diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml new file mode 100644 index 00000000..fcaf4345 --- /dev/null +++ b/.github/workflows/docs.yml @@ -0,0 +1,81 @@ +name: Deploy Documentation + +on: + push: + branches: + - main + - master + paths: + - 'docs/**' + - '.github/workflows/docs.yml' + pull_request: + branches: + - main + - master + paths: + - 'docs/**' + workflow_dispatch: # Allow manual trigger + +permissions: + contents: write + +jobs: + build: + name: Build docs + runs-on: ubuntu-latest + steps: + - name: Checkout repository + uses: actions/checkout@v4 + with: + fetch-depth: 0 # Fetch all history for git-based plugins + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: '3.11' + + - name: Install dependencies + run: | + pip install --upgrade pip + pip install -r docs/requirements.txt + + - name: Build documentation + run: | + cd docs + mkdocs build --strict + + - name: Upload build artifact + if: github.event_name == 'pull_request' + uses: actions/upload-artifact@v4 + with: + name: docs-site + path: docs/site/ + retention-days: 7 + + deploy: + name: Deploy docs + runs-on: ubuntu-latest + needs: build + if: github.event_name == 'push' && (github.ref == 'refs/heads/main' || github.ref == 'refs/heads/master') + steps: + - name: Checkout repository + uses: actions/checkout@v4 + with: + fetch-depth: 0 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: '3.11' + + - name: Install dependencies + run: | + pip install --upgrade pip + pip install -r docs/requirements.txt + + - name: Deploy to GitHub Pages + run: | + cd docs + mkdocs gh-deploy --force --config-file mkdocs.yml + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} diff --git a/.gitignore b/.gitignore index 82b6fd80..b7860735 100644 --- a/.gitignore +++ b/.gitignore @@ -26,3 +26,4 @@ pkg/ *.pyc venv/ python/probing/probing +docs/site/ diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 34b31d3f..57bc1cb7 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -5,6 +5,7 @@ repos: rev: v4.5.0 hooks: - id: check-yaml + exclude: ^docs/mkdocs\.yml$ # mkdocs.yml uses !!python/name tags - id: check-toml - id: end-of-file-fixer - id: trailing-whitespace @@ -20,12 +21,12 @@ repos: hooks: - id: black - - repo: local - hooks: - - id: cargo-fmt - name: cargo fmt - description: Format Rust code - entry: cargo fmt - language: system - types: [rust] - pass_filenames: false + # - repo: local + # hooks: + # - id: cargo-fmt + # name: cargo fmt + # description: Format Rust code + # entry: cargo fmt + # language: system + # types: [rust] + # pass_filenames: false diff --git a/docs/Makefile b/docs/Makefile index c07745e2..538a76ff 100644 --- a/docs/Makefile +++ b/docs/Makefile @@ -1,29 +1,40 @@ -# Minimal makefile for Sphinx documentation -# +# Makefile for Probing Documentation (MkDocs) -# You can set these variables from the command line, and also -# from the environment for the first two. -SPHINXOPTS ?= -SPHINXBUILD ?= sphinx-build -SOURCEDIR = . -BUILDDIR = _build +.PHONY: help install serve build clean deploy -# Put it first so that "make" without argument is like "make help". +# Default target help: - @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + @echo "Probing Documentation Build System" @echo "" - @echo "Additional targets:" - @echo " serve Start live preview server with auto-reload" + @echo "Usage:" + @echo " make install Install documentation dependencies" + @echo " make serve Start live preview server with auto-reload" + @echo " make build Build static documentation site" + @echo " make clean Remove built documentation" + @echo " make deploy Deploy to GitHub Pages" -.PHONY: help Makefile serve +# Install dependencies +install: + @echo "Installing documentation dependencies..." + pip install mkdocs mkdocs-material mkdocs-material-extensions mkdocstrings mkdocstrings-python # Live preview server serve: @echo "Starting live preview server..." @echo "Open http://127.0.0.1:8000 in your browser" - @sphinx-autobuild -b html "$(SOURCEDIR)" "$(BUILDDIR)/html" --host 0.0.0.0 --port 8000 --open-browser + mkdocs serve --dev-addr 0.0.0.0:8000 -# Catch-all target: route all unknown targets to Sphinx using the new -# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). -%: Makefile - @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) +# Build static site +build: + @echo "Building documentation..." + mkdocs build + +# Clean build artifacts +clean: + @echo "Cleaning build artifacts..." + rm -rf site/ + +# Deploy to GitHub Pages +deploy: + @echo "Deploying to GitHub Pages..." + mkdocs gh-deploy --force diff --git a/docs/README.md b/docs/README.md index 7f1ca335..b99f8006 100644 --- a/docs/README.md +++ b/docs/README.md @@ -1,173 +1,90 @@ -# Probing Documentation System +# Probing Documentation -This directory contains the complete documentation for the Probing project, built using [Sphinx](https://www.sphinx-doc.org/). +This directory contains the documentation for Probing, built with [MkDocs](https://www.mkdocs.org/) and the [Material theme](https://squidfunk.github.io/mkdocs-material/). ## Quick Start ### Install Dependencies ```bash -pip install -r requirements_doc.txt +# Using pip +pip install mkdocs mkdocs-material mkdocs-material-extensions mkdocstrings mkdocstrings-python + +# Or using uv +uv pip install -r pyproject.toml ``` -### Build Documentation +### Build and Serve ```bash -# Build HTML documentation -make html +# Start live preview server +make serve -# Or use sphinx-build directly -sphinx-build -b html . _build/html +# Or directly with mkdocs +mkdocs serve ``` -After building, the documentation will be generated in the `_build/html/` directory. Open `_build/html/index.html` in your browser to view the documentation. - -### Live Preview (Development Mode) +Then open http://localhost:8000 in your browser. -Using `sphinx-autobuild` can automatically detect file changes and rebuild the documentation while refreshing the browser: +### Build Static Site ```bash -# Method 1: Start from project root (recommended) -make docs-serve - -# Method 2: Use convenience script in docs directory -cd docs && ./serve.sh - -# Method 3: Use Makefile in docs directory -cd docs && make serve - -# Method 4: Use sphinx-autobuild directly -cd docs && sphinx-autobuild . _build/html --host 0.0.0.0 --port 8000 --open-browser +make build ``` -After starting, the terminal will display the local access address (usually `http://127.0.0.1:8000`). Open it in your browser to view the documentation in real-time. After modifying any documentation files, the documentation will automatically rebuild and refresh the browser. +The built site will be in the `site/` directory. -**Common Options:** -- `--host 0.0.0.0` - Allow access from other devices (on local network) -- `--port 8000` - Specify port number (default 8000) -- `--open-browser` - Automatically open browser - -## Documentation Structure +## Directory Structure ``` docs/ -├── conf.py # Sphinx configuration file -├── index.rst # Main documentation index -├── requirements_doc.txt # Python dependencies (documentation build only) -├── Makefile # Build script (Unix) -├── getting-started/ # Getting started guide -├── user-guide/ # User guide -├── design/ # System design -├── advanced/ # Advanced topics -└── development/ # Development related +├── mkdocs.yml # MkDocs configuration +├── pyproject.toml # Python dependencies +├── Makefile # Build commands +├── overrides/ # Custom templates +│ └── home.html # Homepage template +└── src/ # Documentation source + ├── index.md # Homepage content + ├── installation.md # Installation guide + ├── quickstart.md # Quick start guide + ├── guide/ # User guide + ├── design/ # Design documents + ├── examples/ # Example workflows + ├── api-reference.md # API reference + └── assets/ # Static assets + └── stylesheets/ # CSS styles ``` ## Writing Documentation -### Markdown Support - -The documentation uses [MyST Parser](https://myst-parser.readthedocs.io/) to support Markdown format. You can write documentation directly in Markdown, with support for the following extensions: - -- Code block fences (using `:colon_fence:`) -- Definition lists -- Task lists -- Math formulas (using `$` or `$$`) -- Substitutions and references -- And more - -### Adding New Documentation - -1. Create a `.md` file in the appropriate directory -2. Add the corresponding entry in `index.rst` +- All documentation is written in Markdown +- Use [Material for MkDocs](https://squidfunk.github.io/mkdocs-material/reference/) extensions +- Admonitions, code blocks with copy button, and Mermaid diagrams are supported -For example, to add a new user guide: - -```rst -.. toctree:: - :maxdepth: 2 - :caption: User Guide - - user-guide/sql-analytics - user-guide/memory-analysis - user-guide/new-guide # Newly added documentation -``` - -### Documentation Format Examples +### Example Admonition ```markdown -# Title - -This is a paragraph of regular text. - -## Code Example - -```python -def hello(): - print("Hello, World!") -``` - -## Admonitions - -```{note} -This is a note. -``` - -```{warning} -This is a warning. -``` - -## Math Formulas - -Inline formula: $E = mc^2$ +!!! note "Title" + This is a note admonition. -Block formula: - -$$ -\int_{-\infty}^{\infty} e^{-x^2} dx = \sqrt{\pi} -$$ +!!! warning + This is a warning. ``` -## Build Options - -### HTML Output +### Example Mermaid Diagram -```bash -make html +````markdown +```mermaid +graph LR + A[Start] --> B[Process] + B --> C[End] ``` +```` -### PDF Output (requires LaTeX) - -```bash -make latexpdf -``` +## Deployment -### Other Formats +To deploy to GitHub Pages: ```bash -make help # View all available formats +make deploy ``` - -## Theme and Styling - -The documentation uses the `sphinx_rtd_theme` (Read the Docs theme). You can modify the theme configuration in `conf.py`. - -## Troubleshooting - -### Common Issues - -1. **Module not found error** - - Ensure all dependencies are installed: `pip install -r requirements_doc.txt` - -2. **Build failure** - - Check for syntax errors in the documentation - - Ensure all referenced files exist - -3. **Character encoding issues** - - Ensure documentation uses UTF-8 encoding - - Check language settings in `conf.py` - -## More Information - -- [Sphinx Documentation](https://www.sphinx-doc.org/) -- [MyST Parser Documentation](https://myst-parser.readthedocs.io/) -- [Read the Docs Theme Documentation](https://sphinx-rtd-theme.readthedocs.io/) diff --git a/docs/README_DEPLOYMENT.md b/docs/README_DEPLOYMENT.md deleted file mode 100644 index 51707fdc..00000000 --- a/docs/README_DEPLOYMENT.md +++ /dev/null @@ -1,97 +0,0 @@ -# Documentation Deployment Guide - -This guide explains how the documentation is automatically built and deployed. - -## Automated Deployment - -The documentation is automatically built and deployed using GitHub Actions when: -- Changes are pushed to `master` or `main` branch in the `docs/` directory -- Pull requests are opened that modify files in `docs/` -- The workflow is manually triggered via `workflow_dispatch` - -## Deployment Targets - -### 1. GitHub Pages - -The documentation is automatically deployed to GitHub Pages when changes are pushed to the main branch. The built HTML files are published to the `gh-pages` branch. - -**Access**: `https://.github.io//` (if configured) or via repository settings. - -### 2. Read the Docs - -Read the Docs can be configured to automatically build from the repository using webhooks, or the workflow can trigger builds via the Read the Docs API. - -#### Option A: Webhook (Recommended) - -1. Go to your Read the Docs project settings -2. Enable "Build pull requests" if desired -3. Configure the webhook in GitHub repository settings: - - Go to Settings → Webhooks - - Add webhook: `https://readthedocs.org/api/v2/webhook///` - - Select "Just the push event" - -#### Option B: API Token (Alternative) - -If you prefer to trigger builds via API from the workflow: - -1. Create a Read the Docs API token: - - Go to https://readthedocs.org/accounts/token/ - - Create a new token with appropriate permissions - -2. Add GitHub secrets: - - `RTD_API_TOKEN`: Your Read the Docs API token - - `RTD_PROJECT_SLUG`: Your project slug (defaults to 'probing' if not set) - -3. The workflow will automatically trigger builds when documentation changes are pushed to the main branch. - -## Local Testing - -Before pushing changes, you can test the documentation build locally: - -```bash -cd docs -pip install -r requirements_doc.txt -make html -``` - -To preview the documentation: - -```bash -make serve -# Or use the serve.sh script -./serve.sh -``` - -## Workflow Details - -The workflow (`.github/workflows/docs.yml`) performs the following steps: - -1. **Checkout** the repository -2. **Set up Python** 3.12 environment -3. **Install** documentation dependencies from `requirements_doc.txt` -4. **Build** the documentation using Sphinx -5. **Check links** (with warnings allowed) -6. **Upload artifacts** for review -7. **Deploy to GitHub Pages** (main branch only) -8. **Trigger Read the Docs build** via API (if token is configured) - -## Troubleshooting - -### Build Failures - -- Check the GitHub Actions logs for specific error messages -- Verify that all dependencies in `requirements_doc.txt` are up to date -- Test the build locally before pushing - -### Read the Docs Not Building - -- Verify webhook is configured correctly -- Check Read the Docs project settings for build configuration -- If using API token, verify the token has correct permissions -- Check the workflow logs for API call responses - -### GitHub Pages Not Updating - -- Verify GitHub Pages is enabled in repository settings -- Check that the workflow has permission to write to the repository -- Review the workflow logs for deployment errors diff --git a/docs/advanced/architecture.md b/docs/advanced/architecture.md deleted file mode 100644 index 5da9a104..00000000 --- a/docs/advanced/architecture.md +++ /dev/null @@ -1,273 +0,0 @@ -# Challenges in Large-Scale Distributed Heterogeneous Training - -Current top-tier LLM training has entered the era of tens of thousands of GPUs. Even DeepSeek, which is most cost-effective in computing, uses 2000 H100s, equivalent to 5000+ A100 FP16 computing power. However, the performance diagnosis system severely lags behind the expansion of computing scale, mainly reflected in the following three aspects: - -* **GPU Failures are Normal**: In Meta's LLaMA3 training[^1], 16,000 H100s experienced 466 failures in 54 days, with 58.2% originating from GPU hardware failures. This means facing 8 GPU failures per day, severely affecting training continuity and stability. - -* **Performance Below Expectations is Also a Failure**: Due to using large-scale collective communication semantics (such as All Reduce) for synchronous training, slow nodes cause overall system efficiency degradation. In ByteDance's MegaScale actual measurement[^2], removing slow nodes can bring 0.7% MFU improvement. LLaMA-3 training also mentions that locating such slow node problems is extremely difficult. - -* **Disconnection Between Hardware, System, and Model Optimization**: Diagnostic data at the chip level is not effectively utilized by systems and models. Currently, systems and models only focus on computing utilization MFU, while memory bandwidth utilization MBU, C2C interconnect bandwidth, and network bandwidth utilization lack attention, preventing hardware, system, and model optimization from forming synergy. - -From the above challenges, it can be seen that existing tools have inherent limitations when dealing with large-scale distributed environments, making it difficult to simultaneously meet real-time performance monitoring and fine-grained data collection requirements. - -## Existing Tools and Methods Struggle with These Challenges - -Existing diagnostic tools mostly focus on a single dimension, making it difficult to achieve comprehensive analysis across hardware failures, system communication, and model performance, and lack visualization and real-time monitoring capabilities for distributed environments. This greatly increases diagnosis costs and time for R&D teams. Specific shortcomings include: - -* **Pre-deployment Tool Limitations**: Existing tools require pre-deployment and instrumentation before problems occur, otherwise they cannot capture fault information