, 1) - swap_kb = int(val * multiplier) - return swap_kb - - return 0 - - except Exception as e: - print(f"Error getting Swap via vmmap for PID {pid}: {e}") - return 0 - - -def monitor_subprocess_memory(parent_pid, process_name_keyword, results, stop_event): - """ - Monitors child processes of parent_pid using CLI tools (ps, vmmap). - Tracks the peak SUM of RSS ('mem_cli') and Swap ('swap_cli') across - all children matching the keyword. - If process_name_keyword is None or empty, monitors all child processes. - Updates the 'results' dictionary. - """ - peak_mem_cli = 0 - peak_swap_cli = 0 - tracked_pids: Set[int] = set() - - # Ensure the results dictionary exists and initialize keys - if not isinstance(results, dict): - return - - # Use different keys to distinguish from psutil results - results['peak_subprocess_mem'] = 0 - results['peak_subprocess_swap'] = 0 - results['peak_subprocess_total'] = 0 # Mem + Swap - - try: - parent = psutil.Process(parent_pid) - - while not stop_event.is_set(): - current_total_mem_cli = 0 - current_total_swap_cli = 0 - children_found_this_cycle = [] - try: - # Still use psutil to reliably get all descendants - children_found_this_cycle = parent.children(recursive=True) - except psutil.NoSuchProcess: - # print(f"Parent process {parent_pid} seems to have ended. Stopping CLI monitor.") - break - except Exception as e: - # print(f"Error getting children for PID {parent_pid}: {e}") - time.sleep(0.1) # Avoid tight loop on error - continue - - active_pids_this_cycle = set() - - # First pass: Identify and add new matching children - for proc in children_found_this_cycle: - try: - pid = proc.pid - active_pids_this_cycle.add(pid) # Keep track of currently running children - if pid not in tracked_pids: - # Check name only for newly found processes - if not process_name_keyword: - # Track all child processes if no keyword specified - tracked_pids.add(pid) - else: - proc_name = proc.name() - if process_name_keyword.lower() in proc_name.lower(): - tracked_pids.add(pid) - # print(f"CLI Monitoring child: {proc_name} (PID: {pid})") - - except (psutil.NoSuchProcess, psutil.AccessDenied): - # Process ended or permissions issue while checking name/pid - if pid in tracked_pids: - tracked_pids.remove(pid) # Stop tracking - continue - except Exception as e: - # print(f"Error inspecting potential child {pid}: {e}") - if pid in tracked_pids: - tracked_pids.remove(pid) # Stop tracking - continue - - # Second pass: Check memory for all currently tracked PIDs - pids_to_remove = set() - for pid in tracked_pids: - if pid not in active_pids_this_cycle: - # Process we were tracking is no longer a child (finished?) - pids_to_remove.add(pid) - continue # Don't try to get memory for it - - # Get memory and swap using CLI tools - mem_kb = get_mem_usage_cli(pid) - swap_kb = get_swap_usage_cli(pid) - - # Add to the total for this cycle - # Note: If get_mem/swap returns 0 (e.g., process just died), it adds 0 - current_total_mem_cli += mem_kb - current_total_swap_cli += swap_kb - - # Remove PIDs that are no longer active - tracked_pids.difference_update(pids_to_remove) - if pids_to_remove: - pass - # print(f"Stopped CLI tracking for finished PIDs: {pids_to_remove}") - - # Update overall peaks for the SUM of memory/swap - peak_mem_cli = max(peak_mem_cli, current_total_mem_cli) - peak_swap_cli = max(peak_swap_cli, current_total_swap_cli) - - # Break if stop signal received after checking all children this cycle - if stop_event.is_set(): - break - - # Sleep before next cycle (might need longer sleep due to CLI overhead) - time.sleep(0.1) # Increased sleep interval - - except psutil.NoSuchProcess: - print(f"Initial parent process {parent_pid} not found.") - except Exception as e: - print(f"Major error in CLI monitoring thread: {e}") - finally: - # Store final peak results (convert KB to Bytes for consistency if desired) - # Store in KB as originally retrieved - results['peak_subprocess_mem'] = peak_mem_cli - results['peak_subprocess_swap'] = peak_swap_cli - results['peak_subprocess_total'] = peak_mem_cli + peak_swap_cli - - -class RunnerUtils: - def __init__(self): - pass - - @staticmethod - def _get_file_path() -> str: - """Get the parent directory path of the current file.""" - return os.path.dirname(os.path.dirname(os.path.abspath(__file__))) - - @staticmethod - def preprocess_input(input_path:str, model_directory: str = None, save_reshape: bool = False) -> torch.Tensor: - """ - Preprocess input data from JSON. - """ - - if os.path.isfile(input_path): - with open(input_path, 'r') as f: - input_data = json.load(f) - else: - input_path = os.path.join(RunnerUtils._get_file_path(), input_path) - print( - f"Warning: Input file not found. Trying to use relative path: {input_path} instead." - ) - with open(input_path, 'r') as f: - input_data = json.load(f) - - if isinstance(input_data, dict): - if 'input_data' in input_data: - input_data = input_data['input_data'] - elif 'input' in input_data: - input_data = input_data['input'] - - # Convert to tensor - if isinstance(input_data, list): - if isinstance(input_data[0], list): - # 2D input - input_tensor = torch.tensor(input_data, dtype=torch.float32) - else: - # 1D input - input_tensor = torch.tensor([input_data], dtype=torch.float32) - else: - raise ValueError("Expected input data to be a list or nested list") - - # reshape input tensor for the model - # input_tensor = RunnerUtils.reshape(input_tensor, model_directory=model_directory) - # if save_reshape: - # ModelUtils.save_tensor_to_json(input_tensor, "input_data_reshaped.json", model_directory) - - return input_tensor - - - @staticmethod - def process_final_output(torch_tensor): - """Process the final output of the model.""" - # Apply softmax to get probabilities if not already applied - if len(torch_tensor.shape) != 2: # Ensure raw output is 2D [batch_size, num_classes] - print(f"Warning: Raw output shape {torch_tensor.shape} is not as expected. Reshaping to [1, -1].") - torch_tensor = torch_tensor.reshape(1, -1) - - probabilities = F.softmax(torch_tensor, dim=1) - predicted_action = torch.argmax(probabilities, dim=1).item() - - result = { - "logits": torch_tensor, - "probabilities": probabilities, - "predicted_action": predicted_action - } - - return result - - @staticmethod - def needs_reshape(input_tensor, model_directory: str = None) -> bool: - """Check if tensor needs reshaping based on dimensions and model type""" - if input_tensor.dim() != 2: - return False - if input_tensor.size(1) == 3136 and model_directory and 'doom' in model_directory.lower(): - return True - if input_tensor.size(1) == 3072: - return True - return False - - @staticmethod - def reshape(input_tensor, model_directory: str = None): - # TODO: remove hardcoding to doom and net - if input_tensor.dim() == 1: - input_tensor = input_tensor.unsqueeze(0) - - if input_tensor.dim() == 2 and input_tensor.size(1) == 3136 and 'doom' in model_directory.lower(): - input_tensor = input_tensor.reshape(1, 4, 28, 28) - elif input_tensor.dim() == 2 and input_tensor.size(1) == 3072: - if input_tensor.size(0) == 1: - # Single sample case - input_tensor = input_tensor.reshape(1, 3, 32, 32) - else: - # Multiple samples case (e.g., batch size 100) - print(f"Processing only the first sample out of {input_tensor.size(0)}") - input_tensor = input_tensor[0:1].reshape(1, 3, 32, 32) - else: - raise ValueError(f"Input tensor has unsupported dimensions: {input_tensor.shape}") - - return input_tensor - - @staticmethod - def get_segments(slices_directory): - metadata = ModelUtils.load_metadata(slices_directory) - if metadata is None: - return None - - segments = metadata.get('segments', []) - if not segments: - print("No segments found in metadata.json") - return None - - return segments - - @staticmethod - def save_to_file_shaped(input_tensor: torch.Tensor, file_path: str): - # Convert tensor to list - tensor_data = input_tensor.tolist() - - # Create directory if it doesn't exist - os.makedirs(os.path.dirname(file_path), exist_ok=True) - - # Save tensor data as JSON - data = { - "input": tensor_data - } - with open(file_path, 'w') as f: - json.dump(data, f) - - @staticmethod - def save_to_file_flattened(input_tensor: torch.Tensor, file_path: str): - # Flatten and convert tensor to list - tensor_data = input_tensor.flatten().tolist() - - # Create directory if it doesn't exist - os.makedirs(os.path.dirname(file_path), exist_ok=True) - - # Save flattened tensor data as JSON - data = { - "input_data": [tensor_data] - } - - with open(file_path, 'w') as f: - json.dump(data, f) - - -if __name__ == "__main__": - print(f"Parent path: {RunnerUtils.get_file_path()}") diff --git a/src/slicer.py b/src/slicer.py new file mode 100644 index 0000000..f401bda --- /dev/null +++ b/src/slicer.py @@ -0,0 +1,136 @@ +""" +Slicer orchestrator module. + +This module provides a unified interface for slicing models of different types. +It orchestrates the slicing process by delegating to the appropriate slicer implementation +based on the model type. +""" + +import os +import logging +from typing import Optional + +from src.utils.slicer_utils.onnx_slicer import OnnxSlicer +# Import ModelSlicer for future use +# from src.slicers.model_slicer import ModelSlicer + +logger = logging.getLogger(__name__) + +class Slicer: + """ + Orchestrator class for slicing models of different types. + + This class provides a unified interface for slicing models by delegating + to the appropriate slicer implementation based on the model type. + """ + + @staticmethod + def create(model_path: str, save_path: Optional[str] = None) -> 'Slicer': + """ + Factory method to create a Slicer instance based on the model type. + + Args: + model_path: Path to the model file or directory + save_path: Optional path to save the model analysis + + Returns: + A Slicer instance + + Raises: + ValueError: If the model type is not supported + """ + # Check if the path is a file or directory + if os.path.isfile(model_path): + model_file = model_path + model_dir = os.path.dirname(model_path) + if not model_dir: # If the directory is empty (e.g., just "model.onnx") + model_dir = "." + else: + model_dir = model_path + model_file = None + + # Determine model type + is_onnx = False + + # Check if it's an ONNX model + if model_file and model_file.lower().endswith('.onnx'): + is_onnx = True + elif os.path.exists(os.path.join(model_dir, "model.onnx")): + is_onnx = True + model_file = os.path.join(model_dir, "model.onnx") + + # Create appropriate slicer + if is_onnx: + logger.info(f"Creating ONNX slicer for model: {model_file}") + return Slicer(OnnxSlicer(model_file, save_path)) + else: + # For now, we only support ONNX models as per requirements + # In the future, this can be extended to support other model types + raise ValueError(f"Unsupported model type at path: {model_path}") + + def __init__(self, slicer_impl): + """ + Initialize the Slicer with a specific implementation. + + Args: + slicer_impl: The slicer implementation to use + """ + self.slicer_impl = slicer_impl + + def slice_model(self, output_path: Optional[str] = None, **kwargs): + """ + Slice the model using the appropriate slicer implementation. + + Args: + output_path: Directory to save the sliced model + **kwargs: Additional arguments to pass to the slicer implementation + + Returns: + The result of the slicing operation + """ + logger.info(f"Slicing model to output path: {output_path}") + return self.slicer_impl.slice_model(output_path=output_path) + + +if __name__ == "__main__": + # Choose which model to test + model_choice = 5 # Change this to test different models + + # Model configurations + base_paths = { + 1: "models/doom", + 2: "models/net", + 3: "models/resnet", + 5: "models/age", + 6: "models/version" + } + + # Resolve paths + abs_path = os.path.abspath(base_paths[model_choice]) + model_file = os.path.join(abs_path, "model.onnx") + output_dir = os.path.join(abs_path, "slices") + + try: + # Initialize slicer via orchestrator (auto-selects ONNX slicer) + slicer = Slicer.create(model_path=model_file, save_path=abs_path) + + # Run slicing + print(f"Slicing model at {model_file} to {output_dir}...") + slices = slicer.slice_model(output_path=output_dir) + + # Display results + print("\nSlicing completed!") + if isinstance(slices, list): + print(f"Created {len(slices)} segments.") + # Optionally display first few slice paths + preview = slices[:] + if preview: + print("Sample slice files:") + for p in preview: + print(f" {p}") + else: + print("Slicing returned no slice list. Check logs for details.") + + except Exception as e: + print(f"Error during slicing: {e}") + diff --git a/src/templates/report_template.html b/src/templates/report_template.html deleted file mode 100644 index bafca24..0000000 --- a/src/templates/report_template.html +++ /dev/null @@ -1,848 +0,0 @@ - - - - - - Neural Network Visualization - - - -
-
-
-

Neural Network Visualizer

-

Interactive model architecture visualization and analysis

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- ☀️ -
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Model Summary

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{{ total_params }}
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Total Parameters
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Input Size
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Model Structure

- - - - - - - - - - - - - -
Architecture Type{{ model_type_display }}
Number of Layers{{ total_layers }}
Activation Functions{{ activation_functions|join(", ") }}
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Network Architecture Visualization

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- {% if is_scrollable %} -
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- Neural Network Visualization -
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Scroll horizontally to view the complete network →

- {% else %} - Neural Network Visualization - {% endif %} -
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Layer Details

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- {% for layer in layer_stats %} -
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- {{ layer.name }} - - - -
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- - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Type{{ layer.type }}
Input Features{{ layer.in_features }}
Output Features{{ layer.out_features }}
Activation{{ layer.activation }}
Weights{{ layer.weights_count }}
Biases{{ layer.biases_count }}
Total Parameters{{ layer.params_count }}
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Model Details

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- - - - - Generated with ModelVisualizer | {{ date }} -

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- - - - \ No newline at end of file diff --git a/src/utils/model_generator.py b/src/utils/model_generator.py deleted file mode 100644 index e3cb660..0000000 --- a/src/utils/model_generator.py +++ /dev/null @@ -1,383 +0,0 @@ -import json -import os -import torch - -def create_test_model(model_dir="models/test_model", ): - """ - Creates a test model with predefined state dictionaries and configuration settings, storing - them in the specified directory. The function generates random weights for each layer and - creates a JSON configuration file detailing the architecture of the model. It ensures the - specified directory structure exists and writes both the model and configuration to disk. - - :param model_dir: Directory path where the test model and its configuration are saved. - Default is "models/test_model". - If the directory does not exist, it is created. - :type model_dir: str - :return: A tuple containing the file paths of the saved model and its configuration. - :rtype: tuple - """ - - os.makedirs(os.path.join(os.getcwd(), model_dir), exist_ok=True) - - model_path = os.path.join(model_dir, "test_model.pth") - config_path = os.path.join(model_dir, "test_config.json") - - state_dict = { - 'layer1.weight': torch.randn(10, 5), - 'layer2.weight': torch.randn(20, 10), - 'layer3.weight': torch.randn(15, 20), - 'layer4.weight': torch.randn(25, 15), - 'layer5.weight': torch.randn(30, 25), - 'layer6.weight': torch.randn(18, 30), - 'layer7.weight': torch.randn(12, 18), - 'layer8.weight': torch.randn(22, 12), - 'layer9.weight': torch.randn(16, 22), - 'layer10.weight': torch.randn(14, 16), - 'layer11.weight': torch.randn(28, 14), - 'layer12.weight': torch.randn(32, 28), - 'layer13.weight': torch.randn(19, 32), - 'layer14.weight': torch.randn(2, 19) - } - - config = { - "model_type": "test", - "layers": { - "input": {"size": 5, "type": "input"}, - "layer1": {"size": 10, "activation": "ReLU"}, - "layer2": {"size": 20, "activation": "Tanh"}, - "layer3": {"size": 15, "activation": "Sigmoid"}, - "layer4": {"size": 25, "activation": "LeakyReLU"}, - "layer5": {"size": 30, "activation": "ELU"}, - "layer6": {"size": 18, "activation": "PReLU"}, - "layer7": {"size": 12, "activation": "ReLU"}, - "layer8": {"size": 22, "activation": "Tanh"}, - "layer9": {"size": 16, "activation": "Softplus"}, - "layer10": {"size": 14, "activation": "Softsign"}, - "layer11": {"size": 28, "activation": "ReLU"}, - "layer12": {"size": 32, "activation": "Tanh"}, - "layer13": {"size": 19, "activation": "ReLU"}, - "layer14": {"size": 2, "activation": "Softmax"} - } - } - - print(f"Creating test model in {model_dir}/...") - torch.save(state_dict, model_path) - - with open(config_path, 'w') as f: - json.dump(config, f, indent=2) - - print("Test model and config files created successfully") - - return model_path, config_path - -def create_test_model_with_embedded_activations(model_dir="models/test_model_embedded"): - """ - Creates a test model with predefined weights and activations embedded in its configuration. - - This function generates a model file containing: - 1. A state dictionary with random weights for the model layers. - 2. A configuration dictionary that defines the model type, layer structure, and corresponding - activation functions for each layer. - - The generated file is saved at the specified directory path with the filename `test_model_embedded.pth`. - The directory path is created if it does not exist. - - :param model_dir: Directory path to save the test model file (string). Defaults to "models/test_model_embedded". - :return: Full file path to the saved test model file (string). - """ - - os.makedirs(os.path.join(os.getcwd(), model_dir), exist_ok=True) - - model_path = os.path.join(model_dir, "test_model_embedded.pth") - - # State dictionary for the model weights - state_dict = { - 'layer1.weight': torch.randn(10, 5), - 'layer2.weight': torch.randn(20, 10), - 'layer3.weight': torch.randn(15, 20), - 'layer4.weight': torch.randn(25, 15), - 'layer5.weight': torch.randn(30, 25), - 'layer6.weight': torch.randn(18, 30), - 'layer7.weight': torch.randn(12, 18), - 'layer8.weight': torch.randn(22, 12), - 'layer9.weight': torch.randn(16, 22), - 'layer10.weight': torch.randn(14, 16), - 'layer11.weight': torch.randn(28, 14), - 'layer12.weight': torch.randn(32, 28), - 'layer13.weight': torch.randn(19, 32), - 'layer14.weight': torch.randn(2, 19) - } - - # Configuration with activation details - config = { - "model_type": "test", - "layers": { - "input": {"size": 5, "type": "input"}, - "layer1": {"size": 10, "activation": "ReLU"}, - "layer2": {"size": 20, "activation": "Tanh"}, - "layer3": {"size": 15, "activation": "Sigmoid"}, - "layer4": {"size": 25, "activation": "LeakyReLU"}, - "layer5": {"size": 30, "activation": "ELU"}, - "layer6": {"size": 18, "activation": "PReLU"}, - "layer7": {"size": 12, "activation": "ReLU"}, - "layer8": {"size": 22, "activation": "Tanh"}, - "layer9": {"size": 16, "activation": "Softplus"}, - "layer10": {"size": 14, "activation": "Softsign"}, - "layer11": {"size": 28, "activation": "ReLU"}, - "layer12": {"size": 32, "activation": "Tanh"}, - "layer13": {"size": 19, "activation": "ReLU"}, - "layer14": {"size": 2, "activation": "Softmax"} - } - } - - # Combine state_dict and config so everything is in a single file - combined_dict = { - 'state_dict': state_dict, - 'config': config - } - - # Save model weights and config together - print(f"Creating test model (with embedded activations) in {model_dir}/...") - torch.save(combined_dict, model_path) - print("Test model file (with activations embedded) created successfully.") - - return model_path - -def create_test_model_with_biases(model_dir="models/test_model_with_biases"): - """ - Creates a test model with specified layer weights, biases, and configuration file. This function is designed - to generate a dummy model structure saved in `pth` format and a corresponding configuration saved in JSON - format. The model and configuration files are stored under the specified directory. If the directory does - not exist, it will be created. - - :param model_dir: Path to the directory where the test model and its configuration will be saved. - Default is "models/test_model_with_biases". - :type model_dir: str - :return: A tuple containing the paths to the saved model file ("test_model.pth") and configuration file - ("test_config.json"). - :rtype: Tuple[str, str] - """ - - os.makedirs(os.path.join(os.getcwd(), model_dir), exist_ok=True) - - model_path = os.path.join(model_dir, "test_model.pth") - config_path = os.path.join(model_dir, "test_config.json") - - # Define layer dimensions for clarity - layer_dims = [ - (5, 10), # layer1: 5 input, 10 output - (10, 20), # layer2: 10 input, 20 output - (20, 15), # layer3: 20 input, 15 output - (15, 25), # layer4: 15 input, 25 output - (25, 30), # layer5: 25 input, 30 output - (30, 18), # layer6: 30 input, 18 output - (18, 12), # layer7: 18 input, 12 output - (12, 22), # layer8: 12 input, 22 output - (22, 16), # layer9: 22 input, 16 output - (16, 14), # layer10: 16 input, 14 output - (14, 28), # layer11: 14 input, 28 output - (28, 32), # layer12: 28 input, 32 output - (32, 19), # layer13: 32 input, 19 output - (19, 2), # layer14: 19 input, 2 output - ] - - state_dict = {} - - # Create weights and biases for each layer - for i, (in_size, out_size) in enumerate(layer_dims, 1): - # Create weights - state_dict[f'layer{i}.weight'] = torch.randn(out_size, in_size) - # Create biases - state_dict[f'layer{i}.bias'] = torch.randn(out_size) - - config = { - "model_type": "test", - "layers": { - "input": {"size": 5, "type": "input"}, - "layer1": {"size": 10, "activation": "ReLU", "in_features": 5, "out_features": 10}, - "layer2": {"size": 20, "activation": "Tanh", "in_features": 10, "out_features": 20}, - "layer3": {"size": 15, "activation": "Sigmoid", "in_features": 20, "out_features": 15}, - "layer4": {"size": 25, "activation": "LeakyReLU", "in_features": 15, "out_features": 25}, - "layer5": {"size": 30, "activation": "ELU", "in_features": 25, "out_features": 30}, - "layer6": {"size": 18, "activation": "PReLU", "in_features": 30, "out_features": 18}, - "layer7": {"size": 12, "activation": "ReLU", "in_features": 18, "out_features": 12}, - "layer8": {"size": 22, "activation": "Tanh", "in_features": 12, "out_features": 22}, - "layer9": {"size": 16, "activation": "Softplus", "in_features": 22, "out_features": 16}, - "layer10": {"size": 14, "activation": "Softsign", "in_features": 16, "out_features": 14}, - "layer11": {"size": 28, "activation": "ReLU", "in_features": 14, "out_features": 28}, - "layer12": {"size": 32, "activation": "Tanh", "in_features": 28, "out_features": 32}, - "layer13": {"size": 19, "activation": "ReLU", "in_features": 32, "out_features": 19}, - "layer14": {"size": 2, "activation": "Softmax", "in_features": 19, "out_features": 2} - } - } - - print(f"Creating test model in {model_dir}/...") - torch.save(state_dict, model_path) - - with open(config_path, 'w') as f: - json.dump(config, f, indent=2) - - print("Test model and config files created successfully") - - return model_path, config_path - -def create_test_cnn_model_with_biases(model_dir="models/test_cnn_model_with_biases"): - """ - Creates a test Convolutional Neural Network (CNN) model along with its weights and - configuration file. This function generates a synthetic CNN model with predefined - architecture, saving the model's state dictionary and configuration to the specified - directory. The CNN consists of convolutional, max-pooling, and fully connected layers. - - :param model_dir: Directory where the CNN model and its configuration will be saved. - Defaults to 'models/test_cnn_model_with_biases'. - :type model_dir: str - - :return: A tuple containing the paths to the saved model file and configuration file. - :rtype: tuple - """ - - import os - import json - import torch - - os.makedirs(os.path.join(os.getcwd(), model_dir), exist_ok=True) - - model_path = os.path.join(model_dir, "test_cnn_model.pth") - config_path = os.path.join(model_dir, "test_cnn_config.json") - - # Define CNN architecture - # Format: (layer_type, in_channels/features, out_channels/features, kernel_size, stride, padding) - cnn_architecture = [ - # Convolutional layers - ("conv", 3, 16, 3, 1, 1), # conv1: 3 input channels, 16 output channels - ("pool", 2, 2, 0), # max_pool1: kernel_size=2, stride=2 - ("conv", 16, 32, 3, 1, 1), # conv2: 16 input channels, 32 output channels - ("pool", 2, 2, 0), # max_pool2: kernel_size=2, stride=2 - ("conv", 32, 64, 3, 1, 1), # conv3: 32 input channels, 64 output channels - ("pool", 2, 2, 0), # max_pool3: kernel_size=2, stride=2 - ("conv", 64, 128, 3, 1, 1), # conv4: 64 input channels, 128 output channels - # Assume input size was 32x32, after 3 pooling layers it's now 4x4 - # Fully connected layers - ("fc", 128 * 4 * 4, 512), # fc1: flattened conv output to 512 neurons - ("fc", 512, 256), # fc2: 512 to 256 neurons - ("fc", 256, 128), # fc3: 256 to 128 neurons - ("fc", 128, 10), # fc4: 128 to 10 output classes - ] - - state_dict = {} - config = { - "model_type": "cnn", - "input_shape": [3, 32, 32], # Channels, Height, Width - "layers": { - "input": {"channels": 3, "height": 32, "width": 32, "type": "input"} - } - } - - # Create weights and biases for each layer - for i, layer_info in enumerate(cnn_architecture, 1): - if layer_info[0] == "conv": - _, in_channels, out_channels, kernel_size, stride, padding = layer_info - - # Create weights and biases for conv layers - state_dict[f'conv{i}.weight'] = torch.randn(out_channels, in_channels, kernel_size, kernel_size) - state_dict[f'conv{i}.bias'] = torch.randn(out_channels) - - # Add to config - config["layers"][f"conv{i}"] = { - "type": "conv2d", - "in_channels": in_channels, - "out_channels": out_channels, - "kernel_size": kernel_size, - "stride": stride, - "padding": padding, - "activation": "ReLU" - } - - elif layer_info[0] == "pool": - _, kernel_size, stride, padding = layer_info - - # No weights for pooling layers - config["layers"][f"pool{i}"] = { - "type": "maxpool2d", - "kernel_size": kernel_size, - "stride": stride, - "padding": padding - } - - elif layer_info[0] == "fc": - _, in_features, out_features = layer_info - - # Create weights and biases for fully connected layers - state_dict[f'fc{i}.weight'] = torch.randn(out_features, in_features) - state_dict[f'fc{i}.bias'] = torch.randn(out_features) - - # Add to config - activation = "Softmax" if i == len(cnn_architecture) else "ReLU" # Last layer gets Softmax - config["layers"][f"fc{i}"] = { - "type": "linear", - "in_features": in_features, - "out_features": out_features, - "activation": activation - } - - print(f"Creating test CNN model in {model_dir}/...") - torch.save(state_dict, model_path) - - with open(config_path, 'w') as f: - json.dump(config, f, indent=2) - - print("Test CNN model and config files created successfully") - - return model_path, config_path - -def generate_input(shape, output_file="input.json", batch_size=1): - """ - Takes an input shape and generates an input.json file which can be used to run models. - - :param shape: Input shape for the model (e.g., [3, 224, 224] or [4, 28, 28]) - :type shape: list - :param output_file: Name of the output JSON file. Default is "input.json" - :type output_file: str - :param batch_size: Batch size to use for inference. Default is 1 - :type batch_size: int - :return: Path to the generated input file - :rtype: str - """ - import numpy as np - - # Add batch dimension to the shape if not already present - if isinstance(shape, list) and len(shape) >= 1: - # Create full input shape with batch dimension - full_shape = [batch_size] + shape - - # Generate random input data matching the shape - # Using normal distribution with mean=0, std=1 (typical for model inputs) - input_data = np.random.randn(*full_shape).astype(np.float32) - - # Create the input dictionary - input_dict = { - "input": input_data.tolist(), # Convert numpy array to list for JSON serialization - "shape": full_shape, - "dtype": "float32", - "batch_size": batch_size - } - - # Write to JSON file - with open(output_file, 'w') as f: - json.dump(input_dict, f, indent=2) - - print(f"Generated input file: {output_file}") - print(f"Input shape: {full_shape}") - print(f"Data type: float32") - - return output_file - else: - raise ValueError("Shape must be a list with at least one dimension") - -if __name__ == "__main__": - # generate model - # create_test_model() - # create_test_model_with_embedded_activations() - # create_test_model_with_biases() - # create_test_cnn_model_with_biases() - generate_input([3, 224, 224], output_file="input.json", batch_size=1) diff --git a/src/utils/onnx_utils.py b/src/utils/onnx_utils.py deleted file mode 100644 index 40238f9..0000000 --- a/src/utils/onnx_utils.py +++ /dev/null @@ -1,128 +0,0 @@ -import os -import json -import onnx -from onnx import shape_inference - -class OnnxUtils: - """ - Utility functions for working with ONNX models. - """ - - @staticmethod - def save_metadata_file(metadata, output_dir, filename="metadata.json"): - """ - Save metadata to a JSON file. - - Args: - metadata: Dictionary containing metadata - output_dir: Directory where the metadata will be saved - filename: Name of the metadata file (default: "metadata.json") - """ - metadata_path = os.path.join(output_dir, filename) - with open(metadata_path, 'w') as f: - json.dump(metadata, f, indent=4) - - @staticmethod - def create_node_model(node, inputs, outputs, initializers): - """ - Create a minimal ONNX model containing a single node. - - Args: - node: ONNX node to include in the model - inputs: List of input tensor value infos - outputs: List of output tensor value infos - initializers: List of initializers needed by the node - - Returns: - onnx.ModelProto: ONNX model containing the node - """ - # Create a graph with the node - graph = onnx.helper.make_graph( - [node], # single node - f"{node.name}_graph", - inputs, - outputs, - initializers - ) - - # Create a model from the graph - model = onnx.helper.make_model(graph) - - return model - - @staticmethod - def infer_shapes(model): - """ - Infer shapes for an ONNX model. - - Args: - model: ONNX model - - Returns: - onnx.ModelProto: ONNX model with inferred shapes - """ - try: - inferred_model = shape_inference.infer_shapes(model) - return inferred_model - except Exception as e: - print(f"Warning: Shape inference failed: {e}") - return model - - @staticmethod - def save_model(model, path): - """ - Save an ONNX model to a file. - - Args: - model: ONNX model - path: Path where the model will be saved - """ - onnx.save(model, path) - - @staticmethod - def get_tensor_value_info(name, dtype, shape): - """ - Create a tensor value info. - - Args: - name: Name of the tensor - dtype: Data type of the tensor (e.g., onnx.TensorProto.FLOAT) - shape: Shape of the tensor - - Returns: - onnx.ValueInfoProto: Tensor value info - """ - return onnx.helper.make_tensor_value_info(name, dtype, shape) - - @staticmethod - def extract_shape_from_value_info(value_info): - """ - Extract shape from a value info. - - Args: - value_info: ONNX value info - - Returns: - list: Shape of the tensor - """ - shape = [] - if value_info.type.tensor_type.shape.dim: - for dim in value_info.type.tensor_type.shape.dim: - if dim.dim_param: - shape.append(dim.dim_param) - else: - shape.append(dim.dim_value if dim.dim_value != 0 else None) - return shape - - @staticmethod - def get_output_dir_for_slices(onnx_path): - """ - Get the output directory for sliced models. - - Args: - onnx_path: Path to the original ONNX model - - Returns: - str: Path to the output directory - """ - return os.path.join(os.path.dirname(onnx_path), "onnx_slices") \ No newline at end of file diff --git a/test/__init__.py b/src/utils/runner_utils/__init__.py similarity index 100% rename from test/__init__.py rename to src/utils/runner_utils/__init__.py diff --git a/src/utils/runner_utils/runner_utils.py b/src/utils/runner_utils/runner_utils.py new file mode 100644 index 0000000..bbd8727 --- /dev/null +++ b/src/utils/runner_utils/runner_utils.py @@ -0,0 +1,126 @@ +import json +import os +import torch +import torch.nn.functional as F + +from src.utils.model_utils import ModelUtils + +class RunnerUtils: + def __init__(self): + pass + + @staticmethod + def _get_file_path() -> str: + """Get the parent directory path of the current file.""" + return os.path.dirname(os.path.dirname(os.path.abspath(__file__))) + + @staticmethod + def preprocess_input(input_path:str, model_directory: str = None, save_reshape: bool = False) -> torch.Tensor: + """ + Preprocess input data from JSON. + """ + + if os.path.isfile(input_path): + with open(input_path, 'r') as f: + input_data = json.load(f) + else: + input_path = os.path.join(RunnerUtils._get_file_path(), input_path) + print( + f"Warning: Input file not found. Trying to use relative path: {input_path} instead." + ) + with open(input_path, 'r') as f: + input_data = json.load(f) + + if isinstance(input_data, dict): + if 'input_data' in input_data: + input_data = input_data['input_data'] + elif 'input' in input_data: + input_data = input_data['input'] + + # Convert to tensor + if isinstance(input_data, list): + if isinstance(input_data[0], list): + # 2D input + input_tensor = torch.tensor(input_data, dtype=torch.float32) + else: + # 1D input + input_tensor = torch.tensor([input_data], dtype=torch.float32) + else: + raise ValueError("Expected input data to be a list or nested list") + + # reshape input tensor for the model + # input_tensor = RunnerUtils.reshape(input_tensor, model_directory=model_directory) + # if save_reshape: + # ModelUtils.save_tensor_to_json(input_tensor, "input_data_reshaped.json", model_directory) + + return input_tensor + + + @staticmethod + def process_final_output(torch_tensor): + """Process the final output of the model.""" + # Apply softmax to get probabilities if not already applied + if len(torch_tensor.shape) != 2: # Ensure raw output is 2D [batch_size, num_classes] + print(f"Warning: Raw output shape {torch_tensor.shape} is not as expected. Reshaping to [1, -1].") + torch_tensor = torch_tensor.reshape(1, -1) + + probabilities = F.softmax(torch_tensor, dim=1) + predicted_action = torch.argmax(probabilities, dim=1).item() + + result = { + "logits": torch_tensor, + "probabilities": probabilities, + "predicted_action": predicted_action + } + + return result + + @staticmethod + def get_segments(slices_directory): + metadata = ModelUtils.load_metadata(slices_directory) + if metadata is None: + return None + + segments = metadata.get('segments', []) + if not segments: + print("No segments found in metadata.json") + return None + + return segments + + @staticmethod + def save_to_file_shaped(input_tensor: torch.Tensor, file_path: str): + # Convert tensor to list + tensor_data = input_tensor.tolist() + + # Create directory if it doesn't exist + os.makedirs(os.path.dirname(file_path), exist_ok=True) + + # Save tensor data as JSON + data = { + "input": tensor_data + } + with open(file_path, 'w') as f: + json.dump(data, f) + + @staticmethod + def save_to_file_flattened(input_tensor: torch.Tensor, file_path: str): + # Flatten and convert tensor to list + tensor_data = input_tensor.flatten().tolist() + + # Create directory if it doesn't exist + file_dir = os.path.dirname(file_path) + if file_dir: # Only create directory if path has a directory component + os.makedirs(file_dir, exist_ok=True) + + # Save flattened tensor data as JSON + data = { + "input_data": [tensor_data] + } + + with open(file_path, 'w') as f: + json.dump(data, f) + + +if __name__ == "__main__": + print(f"Parent path: {RunnerUtils.get_file_path()}") diff --git a/src/utils/slicer_utils/__init__.py b/src/utils/slicer_utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/model_slicer.py b/src/utils/slicer_utils/model_slicer.py similarity index 99% rename from src/model_slicer.py rename to src/utils/slicer_utils/model_slicer.py index 09d340c..74993c4 100644 --- a/src/model_slicer.py +++ b/src/utils/slicer_utils/model_slicer.py @@ -6,7 +6,6 @@ import torch import enum -from src.utils.model_analyzer import ModelAnalyzer from src.utils.model_utils import ModelUtils diff --git