during or after the event. Many failures and performance issues are usually exposed during or after training, making effective retrospective analysis impossible. - -* **High Performance Overhead**: These tools consume large amounts of GPU resources and communication bandwidth during capture and diagnosis, significantly impacting production task performance, making it difficult to deploy them permanently in real production environments. - -* **Single-Machine Focus, Lack of Distributed Capabilities**: Most diagnostic tools currently only focus on single-node or single-GPU data, lacking comprehensive analysis capabilities for cross-node communication, load balancing, and other distributed training scenarios, making it difficult to locate system-wide performance bottlenecks. - -> To break through the above limitations, we need a diagnostic tool that has both low intrusiveness and real-time monitoring capabilities, and can achieve end-to-end data collection and correlation analysis in distributed scenarios. - -## What Characteristics Should an Ideal Diagnostic Tool Have - -* **24/7 Availability** - Can be dynamically enabled at any time without pre-deployment or instrumentation, running continuously in production tasks with extremely low performance overhead, achieving real-time monitoring and fault tracing, helping engineering teams respond to system anomalies and hardware failures in a timely manner. - -* **Full Scenario Coverage** - Not only supports single-machine diagnosis but also seamlessly covers distributed training environments. Regardless of cluster scale, ensures consistency in data collection and fault analysis, meeting needs from small-scale R&D to large-scale production. - -* **End-to-End** - Achieves comprehensive collection of diagnostic data from hardware level, chip interconnect status, to framework, system, and model layer data, building a complete closed-loop monitoring system, thereby providing critical feedback data for hardware chip companies to optimize product design and performance. - -# A Distributed Probe System - Probing - -Probing provides the following technical solutions for the above problems: - -+ **Probe Mechanism** - Uses ptrace (Linux only) to inject probes into target processes, thereby gaining the same memory address space and permissions as the target process. The probe is implemented as a shared library (libprobing.so) that is dynamically loaded into the target process. This solution allows direct observation and debugging within the process, surpassing the monitoring capabilities of traditional out-of-process tools. It's worth noting that when no monitoring commands are issued, probes are in a completely silent state and do not introduce additional performance overhead. - -+ **Query Engine** - Uses standard SQL engine for data management and analysis. For numerical sequences and string sequences, the Query engine uses different compression encoding algorithms, utilizing data locality for compression. Data compression ratio varies depending on specific data, achieving 5x to hundreds of times compression, significantly reducing storage overhead while ensuring time-series data precision. - -+ **Distributed Data Processing** - Currently, each node runs an independent query engine that processes local data. The cluster API allows querying node information and status across the distributed system. The control plane can manage any process individually and query cluster information through the cluster management interface. - - **Future Roadmap - Distributed SQL Query**: Probing is planning to implement distributed SQL query capabilities that will automatically split data computation tasks into local aggregation computation within each node and cross-node data aggregation analysis. This will enable unified SQL queries across the entire distributed system, allowing users to write a single SQL statement that transparently aggregates data from all nodes, achieving efficient monitoring and diagnosis in large-scale distributed environments without manual result merging. - -For the two key issues of distribution and heterogeneous computing, Probing makes special optimizations: - -- **Distributed** - - Distributed training state awareness: Real-time monitoring of computation and communication status of each node, supporting automatic discovery of performance anomaly nodes. - - Collective communication performance optimization: Provides specialized performance analysis tools for common distributed training patterns (DP/TP/PP). - - Load balancing analysis: Tracks and records load distribution of each node, timely discovering performance bottlenecks during training. - - **Distributed SQL Query (Planned)**: Future support for transparent cross-node SQL queries that automatically aggregate data from all nodes in the cluster, enabling unified analysis without manual result merging. - -- **Heterogeneous Computing** - - End-to-end data collection: Unified collection of performance metrics from hardware to framework layers, supporting correlation analysis. - - Hardware performance optimization: Provides data support for hardware performance tuning by analyzing training workload characteristics. - -## Overall Architecture - -```mermaid ---- -title: Overall Architecture ---- -graph TB - subgraph "Control Plane" - UI[Web UI] - CLI[Command Line] - API[SQL Query + HTTP Protocol] - UI & CLI --> API - end - - subgraph "Probe Plane" - direction LR - P0[Probe Core Mechanism + Query Engine] - EX[Probe Extension Mechanism] - P1[Python Probe] - P2[PyTorch Probe] - P3[Hardware Probe] - API --> P0 --> EX-.-> P1 & P2 & P3 - end - - style UI fill:#ddd,stroke:#333 - style CLI fill:#ddd,stroke:#333 - style API fill:#ddd,stroke:#333 - style P0 fill:#999,stroke:#333 - style P1 fill:#999,stroke:#333 - style P2 fill:#999,stroke:#333 - style P3 fill:#999,stroke:#333 -``` - -Probing uses a simple and reliable two-layer design: -1. **Probe Plane**, implemented as a shared library (libprobing.so), injected into training processes, providing: - 1. Core mechanism: Implements data collection and code injection via ptrace (Linux only). - 2. Query engine: Built on DataFusion, provides data storage and SQL analysis capabilities. - 3. Extension mechanism: Provides mechanisms for extending probe capabilities based on Python and Rust, while providing Python, Torch, and hardware probe functionality. - 4. HTTP server: Embedded HTTP server (using Axum) that listens on Unix domain sockets (local) or TCP ports (remote) for control commands. -2. **Control Plane**, provides user interaction interfaces, including: - 1. Web interface: Visual operations and data display (Dioxus-based web UI). - 2. Command-line tools: Flexible probe control via HTTP protocol. - 3. HTTP API: Standardized HTTP interface for query execution, configuration, and control. - -In distributed scenarios, each process on each node has an independent probe. Probes report their status to a central coordinator (typically rank 0) via a report worker that periodically sends node information. The control plane can manage any process individually, or query cluster information through the cluster API. Node discovery is based on environment variables (MASTER_ADDR, RANK, etc.) and periodic reporting rather than automatic peer discovery. - -```mermaid ---- -title: Distributed Deployment View ---- -graph TB - subgraph "Control Plane" - UI[Web UI] - CLI[Command Line] - API[Unified API] - UI & CLI --> API - end - - subgraph "Node 1" - P1[Training Process] - PR1[Probe] - P1 -.- PR1 - end - - subgraph "Node 2" - P2[Training Process] - PR2[Probe] - P2 -.- PR2 - end - - subgraph "Node 3" - P3[Training Process] - PR3[Probe] - P3 -.- PR3 - end - - PR1 <--Report Worker--> PR2 - PR2 <--Report Worker--> PR3 - PR3 <--Report Worker--> PR1 - - API --Control/Query--> PR1 - - style UI fill:#ddd,stroke:#333 - style CLI fill:#ddd,stroke:#333 - style API fill:#ddd,stroke:#333 - style P1 fill:#f9f,stroke:#333 - style P2 fill:#f9f,stroke:#333 - style P3 fill:#f9f,stroke:#333 - style PR1 fill:#bfb,stroke:#333 - style PR2 fill:#bfb,stroke:#333 - style PR3 fill:#bfb,stroke:#333 -``` - -## Query Engine - -Introducing a query engine can greatly simplify the design of data storage and processing modules within probes. The query processing flow inside the query engine is shown below: - -```mermaid -graph LR - sql["Query Language"] - --> lp["Logical Plan"] - --> pp["Physical Plan"] - --> ds["Data Sources"] -``` - -Where: -1. Query Language is the query language written by users, generally SQL or DataFrame API. -2. Logical Plan describes the high-level structure of queries and required logical operations, not concerned with low-level execution details. -3. Physical Plan: Specific execution plan generated based on logical plan, containing low-level execution details such as data distribution and operator implementation. -4. Data Sources: Are data sources, any Query will be converted to operations on data sources. - -The entire processing flow uses the open-source query engine DataFusion (version 47.0.0). Probing extends DataFusion by implementing custom data source providers that expose probe-collected data as queryable tables. Data sources are organized in three levels: -``` -Catalog (Namespace) - | - +-- Schema1 (Logical Grouping) - | | - | +-- Table1 - | +-- Table2 - | - +-- Schema2 - | - +-- Table3 -``` - -**Current Implementation**: Each probe runs an independent DataFusion query engine that processes queries against local data sources. Queries are executed on individual nodes, and results can be aggregated manually through the control plane. - -**Future Roadmap - Distributed SQL Query**: Probing plans to extend the query engine with distributed query capabilities, enabling: -- Automatic query planning that splits SQL queries into local and distributed components -- Local aggregation computation within each node to minimize data transfer -- Cross-node data aggregation and result merging -- Transparent distributed query execution from a single SQL statement -- Optimized data locality-aware query routing to reduce network overhead - -This will allow users to write queries like `SELECT AVG(cpu_usage) FROM python.metrics GROUP BY node_id` that automatically aggregate metrics across all nodes in the cluster. - -## Extension Mechanism - -Probing supports two types of data source plugins for static and dynamic data scenarios: - -##### TablePlugin -Static table data source plugin for providing single data tables with fixed structure. Suitable for: -- Hardware performance metrics (CPU/GPU utilization, temperature, etc.) -- Process status monitoring (memory usage, thread count, etc.) -- Fixed-format performance counter data - -##### SchemaPlugin -Dynamic table data source plugin supporting on-demand creation of multiple data tables. Suitable for: -- File system monitoring (each file mapped as a table) -- Python module tracking (each module mapped as a table) -- Dynamically generated performance analysis data - -In addition to the above two extension mechanisms, Probing also provides mechanisms for extending the query engine using Python: - -##### Immediate Query - -Any Python package function can be treated as a query engine extension, just write it in SQL statements as follows: -```SQL --- Directly execute Python expression -SELECT * -FROM python.`` -``` -For example: -```SQL -SELECT * -FROM python.`time.time()` -``` -Will call the `time.time()` statement on the node and return the result as a table. - -##### Table Registration - -Users can also actively write data to Probing in code: -```python -import probing - -# Create and register table -tbl = probing.ExternalTable("test", ["col1", "col2"]) - -# Write data -tbl.append([1, 0.1]) -tbl.append([2, 0.2]) -tbl.append([3, 0.3]) -``` -Data written from Python is also automatically compressed and optimized by the query engine. -```SQL --- Query registered table -SELECT * -FROM python.test -``` - -## Comparison with Other Tools - -| Feature Dimension | NVIDIA DCGM | Nsight Systems | Torch Profiler | **Probing** | -|------------------|-------------|----------------|----------------|-------------| -| **Intrusiveness** | None (driver level) | Low (requires pre-injection) | High (code modification) | **Very Low (dynamic injection)** | -| **Performance Overhead** | None | Dynamic (varies with sampling frequency) | High | **Dynamic (varies with sampling frequency)** | -| **Distributed Correlation Analysis** | ❌ Single node | ❌ Single process | ❌ Single process | **Distributed Query** | -| **Data Collection Dimension** | Hardware metrics | System + Application layer | Framework layer | **End-to-End (Hardware/Framework/Model)** | -| **Dynamic Start/Stop Capability** | ❌ Always-on collection | ❌ Requires restart | ❌ Code modification | **Runtime Injection🚀** | -| **Fault Diagnosis Capability** | Hardware fault alerts | Performance bottleneck location | Operator performance analysis | **Hardware→Model Correlation Analysis🔍** | -| **Slow Node Detection** | ❌ | ❌ | ❌ | **Auto Tagging🏷️** | -| **Communication Performance Analysis** | Bandwidth utilization | NCCL event tracking | None | **Communication + Model Analysis📊** | -| **Memory Diagnosis** | GPU memory usage | GPU memory access patterns | GPU memory allocation statistics | Under development | -| **Data Retrospection Capability** | Rolling window storage | Requires file export (large files) | Requires file export (large files) | **Dynamic Storage📦** | -| **Hardware Feedback Value** | Basic health monitoring | Microarchitecture optimization | None | **Workload-Guided Chip Design💎** | - -# References - -[^1]: [The Llama 3 Herd of Models](https://arxiv.org/pdf/2407.21783) - -[^2]: [MegaScale: Scaling Large Language Model Training to More Than 10, 000 GPUs](https://arxiv.org/pdf/2402.15627) diff --git a/docs/advanced/performance.md b/docs/advanced/performance.md deleted file mode 100644 index 65a46cde..00000000 --- a/docs/advanced/performance.md +++ /dev/null @@ -1,402 +0,0 @@ - - -# Probing Performance Profiling Implementation - -This document details Probing's performance profiling implementation. For Probing's overall architecture design, please refer to [Overall Architecture](./architecture.md). - -When optimizing systems, Benchmark and Profiling are two very important methods. The former refers to methods for testing end-to-end performance of the entire system or end-to-end performance of a subsystem, which can quickly form a feedback loop: code optimization --> Benchmark --> code optimization. This is also the main workflow for most engineers doing performance optimization. However, pure Benchmark workflows easily fall into situations where significant effort is invested in optimizing code, but performance never improves. At this point, the optimizations being invested are no longer the core bottleneck of the entire system, so they cannot bring much improvement to overall system performance. To locate system performance problems and find bottlenecks that need optimization, Profiling is required. - -## Profiling Methodology - -In daily work, we encounter many profiling tools, such as perf built into the Linux kernel, Intel's vtune, and torch's torch.profiler. These tools can be divided into three categories based on their underlying principles: Instrumentation, Sampling, and Performance Counter. - -### Instrumentation Method - -When testing code performance, a commonly used method is to directly insert timing code in the code: - -```python -def foo(): - start = time.time() - do something - end = time.time() - - print("duration = ", end - start) -``` - -In addition to recording time, this manual code insertion can also record branch and loop conditions, memory allocation situations, and other information of interest when analyzing performance. If this process can be automated, inserting relevant timing for every piece of code executed, we can profile the entire application from a global perspective. This is automatic Instrumentation technology. For C/C++ code, this instrumentation requires compiler support, such as GCC's profiling-related options: -```bash -gcc a.c -g -pg -``` - -With Instrumentation, compilers can also implement code coverage, AddressSanitizer, StackProtector, and other features. In addition to directly affecting binary code generation