a/src/utils/slicer_utils/onnx_slicer.py b/src/utils/slicer_utils/onnx_slicer.py new file mode 100644 index 0000000..000d78a --- /dev/null +++ b/src/utils/slicer_utils/onnx_slicer.py @@ -0,0 +1,759 @@ +import os.path +import onnx +import logging +from src.analyzers.onnx_analyzer import OnnxAnalyzer +from typing import List, Dict, Tuple +from src.utils.utils import Utils +from onnx.utils import extract_model +from onnx import shape_inference + +# Configure logger +logger = logging.getLogger(__name__) + + +class OnnxSlicer: + # Control-input specification: op_type -> {input_index: (symbolic_shape, label)} + CONTROL_INPUT_SPECS = { + "Unsqueeze": {1: (["M"], "axes")}, + "Reshape": {1: (["M"], "shape")}, + "Slice": {1: (["M"], "starts"), 2: (["M"], "ends"), 3: (["M"], "axes"), 4: (["M"], "steps")}, + "TopK": {1: ([], "k")}, + "Gather": {1: (["M"], "indices")}, + "ReduceMean": {1: (["M"], "axes")}, + "ReduceSum": {1: (["M"], "axes")}, + "Pad": {1: (["M"], "pads")}, + "Expand": {1: (["M"], "shape")}, + } + def __init__(self, onnx_path, save_path=None): + self.onnx_path = onnx_path + self.onnx_model = onnx.load(onnx_path) + self.model_metadata = None + self.slice_points = None + + self.onnx_analyzer = OnnxAnalyzer(self.onnx_path) + self.analysis = self.onnx_analyzer.analyze(save_path=save_path) + + def determine_slice_points(self, model_metadata) -> List[int]: + """ + Determine the slice points for the model based on nodes with parameter_details in the model_metadata. + + Args: + model_metadata: The model analysis metadata containing node information. + + Returns: + List[int]: List of indices representing nodes with parameter details + """ + # Find nodes with parameter_details in model_metadata + slice_points = [] + for node_name, node_info in model_metadata["nodes"].items(): + if node_info.get("parameter_details") and node_info["parameter_details"]: + slice_points.append(node_info["index"]) + + # Sort slice points by index + slice_points.sort() + + self.slice_points = slice_points + return slice_points + + def _slice_setup(self, model_metadata, output_path=None): + """ + Set up the necessary data structures for slicing. + + Args: + model_metadata: The model analysis metadata containing node information + + Returns: + tuple: (graph, node_map, node_type_index_map, initializer_map, value_info_map, + index_to_node_name, index_to_segment_name, output_dir) + """ + # Create output directory + output_path = os.path.join(os.path.dirname(self.onnx_path), "slices") if output_path is None else output_path + if not os.path.exists(output_path): + os.makedirs(output_path, exist_ok=True) + + # Get the graph from the ONNX model + graph = self.onnx_model.graph + + # Create maps for node lookup + node_map = {node.name: node for node in graph.node} + + # Also create a map with just the op_type and index to handle name mismatches + node_type_index_map = {} + for i, node in enumerate(graph.node): + key = f"{node.op_type}_{i}" + node_type_index_map[key] = node + + initializer_map = {init.name: init for init in graph.initializer} + value_info_map = {vi.name: vi for vi in graph.value_info} + value_info_map.update({vi.name: vi for vi in graph.input}) + value_info_map.update({vi.name: vi for vi in graph.output}) + + # Create a map of node indices to node names + index_to_node_name = {} + index_to_segment_name = {} + for node_name, node_info in model_metadata["nodes"].items(): + index_to_node_name[node_info["index"]] = node_name + index_to_segment_name[node_info["index"]] = node_info["segment_name"] + + return (graph, node_map, node_type_index_map, initializer_map, value_info_map, + index_to_node_name, index_to_segment_name, output_path) + + @staticmethod + def _get_nodes(start_idx, end_idx, index_to_node_name, index_to_segment_name, node_map, node_type_index_map, + segment_idx): + """ + Collect nodes for a specific slice. + + Args: + start_idx: Start index of the slice + end_idx: End index of the slice + index_to_node_name: Map of node indices to node names + index_to_segment_name: Map of node indices to segment names + node_map: Map of node names to nodes + node_type_index_map: Map of node type and index to nodes + segment_idx: Index of the current segment + + Returns: + list: List of nodes for this slice + """ + segment_nodes = [] + for idx in range(start_idx, end_idx): + if idx in index_to_node_name: + node_name = index_to_node_name[idx] + if node_name in node_map: + segment_nodes.append(node_map[node_name]) + else: + # Try to find the node using segment name (op_type_index) + segment_name = index_to_segment_name.get(idx) + if segment_name in node_type_index_map: + segment_nodes.append(node_type_index_map[segment_name]) + else: + logger.warning(f"Node {node_name} (index {idx}) not found in the ONNX model") + + # Skip if no nodes in this slice + if not segment_nodes: + logger.warning(f"No nodes found for segment {segment_idx} (indices {start_idx}-{end_idx - 1})") + + return segment_nodes + + @staticmethod + def _get_segment_details(segment_nodes, graph, initializer_map): + """ + Determine inputs, outputs, and initializers for a segment. + + Args: + segment_nodes: List of nodes in the segment + graph: ONNX graph + value_info_map: Map of value info names to value infos + initializer_map: Map of initializer names to initializers + + Returns: + tuple: (segment_inputs, segment_outputs, segment_initializers) + """ + segment_inputs = [] + segment_outputs = [] + segment_initializers = [] + + # Build a complete map of all value infos including intermediate outputs + all_value_infos = {} + + # Add model inputs + for input_info in graph.input: + all_value_infos[input_info.name] = input_info + + # Add model outputs + for output_info in graph.output: + all_value_infos[output_info.name] = output_info + + # Add any intermediate value infos + for value_info in graph.value_info: + all_value_infos[value_info.name] = value_info + + # Get all outputs from nodes in this segment + segment_node_outputs = set() + for node in segment_nodes: + for output in node.output: + segment_node_outputs.add(output) + + # Get all inputs from nodes in this segment + segment_node_inputs = set() + for node in segment_nodes: + for inp in node.input: + segment_node_inputs.add(inp) + + # Inputs are those that are used by nodes in this segment but not produced by any node in this segment + for inp in segment_node_inputs: + if inp not in segment_node_outputs: + # Check if it's a model input, intermediate value, or an initializer + if inp in all_value_infos: + segment_inputs.append(all_value_infos[inp]) + elif inp in initializer_map: + init = initializer_map[inp] + segment_initializers.append(init) + # Create a value info for this initializer + t = onnx.helper.make_tensor_value_info( + inp, + init.data_type, + list(init.dims) + ) + segment_inputs.append(t) + else: + # For unknown intermediate tensors, we need to infer reasonable shapes and correct dtypes + # Detect if this input is a control tensor (e.g., axes/shape/indices) based on consumer op and input index + consumer = None + consumer_input_idx = None + for n in segment_nodes: + for idx_i, name_i in enumerate(n.input): + if name_i == inp: + consumer = n + consumer_input_idx = idx_i + break + if consumer is not None: + break + # Map control inputs requiring INT64 dtype + int64_controls = OnnxSlicer.CONTROL_INPUT_SPECS + if consumer is not None and consumer.op_type in int64_controls and consumer_input_idx in int64_controls[consumer.op_type]: + shape_sym, _ = int64_controls[consumer.op_type][consumer_input_idx] + t = onnx.helper.make_tensor_value_info( + inp, + onnx.TensorProto.INT64, + shape_sym + ) + segment_inputs.append(t) + else: + # Data tensor: infer shape and use FLOAT as a safe default + inferred_shape = OnnxSlicer._infer_input_shape(inp, segment_nodes) + t = onnx.helper.make_tensor_value_info( + inp, + onnx.TensorProto.FLOAT, + inferred_shape + ) + segment_inputs.append(t) + + # Outputs are those that are produced by nodes in this segment but not consumed by any node in this segment + # or are model outputs + for out in segment_node_outputs: + # Check if this output is used as an input by any node in this segment + is_output = True + for node in segment_nodes: + if out in node.input: + is_output = False + break + + # If it's not used as an input or it's a model output, add it as a segment output + if is_output or out in [o.name for o in graph.output]: + if out in all_value_infos: + segment_outputs.append(all_value_infos[out]) + else: + # For unknown outputs, infer shape from the producing node + inferred_shape = OnnxSlicer._infer_output_shape(out, segment_nodes) + t = onnx.helper.make_tensor_value_info( + out, + onnx.TensorProto.FLOAT, + inferred_shape + ) + segment_outputs.append(t) + + return segment_inputs, segment_outputs, segment_initializers + + @staticmethod + def _infer_input_shape(input_name, segment_nodes): + """ + Infer a reasonable shape for an input tensor based on the nodes that consume it. + Use only symbolic strings (dim_param) and never None. + """ + for node in segment_nodes: + if input_name in node.input: + if node.op_type == "Conv": + # Conv expects 4D input: [N, C, H, W] + return ["N", "C", "H", "W"] + elif node.op_type == "Gemm": + # Gemm expects 2D input: [N, F] + return ["N", "F"] + elif