at compile time, modifications can also be made in the linker/loader. For example, most memory profilers track memory allocation behavior by replacing malloc/free functions in libc. - -#### Python Code Instrumentation - -For Python, the most common instrumentation method is using Decorators: - -```python -def my_decorator(func): - def wrapper(*args, **kwargs): - start = time.time() - ret = func(*args, **kwargs) - end = time.time() - print("duration of {}: {}", func, end - start) - return ret - return wrapper -``` - -When using `my_decorator`: -```python -@my_decorator -def foo(): - pass -``` - -Will be automatically expanded to: -```python -def foo(): - pass -foo = my_decorator(foo) -``` - -Python also supports decorators with parameters, which won't be further expanded here. Decorator syntax greatly simplifies the development cost of instrumentation in Python and is easy to control in code. Working together with decorator syntax is the `with` syntax, which defines a semantic scope that can be used to control the logic of instrumentation code: - -```python -def my_decorator(func): - def wrapper(*args, **kwargs): - enabled = get_enable() - if enabled: - start = time.time() - ret = func(*args, **kwargs) - if enabled: - end = time.time() - print("duration of {}: {}", func, end - start) - return ret - return wrapper - -class MyContext: - def __enter__(self): - set_enable() - def __exit__(self): - set_disable() -``` - -At this point, `MyContext` can be used to control instrumentation code behavior: -```python -foo() # Will not execute instrumentation code -with MyContext(): - foo() # Will print instrumentation timing - -foo() # Will not execute instrumentation code -``` - -#### Python Interpreter Instrumentation - -In addition to instrumentation operations in code, Python also supports instrumentation operations at the interpreter level. The Python interpreter supports user-defined trace functions: - -```C -struct _frame { - PyObject_HEAD - PyFrameObject *f_back; /* previous frame, or NULL */ - struct _PyInterpreterFrame *f_frame; /* points to the frame data */ -==> PyObject *f_trace; /* Trace function */ - int f_lineno; /* Current line number. Only valid if non-zero */ - char f_trace_lines; /* Emit per-line trace events? */ - char f_trace_opcodes; /* Emit per-opcode trace events? */ - PyObject *f_extra_locals; /* Dict for locals set by users using f_locals, could be NULL */ - ... -}; -``` - -When executing Python code, this `f_trace` function can be used to trace interpreter internal execution. There are 5 types of events that will be forwarded to the trace function: -- call: Execute function call. -- line: Execute a line of code. -- return: Function returns. -- exception: An exception occurs. -- opcode: Execute a bytecode instruction. - -Using the `trace` function, Python debuggers, profilers, etc. can be implemented. If we need to implement a distributed debugger, we can develop an RPC-controlled trace function. Additionally, Python code can introspectively obtain bytecode for each function and decompile and instrument these bytecodes. Methods for bytecode decompilation and instrumentation will not be expanded here. - -#### Torch Framework Instrumentation - -Torch framework's dispatch mechanism reserves some hook interfaces that can capture operator calls through the `torch::addGlobalCallback` interface. These hooks are passed a `torch::RecordFunction` structure, through which call context can be obtained, including name, inputs, etc. However, the overhead of recording these call information is high and needs to be well controlled. Torch also provides the `c10::ThreadLocalDebugInfo` interface for tracking some information throughout the forward and backward processes. - -Main functions using `addGlobalCallback` for tracing in PyTorch include: -1. Kineto Profiler, the main implementation of Torch Profiler: torch/csrc/autograd/profiler_kineto.cpp -2. Execution Trace Observer, used to export computation graphs for underlying facility analysis and simulation: torch/csrc/profiler/standalone/execution_trace_observer.cpp - -### Sampling Method - -Instrumentation methods can obtain accurate execution timelines and output timelines. However, instrumentation requires invading target code and brings additional runtime overhead, making it difficult to use directly in online production tasks. - -Sampling methods can well avoid invasion of target code while keeping performance overhead at a reasonable level. There are two common sampling methods: - -#### Pprof Method - -The Pprof method was first seen in Google's gperftools. Its implementation principle is to set a timer based on the `setitimer` method[^setitimer]. The prototype of the `setitimer` method is as follows: - -```C -int setitimer(int which, const struct itimerval *new_value, - struct itimerval *old_value); -``` - -Where `which` has three values: - -- **ITIMER_REAL**: When timing completes, triggers **SIGALRM** signal. -- **ITIMER_VIRTUAL**: When timing completes, triggers **SIGALRM** signal, but only times when process is active. -- **ITIMER_PROF**: When timing completes, triggers **SIGPROF** signal. - -The **SIGPROF** signal randomly selects a thread, interrupts its execution, and runs the signal's handler on its execution stack. Generally, threads that consume more CPU have a higher probability of being selected. In the signal's handler, the call stack of the interrupted thread can be obtained through backtrace. After analyzing the stack through libunwind, a sampling can be completed. By combining multiple sampling results, a flame graph of the current process can be drawn. - -#### PMU Method - -Linux provides perf-related interfaces[^9] that expose hardware PMU (Performance Monitoring Unit) sampling capabilities to developers: - -```C -#include /* Definition of PERF_* constants */ -#include /* Definition of HW_* constants */ -#include /* Definition of SYS_* constants */ -#include - -int syscall(SYS_perf_event_open, struct perf_event_attr *attr, - pid_t pid, int cpu, int group_fd, unsigned long flags); -``` - -Since glibc does not provide a wrapper, system calls need to be made directly through syscall. `perf_event_attr` is used to control PMU sampling: -```C -struct perf_event_attr { - __u32 type; /* Type of event */ - __u32 size; /* Size of attribute structure */ - __u64 config; /* Type-specific configuration */ - - union { - __u64 sample_period; /* Period of sampling */ - __u64 sample_freq; /* Frequency of sampling */ - }; - - __u64 sample_type; /* Specifies values included in sample */ - __u64 read_format; /* Specifies values returned in read */ - - ... - union { - __u32 wakeup_events; /* wakeup every n events */ - __u32 wakeup_watermark; /* bytes before wakeup */ - }; - ... -}; -``` - -Key field descriptions: -- type: Used to define event type - - `PERF_TYPE_HARDWARE` Hardware sampling - - `PERF_TYPE_SOFTWARE` Software sampling -- config: Used to configure events - - `PERF_COUNT_HW_CPU_CYCLES` Sample by clock cycles - - `PERF_COUNT_HW_INSTRUCTIONS` Sample by instruction count -- sample_type: Used to specify information included in sampling events - - `PERF_SAMPLE_IP` Record IP pointer - - `PERF_SAMPLE_TIME` Record timestamp - - `PERF_SAMPLE_STACK_USER` Record user-space call stack - - `PERF_SAMPLE_CALLCHAIN` Record call stack - -Compared to the `setitimer` method, the PMU method can provide higher timing precision, and sampling is completely done by hardware with lower overhead. However, the PMU method depends on specific hardware implementation and requires processes to have CAP_PERFMON permissions (Linux 5.8+) or CAP_SYS_ADMIN permissions. Also, PMU cannot obtain richer information through handlers like SIGPROF. - -#### GPU Sampling Techniques - -CUDA supports sampling PC pointers (program counter) on devices. Each SM (Streaming Multiprocessor) randomly selects a warp at fixed time intervals to record its scheduling state and PC pointer. CUDA also provides methods to associate PC pointers with SASS code for users to obtain function IDs. - -Starting from CUDA 12.6, CUPTI (CUDA Profiling Toolkit Interface) introduced a new performance monitors (PM) interface[^cupti_pm]. Based on this interface, the following can be obtained: -- GR Active: The percentage of cycles the compute engine is active. -- Tensor Active / FP16 Active: The ratio of cycles the SM tensor pipes or FP16x2 pipes were active issuing tensor instructions to the number of cycles in the sample period as a percentage. -- DRAM Read Bandwidth: The ratio of cycles the DRAM interface was active reading data to the elapsed cycles in the same period as a percentage. -- PCIe Read Throughput: The ratio of bytes received on the PCIe interface to the maximum number of bytes receivable in the sample period as a percentage. -- NVLink bytes received: The ratio of bytes received on the NVLink interface to the maximum number of bytes receivable in the sample period as a percentage. - -For the complete list, please refer to Nvidia's official documentation. - -### Performance Counter Method - -From the introduction of Instrumentation and Sampling methods, we can see a trend: sampling-based methods have received increasing development and hardware support due to their low intrusiveness and wide applicability. In addition to conventional IP pointer sampling, memory operation counters, PCIe operation counters, etc. also provide a lot of useful information for diagnosing performance problems. However, the implementation of performance counter methods is not limited to hardware implementation; software layers can also provide diagnostic information through performance counters. Common hardware and software counters are briefly listed here: - -#### Hardware Counters - -In a heterogeneous training hardware system, different hardware components have their own counters. Common hardware counters are listed below: - -- CPU[^1][^2] - - Instruction counters: `instructions` instruction counter and `cycles` clock counter, etc. - - Cache: `cache-misses` L1, L2, L3 cache miss counts and `cache-references` cache access counts, etc. - - Branch: `branch-instructions` branch instruction count and `branch-misses` branch prediction failure count, etc. -- RDMA[^6] - - Basic statistics: `port_rcv_packets` received packet count and `port_xmit_packets` (sent packet count, etc. - - Data: `port_rcv_data` received data volume and `port_xmit_data` sent data volume. - - Congestion control: `np_cnp_sent`, etc. - -#### Software Counters - -At various levels of the entire software stack, there are also counters of various sizes: - -- OS level: - - /proc//stat, CPU, context switches, system calls, and other statistics. - - /proc//statm, process memory usage. -- Framework level: - - NCCL Counter[^7] - - PyTorch Flight Recorder[^8] - -#### GPU Performance Counters - -PMU on GPU is divided into two parts: Core PMU and Uncore PMU, used to monitor performance metrics on Core and outside Core respectively: - -- Core performance counters[^nsyscomp]: - - Computation: `sm_inst_executed` executed instruction count, `sm_inst_executed_atomics` executed atomic instruction count. - - Memory access: `sm_inst_executed_generic_loads` memory load instruction count and `sm_inst_executed_generic_stores` memory save instruction count. -- Uncore performance counters[^uncore] - - SCF PMU: Monitors system-level cache events, CPU traffic, and strongly ordered (SO) PCIe write traffic to local/remote memory. - - NVLink-C2C0 PMU: Monitors incoming traffic from GPUs/CPUs connected through NVLink-C2C (chip-to-chip) interconnect. - - NVLink-C2C1 PMU: Monitors incoming traffic from GPUs connected through NVLink-C2C (chip-to-chip) interconnect. - - CNVLink PMU: Monitors traffic from GPUs and PCIe devices on remote slots to local memory. - - PCIE PMU: Monitors all read/write traffic from PCIe root port to local/remote memory. - -#### Sampling GPU Performance Counters - -A common usage of performance counters is to capture performance counter values at the start and end of a span, and use the difference to determine how much network throughput or computation the code within the span generated. However, for heterogeneous computing scenarios like GPU, sampling and statistics of performance counters often need to consider the synchronization relationship between GPU Stream execution and CPU. That is, asynchronous sampling must be performed on GPU Stream to obtain true values over a period of time. As shown in the figure below, although computation is initiated by CPU at time `2-3`, obtaining hardware performance counters needs to be at time `5-9`. - -```mermaid -block-beta - columns 11 - TL["Time:"] - 1 2 3 4 5 6 7 8 9 10 - C["CPU:"] space CS["launch"]:2 space:7 - G["GPU:"] space:4 S["span"]:5 space:1 - - style TL fill:#00000000, stroke:#00000000; - style G fill:#00000000, stroke:#00000000; - style C fill:#00000000, stroke:#00000000; -``` - -Therefore, reading GPU hardware registers must be completed on the GPU device to better satisfy Stream asynchronous semantics. How to read performance counters on GPU and synchronize data to the CPU side will be discussed in another article. - -## Challenges and Coping Strategies for Distributed Training Profiling - -Existing Profiling tools can better solve single-machine performance analysis problems, but in distributed training scenarios, they face new challenges: - -1. **Dramatic Changes in Performance Characteristics** - - When cluster scale expands, training task performance characteristics will significantly change. For example: - - 3D parallel partitioning strategies are affected by cluster scale and parallel parameters. Single-machine Profiling has difficulty capturing key metrics of all participating roles. - - In large-scale clusters, collective communication may cause latency spikes and bandwidth utilization drops. Single-machine Profiling tools have difficulty accurately reflecting these phenomena. - -2. **Complexity of Data Statistics and Parsing** - - Single-machine Profiling usually relies on single samples to draw relatively certain conclusions, while distributed environments rely more on massive statistical data, greatly increasing accuracy requirements for results and complexity of data parsing. - -3. **Cross-Node Collaborative Debugging Requirements** - - In distributed environments, nodes must achieve precise coordination, performing real-time and in-depth analysis of collaborative behaviors between different roles, which places higher demands on Profiling tools. - - Timeline is extremely helpful for single-machine system performance analysis, but for distributed training problems, timeline methods face two issues: - 1. Data volume and analysis difficulty: In a 1000-node cluster, Timeline data volume may exceed TB level, making storage and visualization infeasible, and 1000 timelines are also difficult to analyze. - 2. Time precision issues: Distributed node time synchronization has precision problems, and millisecond-level time synchronization errors can mask real communication problems. - -**Technical Approaches to Address These Challenges:** - -1. **Sampling-Based Low-Intrusion Profiling** - - By flexibly adjusting sampling rates, Profiling needs from single-machine debugging to large-scale distributed production tasks can be met while ensuring overall system performance. - -2. **Hybrid Approach of Instrumentation and Sampling** - - For long-running, low-frequency tasks, mix Instrumentation methods on top of sampling, ensuring accuracy of key data while providing sufficient context information to assist fine-tuning. - -3. **Combination of Sampling and Performance Modeling** - - Since pure Sampling methods are insufficient to show complete execution timelines, it's necessary to introduce performance modeling: - - Model theoretical FLOPS (Floating-Point Operations Per Second), memory throughput, and communication volume of training tasks. - - Combine modeling results with sampling data to quantify computing utilization and bandwidth utilization of each model layer, comprehensively evaluating potential masking phenomena between computation, communication, and memory access. - -## Probing's Performance Profiling Solution - -### Solution Introduction - -1. Split model execution into different spans according to its Layer structure. This process can be automatically completed using Torch and Python's reflection features. -2. For each span, time it using sampling while recording span-related metadata, such as computation content within the span, inputs for this computation, etc. -3. For each span, while sampling execution time, also sample underlying hardware counters, such as NCCL communication and memory access. -4. Combine with performance modeling results to evaluate whether actual hardware throughput within each span is reasonable, and calculate computing utilization, memory bandwidth utilization, and interconnect bandwidth utilization within the span. - -Probing's profiling solution essentially replaces horizontal timeline correlation analysis with vertical layered decoupling analysis: - -| | Timeline Method | Probing | -| -------- | --------------- | ------- | -| Analysis Dimension | Horizontal timeline correlation analysis | Vertical layered decoupling analysis | -| Focus | Temporal order relationships of cross-node events | Resource utilization efficiency of each abstraction layer on single node | -| Data Dependency | Requires precise global clock synchronization | Only needs relative timestamps or logical causality | -| Typical Output | Gantt charts, communication timing diagrams | Heat maps, layered utilization radar charts | -| Applicable Scenarios | Deadlock diagnosis, fine-grained communication optimization | Normalized performance monitoring, architecture-level bottleneck location | - -### How to Solve Problems - -#### Basic Performance Problems in Single-Machine Environment - -- Locate inefficient operators: - - Traditional difficulty: Can observe time of each operator, but cannot determine what reasonable time is or how much optimization space exists. - - Solution: - - Analyze MFU of each span top-down through Probing. - - Locate fine-grained spans with lower MFU by descending from top-level spans. -- Insufficient task pipeline masking, computation and data copy not fully overlapped: - - Traditional difficulty: Timeline tools can observe event overlap but cannot quantify masking efficiency. - - Solution: - - Define ideal execution time for pipeline stages (e.g., T_pipeline = max(T_load, T_comp, T_save)). - - Calculate actual efficiency (efficiency = T_pipeline ideal value / T_pipeline actual value), identify tail stages. - -#### Distributed Tuning in Small-Scale Clusters - -- Communication-computation masking failure: - - Traditional difficulty: Communication time is affected by multiple factors such as network topology, message size, protocol type, making it difficult to locate root cause. - - Solution: - - Obtain execution time of a span through Probing. - - Calculate ideal computation time and ideal communication time through performance modeling. - - Calculate communication time exposure ratio = (T_span - max(T_comp, T_c2c))/T_c2c. -- Collective operation delay caused by load imbalance: - - Traditional difficulty: Cannot determine when each node enters collective communication semantics. - - Solution: - - Track time from start to end of each collective communication. - - Group nodes according to node roles, compare collective communication time overhead within groups. - -#### Unique Problems in Ultra-Large-Scale Training - -- Training hangs: - - Traditional difficulty: Difficult to locate which process causes hang among thousands of processes. - - Solution: - - View call stack of each process through probing. - - View internal counters of collective communication library within each process through probing. -- Slow node problem: - - Traditional difficulty: Lack of means to locate which process is slow. - - Solution: - - Draw performance heatmap with probing, locate slow nodes on heatmap. - - Locate nodes frequently waited for by other nodes through communication library's performance counters. - -## References - -[^1]: https://github.com/torvalds/linux/blob/master/tools/perf/Documentation/perf-intel-pt.txt -[^2]: Use `perf list` to view complete list -[^3]: [Nvidia GPU Hardware Performance Counters](https://docs.nvidia.com/gameworks/index.html#developertools/desktop/linux_graphics_debugger/lgd_perf_counters.htm) -[^nsyscomp]: [Hardware Metrics in NSight Compute](https://docs.nvidia.com/nsight-compute/ProfilingGuide/index.html#metrics-reference) -[^5]: [PMU Registers in PTX](https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#special-registers-pm0-pm7) -[^6]: [RDMA Network Card Hardware Counters](https://enterprise-support.nvidia.com/s/article/understanding-mlx5-linux-counters-and-status-parameters) -[^7]: https://github.com/NVIDIA/nccl/blob/master/src/include/nvtx3/nvtxDetail/nvtxExtImplCounters_v1.h -[^8]: https://pytorch.org/tutorials/prototype/flight_recorder_tutorial.html#enabling-flight-recorder -[^9]: https://man7.org/linux/man-pages/man2/perf_event_open.2.html -[^setitimer]: https://linux.die.net/man/2/setitimer -[^cupti_pm]: https://docs.nvidia.com/cupti/main/main.html#cupti-pm-sampling-api -[^nvpmu]: https://docs.nvidia.com/grace-perf-tuning-guide/measuring-performance.html#grace-coresight-system-pmu-units -[^uncore]: https://github.com/torvalds/linux/blob/master/Documentation/admin-guide/perf/nvidia-pmu.rst diff --git a/docs/build.md b/docs/build.md deleted file mode 100644 index bc4b1624..00000000 --- a/docs/build.md +++ /dev/null @@ -1,252 +0,0 @@ -# Building Probing from Source - -This guide provides comprehensive instructions for building Probing from source code, including environment setup, compilation, and verification steps. - -## Prerequisites - -### System Requirements - -- **Operating System**: Linux (Ubuntu 20.04+, CentOS 8+) or macOS -- **Memory**: Minimum 4GB RAM (8GB+ recommended for large projects) -- **Disk Space**: At least 2GB for dependencies and build artifacts - -### Required Dependencies - -Before building Probing, install the following dependencies: - -```bash -# Install Rust toolchain -curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -source ~/.cargo/env - -# Install nightly toolchain (required for advanced features) -rustup toolchain install nightly -rustup default nightly - -# Add WebAssembly target for web UI -rustup target add wasm32-unknown-unknown - -# Install Dioxus CLI for building WebAssembly frontend -cargo install dioxus-cli - -# Install cross-compilation tools (for distribution builds) -cargo install cargo-zigbuild -pip install ziglang -``` - -### Optional Dependencies - -```bash -# For testing and development -cargo install cargo-nextest # Faster test runner -pip install pytest # Python testing framework -``` - -## Building from Source - -### Development Build - -For quick iteration and development: - -```bash -# Clone repository -git clone https://github.com/reiase/probing.git -cd probing - -# Development build (faster compilation, debug symbols) -make - -# Build web UI (optional, included in make) -cd web && dx build --release && cd .. -``` - -### Production Build - -For distribution and deployment: - -```bash -# Production build with cross-platform compatibility -make ZIG=1 - -# Generate Python wheel package -make wheel - -# Install the built package -pip install dist/probing-*.whl --force-reinstall -``` - -## Build Verification - -## Build Verification - -### Basic Functionality Test - -Verify the build by running basic tests: - -```bash -# Run the test suite -make test - -# Verify CLI installation -probing --version - -# Test basic functionality -PROBE=1 python examples/test_probing.py -``` - -### Advanced Feature Testing - -Test advanced features and integrations: - -```bash -# Test PyTorch integration with variable tracking -PROBE_TORCH_EXPRS="loss@train,acc1@train" PROBE=1 python examples/imagenet.py - -# Test distributed monitoring (if available) -probing cluster test - -# Test SQL analytics interface -python -c " -import time -import os -os.environ['PROBE'] = '1' -import probing -# Run your test here -" -``` - -## Build Targets and Options - -### Available Make Targets - -```bash -# Development build (default) -make - -# Production build with cross-compilation -make ZIG=1 - -# Run tests -make test - -# Build Python wheel package -make wheel - -# Build web UI only -make web/dist - -# Clean build artifacts -make clean -``` - -### Environment Variables - -Control the build process with environment variables: - -```bash -# Debug build (faster compilation, larger binaries) -DEBUG=1 make - -# Cross-compilation build -ZIG=1 make - -# Verbose build output -VERBOSE=1 make -``` - -## Project Structure - -Understanding the codebase organization: - -``` -probing/ -├── probing/cli/ # Command-line interface -├── probing/core/ # Core profiling engine -├── probing/extensions/ # Language-specific extensions -│ ├── python/ # Python integration -│ └── cc/ # C++ integration -├── probing/server/ # HTTP API server -├── web/ # Web UI source and build output (Dioxus + WebAssembly) -│ └── dist/ # Web UI build output -├── python/ # Python hooks and bindings -├── examples/ # Usage examples and demos -└── docs/ # Documentation -``` - -## Troubleshooting - -### Common Build Issues - -**Rust nightly not found:** -```bash -rustup toolchain install nightly -rustup default nightly -``` - -**WebAssembly target missing:** -```bash -rustup target add wasm32-unknown-unknown -``` - -**Dioxus CLI installation failed:** -```bash -cargo install dioxus-cli --force -``` - -**Cross-compilation errors:** -```bash -# Ensure ziglang is properly installed -pip install ziglang --upgrade -``` - -### Performance Issues - -**Slow compilation:** -- Use development build: `make` (without ZIG=1) -- Enable parallel compilation: `export MAKEFLAGS="-j$(nproc)"` -- Use faster linker: `sudo apt install lld` and add to ~/.cargo/config.toml - -**Large binary size:** -- Production build automatically enables optimizations -- Strip debug symbols: `strip target/release/probing` - -### Platform-Specific Notes - -**Ubuntu/Debian:** -```bash -# Install additional dependencies -sudo apt update -sudo apt install build-essential pkg-config libssl-dev -``` - -**CentOS/RHEL:** -```bash -# Install development tools -sudo yum groupinstall "Development Tools" -sudo yum install openssl-devel -``` - -**macOS:** -```bash -# Install Xcode command line tools -xcode-select --install -``` - -## Contributing to Build System - -### Adding New Build Targets - -1. Edit `Makefile` to add your target -2. Update this documentation -3. Test on multiple platforms -4. Submit pull request - -### Cross-Platform Testing - -We recommend testing builds on: -- Ubuntu 20.04 LTS -- CentOS 8 -- macOS 12+ -- Windows (via WSL2) - -For detailed contribution guidelines, see [CONTRIBUTING.md](../CONTRIBUTING.md). diff --git a/docs/build.sh b/docs/build.sh deleted file mode 100755 index c9d55293..00000000 --- a/docs/build.sh +++ /dev/null @@ -1,23 +0,0 @@ -#!/bin/bash -# Quick build script - -set -e - -echo "📚 Building Probing documentation..." - -# Check dependencies -if ! python3 -c "import sphinx" 2>/dev/null; then - echo "⚠️ Sphinx not detected, installing dependencies..." - pip install -r requirements_doc.txt -fi - -# Build documentation -echo "🔨 Starting build..." -make html - -echo "✅ Build complete!" -echo "📖 Documentation location: _build/html/index.html" -echo "" -echo "💡 Tip: Use the following commands to open documentation in browser:" -echo " open _build/html/index.html # macOS" -echo " xdg-open _build/html/index.html # Linux" diff --git a/docs/conf.py b/docs/conf.py deleted file mode 100644 index 09e9e999..00000000 --- a/docs/conf.py +++ /dev/null @@ -1,80 +0,0 @@ -# Configuration file for the Sphinx documentation builder. -# -# For the full list of built-in configuration values, see the documentation: -# https://www.sphinx-doc.org/en/master/usage/configuration.html - -# -- Project information ----------------------------------------------------- -# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information - -project = "Probing" -copyright = "2024, reiase" -author = "reiase" -release = "0.2.3" - -# -- General configuration --------------------------------------------------- -# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration - -extensions = [ - "myst_parser", - "sphinx.ext.autodoc", - "sphinx.ext.viewcode", - "sphinx.ext.intersphinx", - "sphinx.ext.todo", - "sphinx_copybutton", - "sphinx_design", -] - -# MyST parser configuration -myst_enable_extensions = [ - "colon_fence", - "deflist", - "dollarmath", - "fieldlist", - "html_admonition", - "html_image", - "linkify", - "replacements", - "smartquotes", - "strikethrough", - "substitution", - "tasklist", -] - -myst_heading_anchors = 3 - -templates_path = ["_templates"] -exclude_patterns = ["_build", "Thumbs.db", ".DS_Store", "*.pyc"] - -# -- Options for HTML output ------------------------------------------------- -# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output - -html_theme = "sphinx_rtd_theme" -html_static_path = ["_static"] -# html_logo = '../probing.svg' # Uncomment if logo exists -html_theme_options = { - "logo_only": False, - "display_version": True, - "prev_next_buttons_location": "bottom", - "style_external_links": False, - "vcs_pageview_mode": "", - "style_nav_header_background": "#2980B9", - # Toc options - "collapse_navigation": True, - "sticky_navigation": True, - "navigation_depth": 4, - "includehidden": True, - "titles_only": False, -} - -# -- Internationalization ---------------------------------------------------- -language = "en" - -# -- Extension configuration ------------------------------------------------- - -# Intersphinx mapping -intersphinx_mapping = { - "python": ("https://docs.python.org/3", None), -} - -# Todo extension -todo_include_todos = True diff --git a/docs/design/README.md b/docs/design/README.md deleted file mode 100644 index b241c374..00000000 --- a/docs/design/README.md +++ /dev/null @@ -1,15 +0,0 @@ -# Design Overview - -## Why Python Dominates AI: The Pythonic Advantage - -Python's dominance in AI stems from one core principle: **everything feels like Python**. Whether you're using pandas, PyTorch, or NumPy, you can **talk to them pythonic**—the same `print()`, iteration, and attribute access patterns work everywhere. Interactive environments like REPL and Jupyter notebooks further reinforce this seamless development experience. - - -## How Distributed Systems Break the Pythonic Experience - -As AI models scale to distributed clusters, something fundamental breaks: **distributed systems aren't Pythonic**. Single-machine debugging feels natural—`print(model.parameters())`, `loss.item()`, `torch.cuda.memory_allocated()`—but distributed debugging forces you into system administration tools: `kubectl get nodes`, SSH sessions, log file parsing, monitoring dashboards. You lose the interactive, consistent, composable experience that makes Python powerful. - - -## Probing: Restoring the Pythonic Experience for Distributed Systems - -Probing's core mission is simple: **make distributed systems feel Pythonic again**. Your cluster, nodes, and distributed processes become Python objects with familiar interfaces—`len(cluster.nodes)`, `node.gpu_usage`, `cluster.training.status`. Interactive exploration, consistent patterns, and natural composition return to distributed debugging. Instead of context-switching between tools, you stay in Python and **talk to your distributed system pythonic**. diff --git a/docs/design/architecture.md b/docs/design/architecture.md deleted file mode 100644 index d622d0d8..00000000 --- a/docs/design/architecture.md +++ /dev/null @@ -1,14 +0,0 @@ -# System Architecture - -Probing is designed as a simple two-layer structure: - -1. **Probe**: Injected into target processes to gain full access to all resources of the target process, including the Python interpreter, files, memory, etc. The probe runs an embedded HTTP server that listens on a Unix domain socket (for local connections) or TCP port (for remote connections). -2. **CLI**: Command-line interface for controlling probes within processes, reading data, or executing code. Communicates with probes via HTTP protocol over Unix domain sockets (local) or TCP (remote). Users can also control probes directly through the HTTP API. - -The overall design minimizes unnecessary components to reduce overall complexity and deployment difficulty. - -Inside the probe, there are three main components: - -1. **Engine**: Contains core data storage and processing capabilities, and provides infrastructure such as configuration management and extension mechanisms. -2. **Server**: Responsible for interaction with the CLI. -3. **Extensions**: Provide performance data and debugging capabilities for the probe. diff --git a/docs/design/debugging.md b/docs/design/debugging.md deleted file mode 100644 index 93b73a80..00000000 --- a/docs/design/debugging.md +++ /dev/null @@ -1 +0,0 @@ -# Debugging Mechanisms diff --git a/docs/design/distributed.md b/docs/design/distributed.md deleted file mode 100644 index 1252d972..00000000 --- a/docs/design/distributed.md +++ /dev/null @@ -1 +0,0 @@ -# Distributed Architecture diff --git a/docs/design/extensibility.md b/docs/design/extensibility.md deleted file mode 100644 index f24b8173..00000000 --- a/docs/design/extensibility.md +++ /dev/null @@ -1,195 +0,0 @@ -# Extensibility Framework - -Probing's core capability lies in its ability to both deeply access target processes to obtain critical performance and fault data, and flexibly inject diagnostic logic and custom code into target processes. To meet diverse monitoring and analysis needs, users can customize and enhance Probing's functionality through its powerful extension system, enabling more effective problem-solving in specific scenarios. - -Probing provides two main extension paths: lightweight Python-based extensions and high-performance Rust-based extensions. Python extensions excel in flexibility in real production environments due to their convenience of not requiring Probing rebuilds, while Rust extensions provide developers with lower-level control and higher execution efficiency, suitable for scenarios with extreme performance requirements or deep system integration needs. - -## Extending Probing with Python - -### Direct Python Calls from SQL - -Probing supports directly calling Python functions or variables in SQL queries as a quick external table: - -```bash -# Call Python standard library functions -probing $ENDPOINT query "SELECT * from python.