node.op_type in ["Relu", "BatchNormalization"]: + # These preserve input shape, assume 4D + return ["N", "C", "H", "W"] + + # Default fallback for unknown cases + return ["N", "D"] + + @staticmethod + def _infer_output_shape(output_name, segment_nodes): + """ + Infer a reasonable shape for an output tensor based on the node that produces it. + Use only symbolic strings (dim_param) and never None. + """ + for node in segment_nodes: + if output_name in node.output: + if node.op_type == "Conv": + # Conv output is 4D: [N, C, H, W] + return ["N", "C", "H", "W"] + elif node.op_type == "Gemm": + # Gemm output is 2D: [N, F] + return ["N", "F"] + elif node.op_type in ["Relu", "BatchNormalization"]: + # These preserve input shape + return ["N", "C", "H", "W"] + elif node.op_type == "Reshape": + # Reshape output depends on the target shape; keep generic 2D + return ["N", "F"] + + # Default fallback + return ["N", "D"] + + def _index_graph(self, graph): + name_to_producer = {} + for node in graph.node: + for out in node.output: + name_to_producer[out] = node + init_map = {init.name: init for init in graph.initializer} + value_info_map = {vi.name: vi for vi in list(graph.input) + list(graph.value_info) + list(graph.output)} + return name_to_producer, init_map, value_info_map + + def _collect_control_tensors(self, graph, segment_nodes) -> List[onnx.TensorProto]: + name_to_producer, init_map, _value_info_map = self._index_graph(graph) + to_inject: Dict[str, onnx.TensorProto] = {} + for n in segment_nodes: + spec = self.CONTROL_INPUT_SPECS.get(n.op_type) + if not spec: + continue + for idx_i in spec.keys(): + if idx_i >= len(n.input): + continue + inp_name = n.input[idx_i] + if not inp_name or inp_name in to_inject: + continue + if inp_name in init_map: + t = onnx.TensorProto(); t.CopyFrom(init_map[inp_name]) + to_inject[inp_name] = t + continue + prod = name_to_producer.get(inp_name) + if prod is not None and prod.op_type == "Constant": + for attr in prod.attribute: + if attr.name == "value" and attr.type == onnx.AttributeProto.TENSOR: + const_t = onnx.TensorProto(); const_t.CopyFrom(attr.t) + if not const_t.name: + const_t.name = prod.output[0] if prod.output and prod.output[0] else inp_name + to_inject[inp_name] = const_t + break + return list(to_inject.values()) + + def prepare_extract_model(self, graph, segment_nodes, segment_inputs) -> Tuple[List[str], List[onnx.TensorProto]]: + """ + Prepare input names for onnx.utils.extract_model using true boundary inputs: + - Exclude Constant-produced tensors from external inputs (capture their TensorProtos). + - Exclude initializers (weights/biases/stats). + - Keep boundary names even if untyped; the extractor retry will add temporary ValueInfos. + - Fallback to the first model input only if nothing remains. + """ + name_to_producer, init_map, _value_info_map = self._index_graph(graph) + initializer_names = set(init_map.keys()) + + prepared_inputs: List[str] = [] + seen: set = set() + const_tensors: List[onnx.TensorProto] = [] + + for vi in segment_inputs: + name = getattr(vi, 'name', None) + if not name or name in seen: + continue + seen.add(name) + + # Skip parameters (weights/biases/stats) + if name in initializer_names: + continue + + producer = name_to_producer.get(name) + if producer is not None and producer.op_type == "Constant": + # Capture Constant tensor for later injection; do not expose as external input + for attr in producer.attribute: + if attr.name == "value" and attr.type == onnx.AttributeProto.TENSOR: + t = onnx.TensorProto(); t.CopyFrom(attr.t) + if not t.name: + t.name = producer.output[0] if (producer.output and producer.output[0]) else name + const_tensors.append(t) + break + continue + + # Keep this boundary name even if untyped; we will type it in the temporary pre-extract model + prepared_inputs.append(name) + + if not prepared_inputs and graph.input: + prepared_inputs = [graph.input[0].name] + + # Deduplicate while preserving order + deduped: List[str] = [] + seen2 = set() + for n in prepared_inputs: + if n not in seen2: + seen2.add(n) + deduped.append(n) + + return deduped, const_tensors + + def _prepare_typed_model_for_extraction(self, base_model_path: str, save_dir: str, segment_idx: int, + segment_nodes, input_names: List[str], output_names: List[str]) -> str: + """ + Ensure the model used for extraction has type/shape info for this segment's boundary tensors + (inputs/outputs) to avoid extractor errors about missing 'shape'. Writes a temporary + model file in save_dir when needed and returns its path; otherwise returns base_model_path. + """ + model = onnx.load(base_model_path) + graph = model.graph + typed_names = {vi.name for vi in list(graph.input) + list(graph.value_info) + list(graph.output)} + + added_any = False + # Ensure output value_infos + for out_name in (output_names or []): + if out_name and out_name not in typed_names: + shape = self._infer_output_shape(out_name, segment_nodes) + vi = onnx.helper.make_tensor_value_info(out_name, onnx.TensorProto.FLOAT, shape) + graph.value_info.extend([vi]) + typed_names.add(out_name) + added_any = True + # Ensure input value_infos (only for names that are not already typed and not true graph inputs) + graph_input_names = {vi.name for vi in graph.input} + for in_name in (input_names or []): + if in_name and in_name not in typed_names and in_name not in graph_input_names: + shape = self._infer_input_shape(in_name, segment_nodes) + vi = onnx.helper.make_tensor_value_info(in_name, onnx.TensorProto.FLOAT, shape) + graph.value_info.extend([vi]) + typed_names.add(in_name) + added_any = True + + if not added_any: + return base_model_path + tmp_path = os.path.join(save_dir, f"__pre_extract_{segment_idx}.onnx") + onnx.save(model, tmp_path) + return tmp_path + + + def slice(self, slice_points: List[int], model_metadata, output_path=None): + """ + Slice the ONNX model based on the provided slice points. + + Args: + slice_points: List of indices representing nodes with parameter details + model_metadata: The model analysis metadata containing node information + + Returns: + List[str]: Paths to the sliced model files + """ + # Error handling + if not slice_points: + raise ValueError("No slice points provided.") + + if not model_metadata or "nodes" not in model_metadata: + raise ValueError("Invalid model metadata. Please run 'analyze()' first.") + + # Set up slicing environment + (graph, node_map, node_type_index_map, initializer_map, value_info_map, + index_to_node_name, index_to_segment_name, output_path) = self._slice_setup(model_metadata, output_path) + + # Add the end of the model as a final slice point + max_index = max(node_info["index"] for node_info in model_metadata["nodes"].values()) + # Always add max_index + 1 to ensure we create a segment for the last node + if max_index + 1 not in slice_points: + slice_points.append(max_index + 1) + + # Sort slice points to ensure they're in order + slice_points.sort() + + # Store paths to sliced models + slice_paths = [] + + # Process each segment + for i in range(len(slice_points)): + segment_idx = i - 1 + start_idx = slice_points[i - 1] if i > 0 else 0 + end_idx = slice_points[i] + + # Skip if start and end are the same + if start_idx == end_idx: + continue + + # Get nodes for this segment + segment_nodes = self._get_nodes(start_idx, end_idx, index_to_node_name, + index_to_segment_name, node_map, node_type_index_map, segment_idx) + + # Skip if no nodes in this segment + if not segment_nodes: + continue + + # Get segment details + segment_inputs, segment_outputs, segment_initializers = self._get_segment_details( + segment_nodes, graph, initializer_map) + + # Save the segment model + save_path = os.path.join(output_path, f"segment_{segment_idx}") + if not os.path.exists(save_path): + os.makedirs(save_path, exist_ok=True) + file_path = os.path.join(save_path, f"segment_{segment_idx}.onnx") + + filtered_inputs = Utils.filter_inputs(segment_inputs, graph) + unfiltered_inputs = Utils.get_unfiltered_inputs(segment_inputs) + output_names = [output_info.name for output_info in segment_outputs] + + # Prepare inputs: keep constants internal and collect their tensors + prepared_inputs, const_inits_boundary = self.prepare_extract_model(graph, segment_nodes, segment_inputs) + control_inits = self._collect_control_tensors(graph, segment_nodes) + # Deduplicate by tensor name + seen_names = set() + const_inits = [] + for t in list(const_inits_boundary) + list(control_inits): + if getattr(t, 'name', None) and t.name not in seen_names: + seen_names.add(t.name) + const_inits.append(t) + + # Use extract_model to create the segment + try: + # Build a human-friendly display of inputs similar to old output + display_inputs = [] + # Include initializers consumed by the segment for readability + init_names_set = {init.name for init in graph.initializer} + boundary_inputs = [getattr(vi, 'name', None) for vi in segment_inputs if getattr(vi, 'name', None)] + for n in boundary_inputs: + if n in init_names_set or n in boundary_inputs: + display_inputs.append(n) + # Fall back to unfiltered list for display if empty + if not display_inputs: + display_inputs = unfiltered_inputs + logger.info(f"Extracting segment {segment_idx}: {display_inputs} -> {output_names}") + print(f"Extracting