`time.time()`" - -# Call custom package functions -probing $ENDPOINT query "SELECT * FROM python.`pkg.some_func()`" -``` - -Under the `python` namespace, use backticks "`" to reference Python code. Probing will execute the code and return the result as a table for subsequent SQL execution. - -### Writing Data to Probing from Python - -Probing supports using `dataclass` to define custom data tables: - -```python -from dataclasses import dataclass -from probing.core import table - -@table -@dataclass -class MetricData: - timestamp: int - cpu_usage: float - memory_mb: int - process_name: str - -# Write data -MetricData.append(MetricData(1234567890, 85.2, 1024, "python")) - -# Or more directly -MetricData(1234567890, 85.2, 1024, "python").save() -``` - -Written data can be queried via SQL: -```bash -probing $ENDPOINT query "SELECT * FROM python.metric_data" -``` - -## Rust Extension System - -Using Rust, developers can more directly and deeply extend Probing's data query and processing capabilities. - -### Core Interface - -```rust -pub trait EngineExtension: Debug + Send + Sync + EngineCall + EngineDatasource { - fn name(&self) -> String; // Extension name - fn set(&mut self, key: &str, value: &str) -> Result<...>; // Set configuration - fn get(&self, key: &str) -> Result; // Get configuration - fn options(&self) -> Vec; // List all configuration options -} -``` - -The `EngineDatasource` interface is responsible for feeding data to Probing's data processing core `DataFusion`, while the `EngineCall` interface endows the Probing server with additional remote API call capabilities. This elegantly designed interface system together forms the foundation of Probing's extension system. - -### Data Source Core Interface - EngineDatasource - -Probing's data processing capabilities are built on a flexible data source abstraction. When we talk about performance diagnostics and monitoring, data collection is the foundation of all analysis. The `EngineDatasource` interface is the core of this data bridge, responsible for connecting various data sources to Probing's query engine. - -```rust -pub trait EngineDatasource { - fn datasrc(&self, namespace: &str, name: Option<&str>) - -> Option>; -} -``` - -`EngineExtension` provides data source plugins to Probing by implementing the `EngineDatasource` interface. In Probing, all data is organized and accessed in the form of `namespace.table_name`. Based on this, data source plugins are mainly divided into two types: - -- `CustomTable` (Static table plugin): These plugins provide data tables with fixed structure and content, very suitable for displaying system inherent information, configuration items, or infrequently changing datasets. -- `CustomNamespace` (Dynamic namespace plugin): These plugins can dynamically generate table lists and content based on query requests, suitable for encapsulating complex data sources with frequently changing data structures or content, or interacting with external systems to obtain real-time data. - -These two plugin types together form the skeleton of Probing's data access. Static tables can be imagined as pre-defined, fixed-structure views in a data warehouse, while dynamic namespaces are more like intelligent, on-demand data proxies that can flexibly connect and transform data from various external systems. This two-layer design ensures both ease of use and efficiency when processing simple, static data, and provides sufficient flexibility and powerful functionality for integrating complex, dynamic data sources. - -Through this unified interface design, users can integrate various originally scattered, heterogeneous data sources into Probing's powerful SQL query system. This means developers no longer need to write specialized parsing and processing code for each new data format or data source. Whether monitoring system metrics like CPU usage, analyzing network traffic, checking application logs, or even querying custom business data, all can be accomplished through a standard, consistent SQL interface, greatly simplifying the complexity of data acquisition and analysis. - -This seemingly simple `EngineDatasource` interface contains powerful extension capabilities. It allows Probing to access a wide variety of heterogeneous data sources in a unified and standardized way—from performance counters within the operating system, internal state information exposed by applications, to query results from external databases, and even API responses from remote microservices. When an extension implements this interface, it is actually contributing a new, explorable and analyzable dimension to Probing's vast data universe. Specifically, the interface design brings the following key advantages: - -- **Clear namespace management**: Through the `namespace` parameter, Probing can logically group and isolate data from different sources and types, forming a hierarchical, easily manageable data view. -- **Flexible dynamic data discovery**: The optional design of the `name` parameter allows the `EngineDatasource` interface to support both precise queries for specific data tables and dynamic discovery and enumeration of all available data sources under the entire namespace, which is particularly important for exploring unknown or dynamically changing data environments. -- **Powerful polymorphic plugin system**: The interface's return value uses Rust's trait object design, allowing different types and implementations of data source plugins to be seamlessly integrated into Probing's unified query framework and processed consistently. - -A typical `EngineDatasource` implementation might connect to the operating system's performance counters to collect system load information, query an application's internal state to monitor its health, pull business data from a specialized database, or even call a remote service API to obtain third-party information. Through the adaptation and transformation of the `EngineDatasource` interface, Probing unifies these diverse data sources into structured, SQL-queryable table forms, enabling developers to use familiar SQL syntax for complex, cross-data-source deep data analysis and insights. - -### Data Source Plugins - -When we delve deeper into Probing's data access mechanism, we find that its true power comes from diverse data source plugin implementations. Probing provides two core plugin types, each handling different data access responsibilities. - -**Static Table Plugin (TablePlugin):** - -Static table plugins are the most intuitive data providers in Probing's extension system. They are like pre-defined data views with fixed structure and content, very suitable for displaying system configuration, static information, or cached data. - -```rust -pub trait CustomTable { - fn name() -> &'static str; // Table name - fn schema() -> SchemaRef; // Table schema - fn data() -> Vec; // Data batches -} - -// Register plugin -let plugin = TablePluginHelper::::create("namespace", "table_name"); -``` - -Plugins implementing this trait can provide data access capabilities with minimal code. Developers only need to define the table name, data structure, and content, and Probing will automatically handle SQL query parsing, data filtering, and result return operations. This simplicity makes it very easy to quickly develop data tables for specific purposes. - -In performance diagnostic scenarios, static tables can be used to display system basic information, such as CPU core count, memory configuration, runtime environment variables, or compilation options. Although simple, this information is crucial for understanding the overall system state. - -**Dynamic Table Plugin (SchemaPlugin):** -```rust -#[async_trait] -pub trait CustomNamespace { - async fn table_names(&self) -> Result>; - async fn table(&self, name: &str) -> Result>; -} -``` - -Unlike static tables, dynamic namespace plugins provide greater flexibility. They can dynamically generate table lists and content based on query requests, enabling real-time interaction with external data sources. This design is very suitable for handling changing datasets, such as real-time monitoring metrics, log streams, or remote API responses. - -The core advantage of dynamic namespaces lies in their adaptive capability. The `table_names` method allows plugins to dynamically discover available data tables based on the current environment, while the `table` method is responsible for creating query interfaces for specific tables on demand. This lazy loading strategy not only improves resource utilization efficiency but also enables plugins to adapt to complex and changing data source environments. - -In practical applications, a dynamic namespace plugin might connect to the operating system's process list and dynamically create independent data tables for each process, displaying their resource usage, open file handles, or memory mappings. This dynamically generated table collection provides developers with a rich and precise system view. - -### API Interface Plugin - -In addition to data source plugins, Probing also provides another powerful extension point: API interfaces. Through the `EngineCall` trait, extensions can add custom HTTP endpoints to the Probing server, implementing more complex interaction logic and command execution capabilities. - -```rust -#[async_trait] -pub trait EngineCall { - async fn call( - &self, - path: &str, // API path - params: &HashMap, // Query parameters - body: &[u8], // Request body - ) -> Result, EngineError>; // Response data -} -``` - -This interface design is concise and flexible, similar to a miniature web service framework. Extensions can implement this trait to handle various requests from clients: - -- Execute dynamically generated code snippets -- Inject diagnostic logic into target processes -- Provide interactive debugging control -- Modify monitoring parameters in real-time -- Export analysis results to external formats - -The combination of API interfaces and data sources forms Probing's complete capability loop: data sources provide the "eyes" to observe system state, while API interfaces are the "hands" to intervene in system behavior. Through the synergy of these two capabilities, extensions can implement complete workflows from problem discovery to fault diagnosis to dynamic repair. - -**Existing Rust Extension Examples:** -- **C/C++ Extension**: Native code debugging, memory analysis, system call tracing -- **Python Extension**: REPL, flame graphs, call stack tracing, object inspection -- **Torch Extension**: PyTorch model monitoring and performance analysis - -### Configuration Management - -```rust -// Extension configuration option definition -pub struct EngineExtensionOption { - pub key: String, // Configuration key - pub value: Option, // Current value - pub help: &'static str, // Help information -} - -// Runtime configuration -manager.set_option("my_option", "new_value")?; -let value = manager.get_option("my_option")?; -``` - -## Extension Development Guide - -### Python Extension Development - -1. **Lightweight Extension**: Write Python functions directly, call them in SQL -2. **Heavy Extension**: Use `@table` decorator to create data tables with persistence support -3. **Test Integration**: Ensure data type conversion is correct and SQL queries work normally - -### Rust Extension Development - -1. **Implement Core Traits**: Implement `EngineCall` and/or `EngineDatasource` based on requirements -2. **Define Configuration Options**: List all configurable parameters in the `options()` method -3. **Register Plugin**: Register to the system through `EngineExtensionManager` -4. **Performance Optimization**: Leverage Arrow columnar storage and DataFusion query optimization - -The extension system is deeply integrated with the DataFusion query engine, supporting standard SQL syntax to access all data sources provided by extensions. diff --git a/docs/design/profiling.md b/docs/design/profiling.md deleted file mode 100644 index 311e472f..00000000 --- a/docs/design/profiling.md +++ /dev/null @@ -1 +0,0 @@ -# Profiling Implementation diff --git a/docs/development/building.md b/docs/development/building.md deleted file mode 100644 index 2d5f8bb3..00000000 --- a/docs/development/building.md +++ /dev/null @@ -1,453 +0,0 @@ -# Building from Source - -This guide covers building Probing from source code, including all dependencies and components. - -## Prerequisites - -### Required Tools - -**Rust Toolchain:** -```bash -# Install Rust via rustup -curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -source ~/.cargo/env - -# Verify installation -rustc --version -cargo --version -``` - -**Python Development:** -```bash -# Python 3.7+ required -python3 --version - -# Development dependencies -pip install setuptools wheel build -``` - -**System Dependencies:** -```bash -# Ubuntu/Debian -sudo apt update -sudo apt install build-essential pkg-config libssl-dev - -# CentOS/RHEL/Fedora -sudo yum groupinstall "Development Tools" -sudo yum install openssl-devel - -# macOS (with Homebrew) -brew install pkg-config openssl -``` - -### Optional Tools - -**For cross-platform builds:** -```bash -# Install Zig for better compatibility -cargo install cargo-zigbuild -``` - -**For web UI development:** -```bash -# Install Dioxus CLI for WebAssembly builds -cargo install dioxus-cli -``` - -## Quick Build - -### Standard Build - -Clone and build the project: -```bash -# Clone repository -git clone https://github.com/reiase/probing.git -cd probing - -# Build all components -make - -# Verify build -./target/release/probing --version -``` - -### Development Build - -For faster development cycles: -```bash -# Development build (debug mode) -cargo build - -# Build specific components -cargo build -p probing-cli # CLI only -cargo build -p probing-core # Core library -cargo build -p probing-server # HTTP server -``` - -## Detailed Build Process - -### Core Components - -**1. Probing Core Library:** -```bash -cd probing/core -cargo build --release -``` - -**2. Command Line Interface:** -```bash -cd probing/cli -cargo build --release -``` - -**3. Python Extension:** -```bash -cd probing/extensions/python -cargo build --release -``` - -**4. Server Component:** -```bash -cd probing/server -cargo build --release -``` - -### Python Wheel Package - -**Build Python wheel:** -```bash -# Standard build -python make_wheel.py - -# Cross-platform build with Zig -make ZIG=1 - -# Install locally -pip install dist/probing-*.whl --force-reinstall -``` - -### Web UI (Optional) - -**Build web interface:** -```bash -cd web -dx build --release -``` - -The web UI is built as WebAssembly using Dioxus and provides a graphical interface for Probing. The build output is in `web/dist/` for packaging. - -## Build Configurations - -### Release Build - -For production deployment: -```bash -# Optimized release build -cargo build --release - -# Full production build with all optimizations -make ZIG=1 -``` - -### Debug Build - -For development and debugging: -```bash -# Debug build with symbols -cargo build - -# With additional debug info -RUSTFLAGS="-C debug-assertions=on" cargo build -``` - -### Feature Flags - -Enable specific features during build: -```bash -# Build with all features -cargo build --all-features - -# Build with specific features -cargo build --features "python-ext,server" - -# Build minimal version -cargo build --no-default-features -``` - -## Cross-Platform Building - -### Linux Distributions - -**For older glibc compatibility:** -```bash -# Use Zig for better compatibility -make ZIG=1 - -# Manual Zig build -cargo zigbuild --release --target x86_64-unknown-linux-gnu -``` - -**Static linking:** -```bash -# Static binary (self-contained) -RUSTFLAGS="-C target-feature=+crt-static" cargo build --release --target x86_64-unknown-linux-musl -``` - -### macOS - -```bash -# Standard macOS build -cargo build --release - -# Universal binary (Intel + Apple Silicon) -cargo build --release --target x86_64-apple-darwin -cargo build --release --target aarch64-apple-darwin -``` - -### Windows - -```bash -# Windows build (from Linux using cross-compilation) -cargo build --release --target x86_64-pc-windows-gnu -``` - -## Testing the Build - -### Basic Functionality - -```bash -# Run test suite -make test - -# Verify CLI installation -./target/release/probing --version - -# Test basic functionality -PROBING=1 python examples/test_probing.py -``` - -### Advanced Testing - -```bash -# Test PyTorch integration -PROBING_TORCH_PROFILING="on,exprs=loss@train,acc1@train" PROBING=1 python examples/imagenet.py - -# Test SQL interface -./target/release/probing $ENDPOINT query "SELECT * FROM information_schema.df_settings" - -# Test web UI (if built) -cd web && dx serve -``` - -### Performance Testing - -```bash -# Benchmark core components -cargo bench - -# Memory usage testing -valgrind ./target/release/probing $ENDPOINT inject - -# Load testing -./scripts/load_test.sh -``` - -## Installation - -### System-wide Installation - -```bash -# Install to system location -sudo cp target/release/probing /usr/local/bin/ -sudo cp target/release/libprobing.so /usr/local/lib/ - -# Update library cache -sudo ldconfig -``` - -### User Installation - -```bash -# Install to user directory -mkdir -p ~/.local/bin ~/.local/lib -cp target/release/probing ~/.local/bin/ -cp target/release/libprobing.so ~/.local/lib/ - -# Add to PATH if needed -echo 'export PATH="$HOME/.local/bin:$PATH"' >> ~/.bashrc -``` - -### Python Package Installation - -```bash -# Install Python wheel -pip install dist/probing-*.whl --force-reinstall - -# Verify Python integration -python -c "import probing; print('Success')" -``` - -## Development Setup - -### IDE Configuration - -**VS Code setup:** -```json -// .vscode/settings.json -{ - "rust-analyzer.cargo.features": ["all"], - "rust-analyzer.check.command": "clippy" -} -``` - -**Environment variables:** -```bash -# Development environment -export RUST_LOG=debug -export PROBING_LOGLEVEL=debug -export RUST_BACKTRACE=1 -``` - -### Pre-commit Hooks - -```bash -# Install pre-commit hooks -pip install pre-commit -pre-commit install - -# Manual format and lint -cargo fmt -cargo clippy -- -D warnings -``` - -## Troubleshooting - -### Common Build Issues - -**Linker errors:** -```bash -# Install required system libraries -sudo apt install build-essential pkg-config libssl-dev - -# Check linker configuration -rustc --print cfg -``` - -**Python extension issues:** -```bash -# Ensure Python headers are available -sudo apt install python3-dev - -# Check Python version compatibility -python3 --version -``` - -**Cross-compilation issues:** -```bash -# Install cross-compilation target -rustup target add x86_64-unknown-linux-musl - -# Use Zig for better compatibility -cargo install cargo-zigbuild -cargo zigbuild --release -``` - -### Performance Issues - -**Slow builds:** -```bash -# Use more CPU cores -export CARGO_BUILD_JOBS=8 - -# Enable parallel frontend -export RUSTFLAGS="-C codegen-units=16" - -# Use faster linker (Linux) -sudo apt install lld -export RUSTFLAGS="-C link-arg=-fuse-ld=lld" -``` - -**Memory usage during build:** -```bash -# Reduce memory usage -export RUSTFLAGS="-C opt-level=1" - -# Build with reduced parallelism -cargo build --jobs 2 -``` - -## Project Structure - -Understanding the codebase organization: - -``` -probing/ -├── probing/cli/ # Command-line interface -├── probing/core/ # Core profiling engine -├── probing/extensions/ # Language-specific extensions -│ ├── python/ # Python integration -│ └── cc/ # C++ integration -├── probing/server/ # HTTP API server -├── web/ # Web UI source and build output (Dioxus + WebAssembly) -│ └── dist/ # Web UI build output -├── python/ # Python hooks and bindings -├── examples/ # Usage examples and demos -└── docs/ # Documentation -``` - -### Key Files - -- `Cargo.toml` - Main workspace configuration -- `Makefile` - Build automation -- `make_wheel.py` - Python package builder -- `probing/cli/src/main.rs` - CLI entry point -- `probing/core/src/lib.rs` - Core library -- `python/probing/__init__.py` - Python package entry - -## Contributing to the Build System - -### Adding New Features - -1. **Add Cargo features:** -```toml -# In Cargo.toml -[features] -new-feature = ["dep:some-crate"] -``` - -2. **Update build scripts:** -```bash -# In Makefile -build-new-feature: - cargo build --features new-feature -``` - -3. **Test integration:** -```bash -# Add tests -cargo test --features new-feature -``` - -### Build Optimizations - -**Profile-guided optimization:** -```bash -# Generate profile data -RUSTFLAGS="-C profile-generate=/tmp/pgo-data" cargo build --release - -# Run representative workload -./target/release/probing - -# Build with profile data -RUSTFLAGS="-C profile-use=/tmp/pgo-data" cargo build --release -``` - -**Link-time optimization:** -```bash -# Enable LTO in Cargo.toml -[profile.release] -lto = true -codegen-units = 1 -``` - -For development workflow and contribution guidelines, see the main [README.md](../README.md) and [Contributing Guide](contributing.md). diff --git a/docs/getting-started/introduction.md b/docs/getting-started/introduction.md deleted file mode 100644 index 596df6d8..00000000 --- a/docs/getting-started/introduction.md +++ /dev/null @@ -1,452 +0,0 @@ -# Introduction - -Welcome to **Probing** - a dynamic performance profiler and stability diagnostic tool designed specifically for AI applications. Probing addresses the unique challenges of debugging and optimizing large-scale, distributed, long-running AI workloads such as LLM training and inference. - -## What is Probing? - -Probing is a runtime analysis tool that injects probes into running Python processes to collect detailed performance data and enable real-time monitoring without requiring code modifications or process restarts. Think of it as a powerful magnifying glass for your AI applications that reveals exactly what's happening under the hood. - -## Core Principles - -Probing is built on three fundamental principles: - -### 🔍 **Zero Intrusion** -- No code modifications required -- No environment setup changes needed -- No workflow disruptions -- Dynamic probe injection into running processes - -### 🎯 **Zero Learning Curve** -- Standard SQL interface for data analysis -- Familiar database query patterns -- Intuitive command-line tools -- Web-based dashboard for visualization - -### 📦 **Zero Deployment Burden** -- Single binary deployment (Rust-based) -- Static compilation with minimal dependencies -- Linux-first design with query/eval support on other platforms -- Elastic scaling capabilities - -## Key Features - -### 🎯 **Live Code Execution (`eval`)** -- **Arbitrary Python Code**: Run any Python code inside your target process -- **Real-time State Inspection**: Check variables, objects, and system state instantly -- **Dynamic Behavior Modification**: Change configuration, clear caches, or trigger actions -- **Custom Metrics Collection**: Gather any data your application can compute - -### 📊 **SQL-Based Analytics (`query`)** -- **Structured Performance Data**: Query PyTorch traces, call stacks, and system metrics -- **Familiar SQL Interface**: Use standard SQL syntax with powerful time-series functions -- **Real Tables, Real Data**: Query actual collected data, not synthetic examples -- **Cross-correlation Analysis**: Understand relationships between performance metrics - -### 🔍 **Deep Stack Analysis (`backtrace`)** -- **Real-time Call Stack Capture**: Get live execution context with variable values from the main thread -- **Python Variable Inspection**: See current local variables, function arguments, and object state -- **Live Stack Analysis**: Query current execution state of the main thread -- **Main Thread Focus**: Analyze the primary execution path and main thread activity - -### 🚀 **Production-Ready Foundation** -- **Zero-Intrusion Injection**: Attach to running processes without code changes (Linux only) -- **Minimal Performance Impact**: < 5% overhead in most real-world scenarios -- **Platform Support**: Core injection features require Linux; query and eval work on all platforms -- **Distributed System Ready**: Monitor processes across multiple machines - -## Three Core Capabilities - -Probing provides three powerful capabilities that work together to give you complete insight into your running applications: - -> **📝 Note**: All examples below use `$ENDPOINT` to represent your target process. Set this to your process ID (e.g., `export ENDPOINT=12345`) or remote address (e.g., `export ENDPOINT=host:port`). See the [Quick Start](#quick-start-your-first-5-minutes) section for detailed setup instructions. - -### 🎯 **eval**: Execute Code in Live Processes -Run arbitrary Python code directly inside your target process to inspect state, modify behavior, or gather custom metrics. - -```bash -# Check training threads -probing $ENDPOINT eval "import threading; [print(f'{t.name}: {t.is_alive()}') for t in threading.enumerate()]" -# Expected output: -# MainThread: True -# Thread-1 (train_worker): True -# Thread-2 (data_loader): True - -# Check GPU memory usage -probing $ENDPOINT eval "import torch; print(f'GPU: {torch.cuda.memory_allocated()/1024**3:.1f}GB allocated, {torch.cuda.memory_reserved()/1024**3:.1f}GB cached') if torch.cuda.is_available() else print('CUDA unavailable')" -# Expected output (with GPU): -# GPU: 2.4GB allocated, 3.2GB cached -# Expected output (without GPU): -# CUDA unavailable -``` - -### 📊 **query**: Analyze Data with SQL -Query structured performance data using familiar SQL syntax to identify patterns, bottlenecks, and trends. - -```bash -# Analyze PyTorch training patterns -probing $ENDPOINT query " -SELECT - step, - module, - SUM(allocated) as total_memory_mb, - COUNT(*) as operation_count -FROM python.torch_trace -WHERE step > 100 -GROUP BY step, module -ORDER BY total_memory_mb DESC -LIMIT 10" -# Expected output: -# step | module | total_memory_mb | operation_count -# ------|------------------|-----------------|---------------- -# 150 | transformer.attn | 2048.5 | 24 -# 149 | transformer.attn | 2047.8 | 24 -# 151 | transformer.mlp | 1536.2 | 16 -# 150 | transformer.mlp | 1535.9 | 16 -# ... -``` - -### 🔍 **backtrace**: Debug with Stack Context -Capture detailed call stacks with Python variable values to understand exactly what your main thread is doing at any moment. - -```bash -# Capture current call stack from the main thread -probing $ENDPOINT backtrace - -# Query the live main thread stack trace with variable inspection -probing $ENDPOINT query "SELECT func, file, lineno FROM python.backtrace ORDER BY depth LIMIT 3" -# Expected output: -# func | file | lineno -# ---------------|-----------------------|-------- -# forward_pass | /app/training_loop.py | 89 -# transformer | /app/model.py | 245 -# multi_head_attn| /app/attention.py | 156 - -# The python.backtrace table shows main thread execution context: -# - func: function name -# - file: source file path -# - lineno: line number -# - depth: call stack depth (0 = deepest frame) -``` - ---- - -## Real-World Debugging Scenarios - -Now that you understand the three core capabilities, let's see how they work together to solve common AI/ML debugging challenges: - -### Scenario 1: Training Process Hanging -**Problem**: PyTorch training suddenly stops progressing. -**Solution**: Use the three capabilities in sequence: - -```bash -# 1. BACKTRACE: See what main thread is doing right now -probing $ENDPOINT backtrace - -# 2. EVAL: Check broader system state -probing $ENDPOINT eval "import threading; [(t.name, t.is_alive(), t.daemon) for t in threading.enumerate()]" -# Expected output: -# [('MainThread', True, False), ('Thread-1', False, False), ('Thread-2', True, True)] - -# 3. QUERY: Analyze the captured stack context -probing $ENDPOINT query "SELECT func, file, lineno, depth FROM python.backtrace ORDER BY depth LIMIT 10" -# Expected output: (shows top 10 stack frames) -# func | file | lineno | depth -# -------------|-------------------|--------|------- -# wait_for_data| /app/dataloader.py| 234 | 0 -# get_batch | /app/training.py | 89 | 1 -# train_step | /app/main.py | 156 | 2 -# ... (up to 10 frames total) -``` - -### Scenario 2: Memory Leak Investigation -**Problem**: Memory usage keeps growing during training. -**Solution**: Monitor, analyze, then correlate: - -```bash -# EVAL: Force cleanup and get current state -probing $ENDPOINT eval "import gc, torch; gc.collect(); torch.cuda.empty_cache() if torch.cuda.is_available() else None; print('Cleanup complete')" -# Expected output: -# Cleanup complete - -# QUERY: Analyze allocation trends over recent training steps -probing $ENDPOINT query "SELECT step, AVG(allocated) as avg_memory, MAX(allocated) as peak_memory FROM python.torch_trace WHERE step >= (SELECT MAX(step) - 20 FROM python.torch_trace) GROUP BY step ORDER BY step" -# Expected output: -# step | avg_memory | peak_memory -# -----|------------|------------- -# 980 | 1024.5 | 1156.8 -# 981 | 1026.2 | 1158.9 -# 982 | 1028.7 | 1161.4 # ← Memory growing! -# 983 | 1031.1 | 1164.2 -# ... -``` - -### Scenario 3: Performance Bottleneck Analysis -**Problem**: Need to identify which model components are slowest. -**Solution**: Real-time profiling with contextual analysis: - -```bash -# BACKTRACE: Capture execution state during slow periods -probing $ENDPOINT backtrace - -# QUERY: Find most expensive operations across recent steps -probing $ENDPOINT query "SELECT module, stage, AVG(allocated) as avg_memory, COUNT(*) as frequency FROM python.torch_trace WHERE step >= (SELECT MAX(step) - 5 FROM python.torch_trace) GROUP BY module, stage ORDER BY avg_memory DESC LIMIT 10" -# Expected output: -# module | stage | avg_memory | frequency -# ---------------------|----------|------------|---------- -# transformer.self_attn| forward | 2048.7 | 25 -# transformer.mlp | forward | 1536.4 | 20 -# embedding_layer | forward | 512.8 | 15 -# layer_norm | forward | 64.2 | 40 -# ... - -# EVAL: Get real-time system metrics for correlation -probing $ENDPOINT eval "import psutil; proc = psutil.Process(); print(f'CPU: {proc.cpu_percent():.1f}%, Memory: {proc.memory_info().rss/1024**3:.2f}GB, Threads: {proc.num_threads()}')" -# Expected output: -# CPU: 85.3%, Memory: 12.45GB, Threads: 8 -```` - -## Quick Start: Your First 5 Minutes - -Get immediate value from Probing with this streamlined workflow: - -### Step 1: Set Your Target Process - -All Probing commands need a target endpoint. Set `$ENDPOINT` to either a local process ID or remote address: - -```bash -# Local process - find and set your Python process ID -export ENDPOINT=$(pgrep -f "python.*your_script") -# Expected result: ENDPOINT now contains your process ID (e.g., 12345) - -# Or for remote processes -export ENDPOINT=remote-host:8080 -# Expected result: ENDPOINT set to remote address -``` - -> **💡 Need help finding processes?** Use `ps aux | grep python` or `pgrep -f "python.*train"` to locate your target. - -### Step 2: Connect and Explore (30 seconds) - -```bash -# Connect to your process (Linux only) -probing $ENDPOINT inject -# Expected output: -# Successfully injected probes into process 12345 - -# Get basic process info -probing $ENDPOINT eval "import os, psutil; proc = psutil.Process(); print(f'PID: {os.getpid()}, Memory: {proc.memory_info().rss/1024**2:.1f}MB')" -# Expected output: -# PID: 12345, Memory: 1250.4MB -``` - -### Step 3: Try All Three Core Capabilities (2 minutes) - -**📊 Query structured data:** -```bash -probing $ENDPOINT query "SELECT name, value FROM information_schema.df_settings LIMIT 5" -# Expected output: -# name | value -# ------------------------|-------- -# max_threads | 8 -# memory_pool_size | 2048 -# cache_enabled | true -# debug_mode | false -# profiling_interval | 100 -``` - -**🎯 Execute live code:** -```bash -probing $ENDPOINT eval "import torch; print(f'CUDA: {torch.cuda.is_available()}') if 'torch' in globals() else print('PyTorch not loaded')" -# Expected output (with PyTorch): -# CUDA: True -# Expected output (without PyTorch): -# PyTorch not loaded -``` - -**🔍 Capture execution context:** -```bash -probing $ENDPOINT backtrace - -probing $ENDPOINT query "SELECT func, file, lineno FROM python.backtrace ORDER BY depth LIMIT 5" -# Expected output: -# func | file | lineno -# ---------------|-----------------------|-------- -# forward | /app/model.py | 89 -# train_step | /app/training.py | 156 -# main_loop | /app/main.py | 234 -# ... -``` - -### Step 4: Real Debugging Workflow (2 minutes) - -Combine all capabilities to debug like a pro: -```bash -# 1. Capture current state -probing $ENDPOINT backtrace - -# 2. Check what's happening now -probing $ENDPOINT eval "import threading; print('Active threads:', len(threading.enumerate()))" -# Expected output: -# Active threads: 5 - -# 3. Analyze the results -probing $ENDPOINT query "SELECT func, file, lineno FROM python.backtrace ORDER BY depth" -# Expected output: -# func | file | lineno -# --------------------|-------------------------|-------- -# compute_loss | /app/loss.py | 45 -# forward_pass | /app/model.py | 123 -# train_batch | /app/training.py | 89 -# main_loop | /app/main.py | 234 -# | /app/main.py | 15 -``` - -**🎉 That's it!** You're now using all three core capabilities. Continue reading for advanced techniques and real-world scenarios. - ---- - -## Detailed Setup Guide - -For production deployments and distributed setups, here's the complete configuration: - -### Local Process Discovery -```bash -# Find your training process -ps aux | grep python | grep train -# Expected output: -# user 12345 15.2 8.5 2048576 1048576 ? S 10:30 0:05 python train_model.py -# user 12346 2.1 1.2 524288 131072 ? S 10:31 0:01 python data_loader.py - -# Or use pgrep for specific patterns -pgrep -f "python.*train" -# Expected output: -# 12345 -# 12346 -``` - -### Remote Process Setup -For distributed setups, target processes need network server enabled: -```bash -# On the remote machine - start your Python process with remote server -PROBING_PORT=8080 python your_training_script.py -# Expected output: -# Probing server listening on 0.0.0.0:8080 -# Starting training... - -# From your local machine - connect directly to the remote process -export ENDPOINT=remote-host:8080 -# Expected result: Commands now target the remote process -``` - -### Process Launch Options -```bash -# Option A: Launch your application with probing enabled -PROBING=1 python your_app.py -# Expected output: -# Probing enabled for process 12345 -# Your application output follows... - -# Option B: Attach to an already running process (recommended, Linux only) -probing $ENDPOINT inject -# Expected output: -# Successfully injected probes into process 12345 -``` - ---- - -## Who Should Use Probing? - -Based on the capabilities and scenarios above, Probing is designed for different roles in AI/ML teams: - -### AI/ML Engineers -**Debug training instabilities and optimize model performance** -- "Why did my training suddenly diverge at step 15,000?" → Use `backtrace` + `query` to see exact main thread state -- "Which layer is using the most GPU memory?" → Use `eval` to inspect torch memory + `query` torch_trace -- "Is my data loader causing bottlenecks?" → Use `eval` to check thread states and `backtrace` for main thread analysis - -### DevOps Engineers -**Monitor production AI services and troubleshoot issues** -- "Service is using 90% CPU but I can't reproduce it" → Use `inject` + `eval` to inspect live production -- "Memory usage keeps growing, is it a leak?" → Use `eval` + `query` for memory trend analysis -- "Which requests are taking the longest?" → Use `backtrace` to capture current main thread execution state of slow requests - -### Research Scientists -**Profile experimental models and analyze performance** -- "How does my new attention mechanism compare?" → Use `query` to analyze torch_trace data -- "Are my optimizations actually faster?" → Use `eval` for before/after benchmarks -- "What's the memory footprint of different model sizes?" → Use `query` for memory analytics - -### Platform Engineers -**Build monitoring infrastructure and optimize resource allocation** -- "Need custom metrics for our ML platform" → Use `eval` to collect any Python data -- "Want to track training progress across the fleet" → Use `query` for aggregated analytics -- "Need to debug distributed training issues" → Use `backtrace` to inspect live main thread execution state, combined with `eval` for multi-thread analysis - ---- - -## How Probing Works: Architecture Overview - -Understanding Probing's architecture helps you use it more effectively: - -### Data Plane (Probes) -- Lightweight probing components injected into target processes -- Collect performance metrics and system data -- Minimal performance overhead (<5% in most cases) -- Distributed architecture with no single point of failure - -### Control Plane (Interface) -- Command-line tools for scripting and automation (what you've been using above) -- Web UI for visualization and dashboard creation (Dioxus-based) -- HTTP API for integration with other tools (over Unix socket or TCP) -- SQL query engine for data analysis (built on DataFusion) - -This architecture enables the zero-intrusion, zero-setup experience you've seen in the examples above. - ---- - -## Comparison with Other Tools - -| Feature | Probing | Traditional Profilers | APM Solutions | -|---------|---------|---------------------|---------------| -| **AI/ML Focus** | ✅ Native PyTorch support | ❌ Generic profiling | ⚠️ Limited ML features | -| **Zero Setup** | ✅ Dynamic injection | ❌ Code instrumentation | ❌ Agent installation | -| **SQL Interface** | ✅ Full SQL support | ❌ Limited querying | ⚠️ Vendor-specific | -| **Real-time** | ✅ Live monitoring | ⚠️ Post-mortem analysis | ✅ Real-time | -| **Distributed** | ✅ Multi-node support | ❌ Single process | ✅ Distributed | -| **Cost** | ✅ Open source | ⚠️ Mixed | ❌ Expensive | - -## Getting Started - -Ready to dive deeper? Here's your recommended learning path: - -### 🚀 **Start Here** (Essential - 30 minutes) -1. **[Installation](installation.md)** - Set up Probing on your system - -### 🎯 **Deep Dive** (Choose based on your needs) -- **[SQL Analytics](../user-guide/sql-analytics.md)** - Advanced `query` techniques and data analysis -- **[Memory Analysis](../user-guide/memory-analysis.md)** - Tools for analyzing memory usage -- **[Debugging Guide](../user-guide/debugging.md)** - Expert `backtrace` usage for complex issues - -### 🔧 **Production & Advanced** (When you're ready to scale) -- **[Production Deployment](../deployment/production.md)** - Scale Probing for production workloads -- **[Performance Tuning](../user-guide/performance.md)** - Optimize Probing itself for minimal overhead -- **[Building from Source](../development/building.md)** - Build Probing from source code - -### 💡 **Learn from Examples** -Browse `examples/` directory for real-world patterns: -- **Training debugging workflows** - Common PyTorch issues and solutions -- **Production monitoring setups** - Real deployment patterns -- **Custom analytics queries** - Advanced SQL patterns for ML workloads - -## Community and Support - -- **GitHub Repository**: [github.com/reiase/probing](https://github.com/reiase/probing) -- **Documentation**: Browse this guide for comprehensive information -- **Examples**: Check the `examples/` directory for real-world usage patterns -- **Issues**: Report bugs and feature requests on GitHub - -Probing is actively developed and welcomes community contributions. Whether you're fixing bugs, adding features, or improving documentation, your help makes Probing better for everyone. - ---- - -**Next**: Learn how to [install Probing](installation.md) on your system. diff --git a/docs/index.rst b/docs/index.rst deleted file mode 100644 index 48efb110..00000000 --- a/docs/index.rst +++ /dev/null @@ -1,51 +0,0 @@ -.. Probing documentation master file - -Welcome to Probing's documentation! -=================================== - -.. toctree:: - :maxdepth: 2 - :caption: Getting Started - - getting-started/introduction - getting-started/installation - -.. toctree:: - :maxdepth: 2 - :caption: User Guide - - user-guide/sql-analytics - user-guide/memory-analysis - user-guide/debugging - user-guide/troubleshooting - -.. toctree:: - :maxdepth: 2 - :caption: System Design - - design/README - design/architecture - design/debugging - design/profiling - design/distributed - design/extensibility - -.. toctree:: - :maxdepth: 2 - :caption: Advanced Topics - - advanced/architecture - advanced/performance - -.. toctree:: - :maxdepth: 2 - :caption: Development - - development/building - -Indices and tables -================== - -* :ref:`genindex` -* :ref:`modindex` -* :ref:`search` diff --git a/docs/intro.typ b/docs/intro.typ deleted file mode 100644 index a0c8dea8..00000000 --- a/docs/intro.typ +++ /dev/null @@ -1,95 +0,0 @@ -#import "@preview/touying:0.6.1": * -#import themes.metropolis: * - -#import "@preview/numbly:0.1.0": numbly - -#show: metropolis-theme.with( - aspect-ratio: "16-9", - footer: self => self.info.institution, - config-info( - title: [Probing 分布式探针系统], - subtitle: [一种分布式问题与性能诊断工具], - author: [侯杰], - date: datetime.today(), - institution: [昆仑芯], - logo: emoji.city, - ), -) - -#set heading(numbering: numbly("{1}.", default: "1.1")) -#set text(font: "Source Han Serif") - -#title-slide() - -= Probing 简介 - -== 什么是 Probing？ - - *Probing* 是一款Python与AI应用的*性能与稳定性诊断工具*。 - - 旨在解决大规模、分布式、长周期AI异构计算任务（如LLM训练和推理）中的*调试与优化难题*。 - -== 为何需要Probing - - === 大规模训练的现实困境 - - *高昂成本*：千卡训练，效率问题直接转化为经济损失。 - - *故障难追*：训练偶发性hang住或崩溃，原因追踪困难。 - - *性能瓶颈*：节点间性能差异、分布式通信瓶颈，难以定位。 - - *调试低效*：停机调试意味着浪费珍贵的计算资源。 - - --- - - === 现有工具的局限性 - - *单机工具限制*：VTune/Nsight等优秀工具仅限单机分析，无法扩展至分布式场景。 - - *PyTorch内置Profiler困境*： - - 性能开销大：全量采集模式显著影响训练速度。 - - 数据爆炸：大规模集群环境下产生TB级数据，分析困难。 - - 缺乏节点间协调：难以进行有效的分布式诊断。 - - *Timeline方法瓶颈*： - - 数据量庞大导致可视化分析困难。 - - 难以刻画系统统计特性（如P99分位耗时）。 - - 不易发现分布式环境中的负载不均等现象。 - -= Probing 核心设计理念 ---- - - *零侵入*：无需代码改造、环境适配或流程变更，通过动态探针实现透明接入。 - - *零认知门槛*：采用标准SQL交互，将复杂性能分析转化为直观的数据库查询。 - - *零部署负担*：基于Rust的极简静态编译，实现单文件部署与弹性扩展。 - - *核心技术要素*： - - 动态代码注入 (Instrumentation) - - 按需智能采样 (Sampling) - - 原生分布式设计 - -= 整体架构 ---- - - 简洁可靠的*两层设计*： - - *探针平面 (Probe Plane)*：作为动态链接库(.so)注入目标进程，负责核心数据采集、代码注入、内置查询引擎及功能扩展。 - - *控制平面 (Control Plane)*：提供用户交互界面，包括Web UI、命令行工具(CLI)和统一API。 - -= 核心功能 - == 堆栈分析 (Stack Analysis) - - *Python 调用栈捕获*：获取Python调用栈、调用参数和局部变量； - - *Native 调用栈捕获*：获取C/C++调用堆栈信息； - - #figure( - image("imgs/stacks.png",width: 60%), - caption: [堆栈分析示意图] - ) - - == 性能分析 (Profiling) - - *Torch Profiling*： - - 将模型执行按其Layer结构，自动拆分成不同的Span； - - 对每个Span以采样方式计时，并记录相关元数据（如计算内容、输入特征）； - - 同步采样底层硬件计数器（如NCCL通信量、内存访存数据）； - - 结合性能建模，评估各Span硬件吞吐的合理性，计算算力、内存带宽、互联带宽利用率。 - #figure( - image("imgs/torch_profiling.png",width: 60%), - caption: [Torch Profiling示意图] - ) - - == 数据洞察（Timeseries） - - *数据采集*：主要支持in-memory数据采集，无需额外的存储开销； - - *数据分析*：基于内涵SQL的分析引擎，支持多种数据分析和可视化方式； - - #figure( - image("imgs/data_explore.png",width: 60%), - caption: [数据分析视图] - ) diff --git a/docs/mermaid.min.js b/docs/mermaid.min.js deleted file mode 100644 index 695de17d..00000000 --- a/docs/mermaid.min.js +++ /dev/null @@ -1,2186 +0,0 @@ -"use strict";var __esbuild_esm_mermaid=(()=>{var Pve=Object.create;var G1=Object.defineProperty;var Bve=Object.getOwnPropertyDescriptor;var Fve=Object.getOwnPropertyNames;var zve=Object.getPrototypeOf,Gve=Object.prototype.hasOwnProperty;var o=(t,e)=>G1(t,"name",{value:e,configurable:!0});var R=(t,e)=>()=>(t&&(e=t(t=0)),e);var gi=(t,e)=>()=>(e||t((e={exports:{}}).exports,e),e.exports),hr=(t,e)=>{for(var r in e)G1(t,r,{get:e[r],enumerable:!0})},Rb=(t,e,r,n)=>{if(e&&typeof e=="object"||typeof e=="function")for(let i of Fve(e))!Gve.call(t,i)&&i!==r&&G1(t,i,{get:()=>e[i],enumerable:!(n=Bve(e,i))||n.enumerable});return 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cScale7||Oe(this.primaryColor,{h:150}),this.cScale8=this.cScale8||Oe(this.primaryColor,{h:210,l:150}),this.cScale9=this.cScale9||Oe(this.primaryColor,{h:270}),this.cScale10=this.cScale10||Oe(this.primaryColor,{h:300}),this.cScale11=this.cScale11||Oe(this.primaryColor,{h:330}),this.darkMode)for(let r=0;r{this[n]=e[n]}),this.updateColors(),r.forEach(n=>{this[n]=e[n]})}},Nz=o(t=>{let e=new QC;return e.calculate(t),e},"getThemeVariables")});var ZC,Iz,Oz=R(()=>{"use strict";al();up();ZC=class{static{o(this,"Theme")}constructor(){this.background="#333",this.primaryColor="#1f2020",this.secondaryColor=Et(this.primaryColor,16),this.tertiaryColor=Oe(this.primaryColor,{h:-160}),this.primaryBorderColor=ot(this.background),this.secondaryBorderColor=yi(this.secondaryColor,this.darkMode),this.tertiaryBorderColor=yi(this.tertiaryColor,this.darkMode),this.primaryTextColor=ot(this.primaryColor),this.secondaryTextColor=ot(this.secondaryColor),this.tertiaryTextColor=ot(this.tertiaryColor),this.lineColor=ot(this.background),this.textColor=ot(this.background),this.mainBkg="#1f2020",this.secondBkg="calculated",this.mainContrastColor="lightgrey",this.darkTextColor=Et(ot("#323D47"),10),this.lineColor="calculated",this.border1="#ccc",this.border2=Ws(255,255,255,.25),this.arrowheadColor="calculated",this.fontFamily='"trebuchet