segment {segment_idx}: {display_inputs} -> {output_names}") + # Extract the model directly to final path with strictly typed inputs + try: + extract_model( + input_path=self.onnx_path, + output_path=file_path, + input_names=(prepared_inputs or filtered_inputs), + output_names=output_names + ) + except Exception as ex_first: + # Retry using a temporary model with typed boundary tensors + prepped_model_path = self._prepare_typed_model_for_extraction( + base_model_path=self.onnx_path, + save_dir=save_path, + segment_idx=segment_idx, + segment_nodes=segment_nodes, + input_names=(prepared_inputs or filtered_inputs), + output_names=output_names, + ) + extract_model( + input_path=prepped_model_path, + output_path=file_path, + input_names=(prepared_inputs or filtered_inputs), + output_names=output_names + ) + # Post-fix: normalize control attributes and inject constants (collision-safe) + try: + sub = onnx.load(file_path) + init_names = {i.name for i in sub.graph.initializer} + produced_names = set() + for n in sub.graph.node: + for out in n.output: + if out: + produced_names.add(out) + # Convert Unsqueeze 'axes' attribute to second input if missing + new_inits = [] + for n in sub.graph.node: + if n.op_type == "Unsqueeze" and len(n.input) < 2: + axes_vals = None + for a in list(n.attribute): + if a.name == "axes" and a.ints: + axes_vals = list(a.ints) + n.attribute.remove(a) + break + if axes_vals is not None: + axes_name = f"{n.name or n.output[0]}__axes" + if axes_name not in init_names and axes_name not in produced_names: + t = onnx.helper.make_tensor( + name=axes_name, + data_type=onnx.TensorProto.INT64, + dims=[len(axes_vals)], + vals=axes_vals, + ) + new_inits.append(t) + n.input.extend([axes_name]) + if new_inits: + sub.graph.initializer.extend(new_inits) + # Remove any same-named graph inputs + for t in new_inits: + for idx, gi in enumerate(list(sub.graph.input)): + if gi.name == t.name: + del sub.graph.input[idx] + break + # Inject captured const/control tensors + def remove_input_by_name(g, n): + for idx, gi in enumerate(list(g.input)): + if gi.name == n: + del g.input[idx] + return + for t in const_inits: + if not getattr(t, 'name', None): + continue + # Skip if already present or would violate SSA by colliding with produced value names + if t.name in init_names or t.name in produced_names: + continue + sub.graph.initializer.extend([t]) + remove_input_by_name(sub.graph, t.name) + onnx.checker.check_model(sub) + onnx.save(sub, file_path) + except Exception as post_e: + logger.info(f"Post-injection check skipped/failed: {post_e}") + slice_paths.append(file_path) + + except Exception as e: + try: + logger.info(f"Error extracting segment, trying to create it instead {segment_idx}: {e}") + print(f"Error extracting segment, trying to create it instead {segment_idx}: {e}") + # Before creating graph, inject control/constants as initializers and remove them from inputs + # Compute names produced by nodes in this segment to avoid SSA collisions + produced_by_segment = set() + for n in segment_nodes: + for out in n.output: + if out: + produced_by_segment.add(out) + existing_init_names = {init.name for init in segment_initializers} + # Keep only safe constants that do not collide with produced outputs or existing initializers + safe_const_inits = [t for t in const_inits if getattr(t, 'name', None) and t.name not in produced_by_segment and t.name not in existing_init_names] + init_name_set = {t.name for t in safe_const_inits} + # Extend initializers with safe const/control + segment_initializers = list(segment_initializers) + safe_const_inits + # Filter out any inputs that collide with injected initializers + segment_inputs = [vi for vi in segment_inputs if getattr(vi, 'name', None) not in init_name_set] + + segment_graph = onnx.helper.make_graph( + segment_nodes, + f"segment_{segment_idx}_graph", + segment_inputs, + segment_outputs, + segment_initializers + ) + + # Create a model from the graph + segment_model = onnx.helper.make_model(segment_graph) + + # Normalize Unsqueeze in manual path: convert 'axes' attribute to second INT64 input if missing + try: + g = segment_model.graph + init_names_manual = {i.name for i in g.initializer} + produced_names_manual = set() + for node_m in g.node: + for out_m in node_m.output: + if out_m: + produced_names_manual.add(out_m) + new_axes_inits = [] + for node_m in g.node: + if node_m.op_type == "Unsqueeze" and len(node_m.input) < 2: + axes_vals = None + for a in list(node_m.attribute): + if a.name == "axes" and a.ints: + axes_vals = list(a.ints) + node_m.attribute.remove(a) + break + if axes_vals is not None: + base_name = f"{node_m.name or (node_m.output[0] if node_m.output else 'unsq')}__axes" + axes_name = base_name + # ensure uniqueness to avoid SSA collisions + suffix = 1 + while axes_name in init_names_manual or axes_name in produced_names_manual: + axes_name = f"{base_name}_{suffix}" + suffix += 1 + t_axes = onnx.helper.make_tensor( + name=axes_name, + data_type=onnx.TensorProto.INT64, + dims=[len(axes_vals)], + vals=axes_vals, + ) + new_axes_inits.append(t_axes) + node_m.input.extend([axes_name]) + if new_axes_inits: + g.initializer.extend(new_axes_inits) + # Remove any same-named graph inputs + for t_ax in new_axes_inits: + for idx_gi, gi in enumerate(list(g.input)): + if gi.name == t_ax.name: + del g.input[idx_gi] + break + except Exception: + pass + + # Validate and save + try: + onnx.checker.check_model(segment_model) + except Exception: + pass + onnx.save(segment_model, file_path) + slice_paths.append(file_path) + + except Exception as e: + logger.error(f"Error creating segment {segment_idx}: {e}") + continue + + return self.slice_post_process(slice_paths, self.analysis) + + @staticmethod + def slice_post_process(slices_paths, model_metadata): + abs_paths = [] + for path in slices_paths: + abs_path = os.path.abspath(path) + abs_paths.append(abs_path) + try: + model = onnx.load(path) + # if OnnxUtils.has_fused_operations(model): + # model = OnnxUtils.unfuse_operations(model) + + # Infer concrete shapes so saved slices carry numeric dims where possible + try: + model = shape_inference.infer_shapes(model) + except Exception as inf_err: + logger.info(f"Shape inference skipped for {path}: {inf_err}") + + onnx.checker.check_model(model) + # model = OnnxUtils.optimize_model(abs_path, model) + # model = OnnxUtils.add_shape_inference(model, model_metadata, path) + onnx.save(model, path) + except Exception as e: + logger.error(f"Error processing {path}: {e}") + continue + + return abs_paths + + def slice_model(self, output_path=None): + """ + Run the complete workflow: determine slice points and slice. + + Args: + output_path: The path to save the slices to. + + Returns: + Dict[str, Any]: Metadata about the sliced model + """ + + # Step 1: Determine slice points + slice_points = self.determine_slice_points(self.analysis) + + # Step 2: Slice the model + slices_paths = self.slice(slice_points, self.analysis, output_path) + + # Step 3: generate slices metadata + self.onnx_analyzer.generate_slices_metadata(self.analysis, slice_points, slices_paths, output_path) + + return slices_paths + +if __name__ == "__main__": + + model_choice = 5 # Change this to test different models + + base_paths = { + 1: "../../models/doom", + 2: "../../models/net", + 3: "../../models/resnet", + 4: "../../models/yolov3", + 5: "../../models/age", + } + abs_path = os.path.abspath(base_paths[model_choice]) + model_dir = os.path.join(abs_path, "model.onnx") + output_dir = os.path.join(abs_path, "slices") + onnx_slicer = OnnxSlicer(model_dir, save_path=base_paths[model_choice]) + onnx_slicer.slice_model(output_path=output_dir) diff --git a/src/utils/utils.py b/src/utils/utils.py new file mode 100644 index 0000000..a851f2e --- /dev/null +++ b/src/utils/utils.py @@ -0,0 +1,156 @@ +import json +import logging +from pathlib import Path +import torch + +# Configure logger +logger = logging.getLogger(__name__) + + +class Utils: + """ + Utility functions for working with ONNX models. + """ + + @staticmethod + def save_metadata_file(metadata, output_path, filename="metadata.json"): + """ + Save metadata to a JSON file. + + Args: + metadata: Dictionary containing metadata + output_path: Directory where the metadata will be saved + filename: Name of the metadata file (default: "metadata.json") + """ + output = Path(output_path) + + # Check if the provided path is a directory + if output.is_dir(): + # Combine the directory with the default or given filename + file_path = output / filename + else: + # Use the path as-is, assuming it includes the filename + file_path = output + + # Ensure the parent directory exists + file_path.parent.mkdir(parents=True, exist_ok=True) + + # Write metadata to the file + with file_path.open('w') as f: + json.dump(metadata, f, indent=4) + + + @staticmethod + def filter_inputs(segment_inputs, graph): + """ + Return the correct set of external input tensor names for ONNX extract_model. + Rules: + - Exclude initializers (weights/biases/constants) — extractor includes them automatically. + - Prefer only names that have type/shape in the original model (present in graph.input or graph.value_info). + - Preserve order and avoid duplicates. + - Provide safe fallbacks so we never return an empty list. + """ + # Names of model-level inputs and typed value_infos + graph_input_names = [inp.name for inp in graph.input] + graph_input_name_set = set(graph_input_names) + typed_names = set(graph_input_names) + typed_names.update({vi.name for vi in graph.value_info}) + # Names of initializers (weights/biases/statistics/constants) + initializer_names = {init.name for init in graph.initializer} + + # Phase 1: collect candidate external inputs from provided segment_inputs + # Exclude initializers + candidates_in_order = [] + seen = set() + for vi in segment_inputs: + name = getattr(vi, 'name', None) + if not name or name in seen: + continue + seen.add(name) + if name in initializer_names: + # Skip parameters; extractor will wire them automatically + continue + candidates_in_order.append(name) + + # Phase 2: keep only "typed" candidates (present in model inputs or value_info) + typed_candidates = [n for n in candidates_in_order if n in typed_names] + + # Use typed candidates if available + if typed_candidates: + return typed_candidates + + # Phase 3: fallbacks + # 3a) If the model has at least one input, use it as a conservative default + if graph_input_names: + return [graph_input_names[0]] + + # 3b) Use first non-initializer candidate if any + if candidates_in_order: + return [candidates_in_order[0]] + + # 3c) Last resort: if segment_inputs exist, return its first name + if segment_inputs: + first_name = getattr(segment_inputs[0], 'name', None) + if first_name: + return [first_name] + + # Should not happen, but return empty list if absolutely nothing is available + return [] + + @staticmethod + def get_unfiltered_inputs(segment_inputs): + """ + Return raw input tensor names from segment inputs for extract_model usage. + This intentionally avoids filtering out weights/biases/etc. + """ + segment_unfiltered_inputs = [getattr(inp, 'name', None) for inp in segment_inputs if getattr(inp, 'name', None)] + if not segment_unfiltered_inputs and segment_inputs: + segment_unfiltered_inputs = [segment_inputs[0].name] + return segment_unfiltered_inputs + + @staticmethod + def _get_original_model_shapes(model_metadata: dict): + """ + Extract shape information from model metadata. + + Args: + model_metadata: Dictionary containing model metadata with shape information + + Returns: + dict: Dictionary mapping tensor names to their shapes + """ + shapes = {} + + # Extract shapes from input_shape + input_shape = model_metadata.get("input_shape", []) + if input_shape and len(input_shape) > 0: + shapes["input"] = input_shape[0] + + # Extract shapes from output_shapes + output_shapes = model_metadata.get("output_shapes", []) + if output_shapes and len(output_shapes) > 0: + shapes["output"] = output_shapes[0] + + # Extract shapes from nodes if available + nodes = model_metadata.get("nodes", {}) + for node_name, node_info in nodes.items(): + if "parameter_details" in node_info: + for param_name, param_info in node_info["parameter_details"].items(): + if "shape" in param_info: + shapes[param_name] = param_info["shape"] + + return shapes + + @staticmethod + def write_input(tensor: torch.Tensor, file_path): + """Write tensor to input.json format.""" + data = {"input_data": tensor.tolist()} + with open(file_path, 'w') as f: + json.dump(data, f) + + @staticmethod + def read_input(file_path) -> torch.Tensor: + """Read tensor from input.json format.""" + with open(file_path, 'r') as f: + data = json.load(f) + return torch.tensor(data["input_data"]) diff --git a/src/verifier.py b/src/verifier.py new file mode 100644 index 0000000..f4f2bd4 --- /dev/null +++ b/src/verifier.py @@ -0,0 +1,171 @@ +""" +Orchestration for verifying proofs. +""" + +import os +import json +import time +from pathlib import Path +from src.backends.ezkl import EZKL + +class Verifier: + """ + Orchestrator for verifying model execution proofs. + """ + + def __init__(self): + """ + Initialize the verifier. + """ + self.ezkl_runner = EZKL() + + def verify_run(self, run_results_path, metadata_path): + """ + Verify the proofs in a run. + + Args: + run_results_path (str): Path to the run_results.json file + metadata_path (str): Path to the metadata.json file + + Returns: + dict: Updated run results with verification information + """ + # Load the run results and metadata + with open(run_results_path, 'r') as f: + run_results = json.load(f) + + with open(metadata_path, 'r') as f: + metadata = json.load(f) + + # Initialize counters + verified_segments = 0 + total_ezkl_segments = 0 + + # Process each segment in the execution results + for segment in run_results["execution_chain"]["execution_results"]: + segment_id = segment["segment_id"] + + # Skip segments that don't have proof_execution + if "proof_execution" not in segment: + continue + + # Skip segments where proof generation was not successful + if not segment["proof_execution"]["success"]: + continue + + # Get the proof file path + proof_path = segment["proof_execution"]["proof_file"] + if not os.path.exists(proof_path): + print(f"Warning: Proof file not found for {segment_id}: {proof_path}") + continue + + total_ezkl_segments += 1 + + # Get the segment metadata + segment_metadata = metadata["slices"].get(segment_id) + if not segment_metadata: + print(f"Warning: Metadata for segment {segment_id} not found") + continue + + # Get the paths for verification + settings_path = segment_metadata["settings_path"] + vk_path = segment_metadata["vk_path"] + + # Verify the proof + print(f"Verifying proof for {segment_id}...") + start_time = time.time() + verify_success = self.ezkl_runner.verify( + proof_path=proof_path, + settings_path=settings_path, + vk_path=vk_path + ) + verify_time = time.time() - start_time + + # Create or update verification_execution object + verification_execution = { + "verified": verify_success, + "success": verify_success, + "verification_time": verify_time + } + + if verify_success: + verified_segments += 1 + print(f"Successfully verified {segment_id}") + else: + print(f"Failed to verify {segment_id}") + + # Update the segment with the verification information + segment["verification_execution"] = verification_execution + + # Update the execution_chain with the new counters + # Keep the existing ezkl_witness_slices and ezkl_proved_slices + run_results["execution_chain"]["ezkl_verified_slices"] = verified_segments + + # Save the updated run results + with open(run_results_path, 'w') as f: + json.dump(run_results, f, indent=2) + + return run_results + + +if __name__ == "__main__": + # Choose which model to test + model_choice = 2 # Change this to test different models + + # Model configurations + base_paths = { + 1: "models/doom", + 2: "models/net", + 3: "models/resnet" + } + + # Get model directory + model_dir = os.path.abspath(base_paths[model_choice]) + + # Get run directory - use the latest run in the model's run directory + run_dir = os.path.join(model_dir, "run") + if not os.path.exists(run_dir): + print(f"Error: Run directory not found at {run_dir}") + exit(1) + + # Find the latest run + run_dirs = sorted([d for d in os.listdir(run_dir) if d.startswith("run_")]) + if not run_dirs: + print(f"Error: No runs found in {run_dir}") + exit(1) + + latest_run = run_dirs[-1] + run_path = os.path.join(run_dir, latest_run) + + # Construct paths for run_results.json and metadata.json + run_results_path = os.path.join(run_path, "run_result.json") + metadata_path = os.path.join(run_dir, "metadata.json") + + if not os.path.exists(run_results_path): + print(f"Error: run_result.json not found at {run_results_path}") + exit(1) + + if not os.path.exists(metadata_path): + print(f"Error: metadata.json not found at {metadata_path}") + exit(1) + + # Initialize verifier + verifier = Verifier() + + # Run verification + print(f"Verifying run {latest_run} for model {base_paths[model_choice]}...") + results = verifier.verify_run(run_results_path, metadata_path) + + # Display results + print(f"\nVerification completed!") + print(f"Verified segments: {results['execution_chain']['ezkl_verified_slices']} of {results['execution_chain']['ezkl_proved_slices']}") + + # Print details for each segment + print("\nSegment details:") + for segment in results["execution_chain"]["execution_results"]: + segment_id = segment["segment_id"] + if "verification_execution" in segment: + verified = segment["verification_execution"]["verified"] + status = "Success" if verified else "Failed" + time_taken = segment["verification_execution"]["verification_time"] + print(f" {segment_id}: {status} (Time: {time_taken:.2f}s)") \ No newline at end of file diff --git a/test/test_models/test_model_embedded/output/model_config.json b/test/test_models/test_model_embedded/output/model_config.json deleted file mode 100644 index 46e83ea..0000000 --- a/test/test_models/test_model_embedded/output/model_config.json +++ /dev/null @@ -1,5 +0,0 @@ -{ - "model_type": "sequential", - "layers": [], - "connections": [] -} \ No newline at end of file diff --git a/test/test_models/test_model_embedded/test_model_embedded.pth b/test/test_models/test_model_embedded/test_model_embedded.pth deleted file mode 100644 index a8e89c6..0000000 Binary files a/test/test_models/test_model_embedded/test_model_embedded.pth and /dev/null differ