ms", verdana, arial, sans-serif',this.fontSize="16px",this.labelBackground="#181818",this.textColor="#ccc",this.THEME_COLOR_LIMIT=12,this.nodeBkg="calculated",this.nodeBorder="calculated",this.clusterBkg="calculated",this.clusterBorder="calculated",this.defaultLinkColor="calculated",this.titleColor="#F9FFFE",this.edgeLabelBackground="calculated",this.actorBorder="calculated",this.actorBkg="calculated",this.actorTextColor="calculated",this.actorLineColor="calculated",this.signalColor="calculated",this.signalTextColor="calculated",this.labelBoxBkgColor="calculated",this.labelBoxBorderColor="calculated",this.labelTextColor="calculated",this.loopTextColor="calculated",this.noteBorderColor="calculated",this.noteBkgColor="#fff5ad",this.noteTextColor="calculated",this.activationBorderColor="calculated",this.activationBkgColor="calculated",this.sequenceNumberColor="black",this.sectionBkgColor=Dt("#EAE8D9",30),this.altSectionBkgColor="calculated",this.sectionBkgColor2="#EAE8D9",this.excludeBkgColor=Dt(this.sectionBkgColor,10),this.taskBorderColor=Ws(255,255,255,70),this.taskBkgColor="calculated",this.taskTextColor="calculated",this.taskTextLightColor="calculated",this.taskTextOutsideColor="calculated",this.taskTextClickableColor="#003163",this.activeTaskBorderColor=Ws(255,255,255,50),this.activeTaskBkgColor="#81B1DB",this.gridColor="calculated",this.doneTaskBkgColor="calculated",this.doneTaskBorderColor="grey",this.critBorderColor="#E83737",this.critBkgColor="#E83737",this.taskTextDarkColor="calculated",this.todayLineColor="#DB5757",this.personBorder=this.primaryBorderColor,this.personBkg=this.mainBkg,this.archEdgeColor="calculated",this.archEdgeArrowColor="calculated",this.archEdgeWidth="3",this.archGroupBorderColor=this.primaryBorderColor,this.archGroupBorderWidth="2px",this.labelColor="calculated",this.errorBkgColor="#a44141",this.errorTextColor="#ddd"}updateColors(){this.secondBkg=Et(this.mainBkg,16),this.lineColor=this.mainContrastColor,this.arrowheadColor=this.mainContrastColor,this.nodeBkg=this.mainBkg,this.nodeBorder=this.border1,this.clusterBkg=this.secondBkg,this.clusterBorder=this.border2,this.defaultLinkColor=this.lineColor,this.edgeLabelBackground=Et(this.labelBackground,25),this.actorBorder=this.border1,this.actorBkg=this.mainBkg,this.actorTextColor=this.mainContrastColor,this.actorLineColor=this.actorBorder,this.signalColor=this.mainContrastColor,this.signalTextColor=this.mainContrastColor,this.labelBoxBkgColor=this.actorBkg,this.labelBoxBorderColor=this.actorBorder,this.labelTextColor=this.mainContrastColor,this.loopTextColor=this.mainContrastColor,this.noteBorderColor=this.secondaryBorderColor,this.noteBkgColor=this.secondBkg,this.noteTextColor=this.secondaryTextColor,this.activationBorderColor=this.border1,this.activationBkgColor=this.secondBkg,this.altSectionBkgColor=this.background,this.taskBkgColor=Et(this.mainBkg,23),this.taskTextColor=this.darkTextColor,this.taskTextLightColor=this.mainContrastColor,this.taskTextOutsideColor=this.taskTextLightColor,this.gridColor=this.mainContrastColor,this.doneTaskBkgColor=this.mainContrastColor,this.taskTextDarkColor=this.darkTextColor,this.archEdgeColor=this.lineColor,this.archEdgeArrowColor=this.lineColor,this.transitionColor=this.transitionColor||this.lineColor,this.transitionLabelColor=this.transitionLabelColor||this.textColor,this.stateLabelColor=this.stateLabelColor||this.stateBkg||this.primaryTextColor,this.stateBkg=this.stateBkg||this.mainBkg,this.labelBackgroundColor=this.labelBackgroundColor||this.stateBkg,this.compositeBackground=this.compositeBackground||this.background||this.tertiaryColor,this.altBackground=this.altBackground||"#555",this.compositeTitleBackground=this.compositeTitleBackground||this.mainBkg,this.compositeBorder=this.compositeBorder||this.nodeBorder,this.innerEndBackground=this.primaryBorderColor,this.specialStateColor="#f4f4f4",this.errorBkgColor=this.errorBkgColor||this.tertiaryColor,this.errorTextColor=this.errorTextColor||this.tertiaryTextColor,this.fillType0=this.primaryColor,this.fillType1=this.secondaryColor,this.fillType2=Oe(this.primaryColor,{h:64}),this.fillType3=Oe(this.secondaryColor,{h:64}),this.fillType4=Oe(this.primaryColor,{h:-64}),this.fillType5=Oe(this.secondaryColor,{h:-64}),this.fillType6=Oe(this.primaryColor,{h:128}),this.fillType7=Oe(this.secondaryColor,{h:128}),this.cScale1=this.cScale1||"#0b0000",this.cScale2=this.cScale2||"#4d1037",this.cScale3=this.cScale3||"#3f5258",this.cScale4=this.cScale4||"#4f2f1b",this.cScale5=this.cScale5||"#6e0a0a",this.cScale6=this.cScale6||"#3b0048",this.cScale7=this.cScale7||"#995a01",this.cScale8=this.cScale8||"#154706",this.cScale9=this.cScale9||"#161722",this.cScale10=this.cScale10||"#00296f",this.cScale11=this.cScale11||"#01629c",this.cScale12=this.cScale12||"#010029",this.cScale0=this.cScale0||this.primaryColor,this.cScale1=this.cScale1||this.secondaryColor,this.cScale2=this.cScale2||this.tertiaryColor,this.cScale3=this.cScale3||Oe(this.primaryColor,{h:30}),this.cScale4=this.cScale4||Oe(this.primaryColor,{h:60}),this.cScale5=this.cScale5||Oe(this.primaryColor,{h:90}),this.cScale6=this.cScale6||Oe(this.primaryColor,{h:120}),this.cScale7=this.cScale7||Oe(this.primaryColor,{h:150}),this.cScale8=this.cScale8||Oe(this.primaryColor,{h:210}),this.cScale9=this.cScale9||Oe(this.primaryColor,{h:270}),this.cScale10=this.cScale10||Oe(this.primaryColor,{h:300}),this.cScale11=this.cScale11||Oe(this.primaryColor,{h:330});for(let e=0;e{this[n]=e[n]}),this.updateColors(),r.forEach(n=>{this[n]=e[n]})}},Iz=o(t=>{let e=new ZC;return e.calculate(t),e},"getThemeVariables")});var JC,hp,jb=R(()=>{"use strict";al();up();j1();JC=class{static{o(this,"Theme")}constructor(){this.background="#f4f4f4",this.primaryColor="#ECECFF",this.secondaryColor=Oe(this.primaryColor,{h:120}),this.secondaryColor="#ffffde",this.tertiaryColor=Oe(this.primaryColor,{h:-160}),this.primaryBorderColor=yi(this.primaryColor,this.darkMode),this.secondaryBorderColor=yi(this.secondaryColor,this.darkMode),this.tertiaryBorderColor=yi(this.tertiaryColor,this.darkMode),this.primaryTextColor=ot(this.primaryColor),this.secondaryTextColor=ot(this.secondaryColor),this.tertiaryTextColor=ot(this.tertiaryColor),this.lineColor=ot(this.background),this.textColor=ot(this.background),this.background="white",this.mainBkg="#ECECFF",this.secondBkg="#ffffde",this.lineColor="#333333",this.border1="#9370DB",this.border2="#aaaa33",this.arrowheadColor="#333333",this.fontFamily='"trebuchet ms", verdana, arial, sans-serif',this.fontSize="16px",this.labelBackground="rgba(232,232,232, 0.8)",this.textColor="#333",this.THEME_COLOR_LIMIT=12,this.nodeBkg="calculated",this.nodeBorder="calculated",this.clusterBkg="calculated",this.clusterBorder="calculated",this.defaultLinkColor="calculated",this.titleColor="calculated",this.edgeLabelBackground="calculated",this.actorBorder="calculated",this.actorBkg="calculated",this.actorTextColor="black",this.actorLineColor="calculated",this.signalColor="calculated",this.signalTextColor="calculated",this.labelBoxBkgColor="calculated",this.labelBoxBorderColor="calculated",this.labelTextColor="calculated",this.loopTextColor="calculated",this.noteBorderColor="calculated",this.noteBkgColor="#fff5ad",this.noteTextColor="calculated",this.activationBorderColor="#666",this.activationBkgColor="#f4f4f4",this.sequenceNumberColor="white",this.sectionBkgColor="calculated",this.altSectionBkgColor="calculated",this.sectionBkgColor2="calculated",this.excludeBkgColor="#eeeeee",this.taskBorderColor="calculated",this.taskBkgColor="calculated",this.taskTextLightColor="calculated",this.taskTextColor=this.taskTextLightColor,this.taskTextDarkColor="calculated",this.taskTextOutsideColor=this.taskTextDarkColor,this.taskTextClickableColor="calculated",this.activeTaskBorderColor="calculated",this.activeTaskBkgColor="calculated",this.gridColor="calculated",this.doneTaskBkgColor="calculated",this.doneTaskBorderColor="calculated",this.critBorderColor="calculated",this.critBkgColor="calculated",this.todayLineColor="calculated",this.sectionBkgColor=Ws(102,102,255,.49),this.altSectionBkgColor="white",this.sectionBkgColor2="#fff400",this.taskBorderColor="#534fbc",this.taskBkgColor="#8a90dd",this.taskTextLightColor="white",this.taskTextColor="calculated",this.taskTextDarkColor="black",this.taskTextOutsideColor="calculated",this.taskTextClickableColor="#003163",this.activeTaskBorderColor="#534fbc",this.activeTaskBkgColor="#bfc7ff",this.gridColor="lightgrey",this.doneTaskBkgColor="lightgrey",this.doneTaskBorderColor="grey",this.critBorderColor="#ff8888",this.critBkgColor="red",this.todayLineColor="red",this.personBorder=this.primaryBorderColor,this.personBkg=this.mainBkg,this.archEdgeColor="calculated",this.archEdgeArrowColor="calculated",this.archEdgeWidth="3",this.archGroupBorderColor=this.primaryBorderColor,this.archGroupBorderWidth="2px",this.labelColor="black",this.errorBkgColor="#552222",this.errorTextColor="#552222",this.updateColors()}updateColors(){this.cScale0=this.cScale0||this.primaryColor,this.cScale1=this.cScale1||this.secondaryColor,this.cScale2=this.cScale2||this.tertiaryColor,this.cScale3=this.cScale3||Oe(this.primaryColor,{h:30}),this.cScale4=this.cScale4||Oe(this.primaryColor,{h:60}),this.cScale5=this.cScale5||Oe(this.primaryColor,{h:90}),this.cScale6=this.cScale6||Oe(this.primaryColor,{h:120}),this.cScale7=this.cScale7||Oe(this.primaryColor,{h:150}),this.cScale8=this.cScale8||Oe(this.primaryColor,{h:210}),this.cScale9=this.cScale9||Oe(this.primaryColor,{h:270}),this.cScale10=this.cScale10||Oe(this.primaryColor,{h:300}),this.cScale11=this.cScale11||Oe(this.primaryColor,{h:330}),this.cScalePeer1=this.cScalePeer1||Dt(this.secondaryColor,45),this.cScalePeer2=this.cScalePeer2||Dt(this.tertiaryColor,40);for(let e=0;e{this[n]=e[n]}),this.updateColors(),r.forEach(n=>{this[n]=e[n]})}},hp=o(t=>{let e=new JC;return e.calculate(t),e},"getThemeVariables")});var e7,Pz,Bz=R(()=>{"use strict";al();j1();up();e7=class{static{o(this,"Theme")}constructor(){this.background="#f4f4f4",this.primaryColor="#cde498",this.secondaryColor="#cdffb2",this.background="white",this.mainBkg="#cde498",this.secondBkg="#cdffb2",this.lineColor="green",this.border1="#13540c",this.border2="#6eaa49",this.arrowheadColor="green",this.fontFamily='"trebuchet ms", verdana, arial, sans-serif',this.fontSize="16px",this.tertiaryColor=Et("#cde498",10),this.primaryBorderColor=yi(this.primaryColor,this.darkMode),this.secondaryBorderColor=yi(this.secondaryColor,this.darkMode),this.tertiaryBorderColor=yi(this.tertiaryColor,this.darkMode),this.primaryTextColor=ot(this.primaryColor),this.secondaryTextColor=ot(this.secondaryColor),this.tertiaryTextColor=ot(this.primaryColor),this.lineColor=ot(this.background),this.textColor=ot(this.background),this.THEME_COLOR_LIMIT=12,this.nodeBkg="calculated",this.nodeBorder="calculated",this.clusterBkg="calculated",this.clusterBorder="calculated",this.defaultLinkColor="calculated",this.titleColor="#333",this.edgeLabelBackground="#e8e8e8",this.actorBorder="calculated",this.actorBkg="calculated",this.actorTextColor="black",this.actorLineColor="calculated",this.signalColor="#333",this.signalTextColor="#333",this.labelBoxBkgColor="calculated",this.labelBoxBorderColor="#326932",this.labelTextColor="calculated",this.loopTextColor="calculated",this.noteBorderColor="calculated",this.noteBkgColor="#fff5ad",this.noteTextColor="calculated",this.activationBorderColor="#666",this.activationBkgColor="#f4f4f4",this.sequenceNumberColor="white",this.sectionBkgColor="#6eaa49",this.altSectionBkgColor="white",this.sectionBkgColor2="#6eaa49",this.excludeBkgColor="#eeeeee",this.taskBorderColor="calculated",this.taskBkgColor="#487e3a",this.taskTextLightColor="white",this.taskTextColor="calculated",this.taskTextDarkColor="black",this.taskTextOutsideColor="calculated",this.taskTextClickableColor="#003163",this.activeTaskBorderColor="calculated",this.activeTaskBkgColor="calculated",this.gridColor="lightgrey",this.doneTaskBkgColor="lightgrey",this.doneTaskBorderColor="grey",this.critBorderColor="#ff8888",this.critBkgColor="red",this.todayLineColor="red",this.personBorder=this.primaryBorderColor,this.personBkg=this.mainBkg,this.archEdgeColor="calculated",this.archEdgeArrowColor="calculated",this.archEdgeWidth="3",this.archGroupBorderColor=this.primaryBorderColor,this.archGroupBorderWidth="2px",this.labelColor="black",this.errorBkgColor="#552222",this.errorTextColor="#552222"}updateColors(){this.actorBorder=Dt(this.mainBkg,20),this.actorBkg=this.mainBkg,this.labelBoxBkgColor=this.actorBkg,this.labelTextColor=this.actorTextColor,this.loopTextColor=this.actorTextColor,this.noteBorderColor=this.border2,this.noteTextColor=this.actorTextColor,this.actorLineColor=this.actorBorder,this.cScale0=this.cScale0||this.primaryColor,this.cScale1=this.cScale1||this.secondaryColor,this.cScale2=this.cScale2||this.tertiaryColor,this.cScale3=this.cScale3||Oe(this.primaryColor,{h:30}),this.cScale4=this.cScale4||Oe(this.primaryColor,{h:60}),this.cScale5=this.cScale5||Oe(this.primaryColor,{h:90}),this.cScale6=this.cScale6||Oe(this.primaryColor,{h:120}),this.cScale7=this.cScale7||Oe(this.primaryColor,{h:150}),this.cScale8=this.cScale8||Oe(this.primaryColor,{h:210}),this.cScale9=this.cScale9||Oe(this.primaryColor,{h:270}),this.cScale10=this.cScale10||Oe(this.primaryColor,{h:300}),this.cScale11=this.cScale11||Oe(this.primaryColor,{h:330}),this.cScalePeer1=this.cScalePeer1||Dt(this.secondaryColor,45),this.cScalePeer2=this.cScalePeer2||Dt(this.tertiaryColor,40);for(let e=0;e{this[n]=e[n]}),this.updateColors(),r.forEach(n=>{this[n]=e[n]})}},Pz=o(t=>{let e=new e7;return e.calculate(t),e},"getThemeVariables")});var t7,Fz,zz=R(()=>{"use strict";al();up();j1();t7=class{static{o(this,"Theme")}constructor(){this.primaryColor="#eee",this.contrast="#707070",this.secondaryColor=Et(this.contrast,55),this.background="#ffffff",this.tertiaryColor=Oe(this.primaryColor,{h:-160}),this.primaryBorderColor=yi(this.primaryColor,this.darkMode),this.secondaryBorderColor=yi(this.secondaryColor,this.darkMode),this.tertiaryBorderColor=yi(this.tertiaryColor,this.darkMode),this.primaryTextColor=ot(this.primaryColor),this.secondaryTextColor=ot(this.secondaryColor),this.tertiaryTextColor=ot(this.tertiaryColor),this.lineColor=ot(this.background),this.textColor=ot(this.background),this.mainBkg="#eee",this.secondBkg="calculated",this.lineColor="#666",this.border1="#999",this.border2="calculated",this.note="#ffa",this.text="#333",this.critical="#d42",this.done="#bbb",this.arrowheadColor="#333333",this.fontFamily='"trebuchet ms", verdana, arial, sans-serif',this.fontSize="16px",this.THEME_COLOR_LIMIT=12,this.nodeBkg="calculated",this.nodeBorder="calculated",this.clusterBkg="calculated",this.clusterBorder="calculated",this.defaultLinkColor="calculated",this.titleColor="calculated",this.edgeLabelBackground="white",this.actorBorder="calculated",this.actorBkg="calculated",this.actorTextColor="calculated",this.actorLineColor=this.actorBorder,this.signalColor="calculated",this.signalTextColor="calculated",this.labelBoxBkgColor="calculated",this.labelBoxBorderColor="calculated",this.labelTextColor="calculated",this.loopTextColor="calculated",this.noteBorderColor="calculated",this.noteBkgColor="calculated",this.noteTextColor="calculated",this.activationBorderColor="#666",this.activationBkgColor="#f4f4f4",this.sequenceNumberColor="white",this.sectionBkgColor="calculated",this.altSectionBkgColor="white",this.sectionBkgColor2="calculated",this.excludeBkgColor="#eeeeee",this.taskBorderColor="calculated",this.taskBkgColor="calculated",this.taskTextLightColor="white",this.taskTextColor="calculated",this.taskTextDarkColor="calculated",this.taskTextOutsideColor="calculated",this.taskTextClickableColor="#003163",this.activeTaskBorderColor="calculated",this.activeTaskBkgColor="calculated",this.gridColor="calculated",this.doneTaskBkgColor="calculated",this.doneTaskBorderColor="calculated",this.critBkgColor="calculated",this.critBorderColor="calculated",this.todayLineColor="calculated",this.personBorder=this.primaryBorderColor,this.personBkg=this.mainBkg,this.archEdgeColor="calculated",this.archEdgeArrowColor="calculated",this.archEdgeWidth="3",this.archGroupBorderColor=this.primaryBorderColor,this.archGroupBorderWidth="2px",this.labelColor="black",this.errorBkgColor="#552222",this.errorTextColor="#552222"}updateColors(){this.secondBkg=Et(this.contrast,55),this.border2=this.contrast,this.actorBorder=Et(this.border1,23),this.actorBkg=this.mainBkg,this.actorTextColor=this.text,this.actorLineColor=this.actorBorder,this.signalColor=this.text,this.signalTextColor=this.text,this.labelBoxBkgColor=this.actorBkg,this.labelBoxBorderColor=this.actorBorder,this.labelTextColor=this.text,this.loopTextColor=this.text,this.noteBorderColor="#999",this.noteBkgColor="#666",this.noteTextColor="#fff",this.cScale0=this.cScale0||"#555",this.cScale1=this.cScale1||"#F4F4F4",this.cScale2=this.cScale2||"#555",this.cScale3=this.cScale3||"#BBB",this.cScale4=this.cScale4||"#777",this.cScale5=this.cScale5||"#999",this.cScale6=this.cScale6||"#DDD",this.cScale7=this.cScale7||"#FFF",this.cScale8=this.cScale8||"#DDD",this.cScale9=this.cScale9||"#BBB",this.cScale10=this.cScale10||"#999",this.cScale11=this.cScale11||"#777";for(let 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r=="function"||!r.unexpandable:fh.hasOwnProperty(e)&&!fh[e].primitive}},SG=/^[₊₋₌₍₎₀₁₂₃₄₅₆₇₈₉ₐₑₕᵢⱼₖₗₘₙₒₚᵣₛₜᵤᵥₓᵦᵧᵨᵩᵪ]/,c4=Object.freeze({"\u208A":"+","\u208B":"-","\u208C":"=","\u208D":"(","\u208E":")","\u2080":"0","\u2081":"1","\u2082":"2","\u2083":"3","\u2084":"4","\u2085":"5","\u2086":"6","\u2087":"7","\u2088":"8","\u2089":"9","\u2090":"a","\u2091":"e","\u2095":"h","\u1D62":"i","\u2C7C":"j","\u2096":"k","\u2097":"l","\u2098":"m","\u2099":"n","\u2092":"o","\u209A":"p","\u1D63":"r","\u209B":"s","\u209C":"t","\u1D64":"u","\u1D65":"v","\u2093":"x","\u1D66":"\u03B2","\u1D67":"\u03B3","\u1D68":"\u03C1","\u1D69":"\u03D5","\u1D6A":"\u03C7","\u207A":"+","\u207B":"-","\u207C":"=","\u207D":"(","\u207E":")","\u2070":"0","\xB9":"1","\xB2":"2","\xB3":"3","\u2074":"4","\u2075":"5","\u2076":"6","\u2077":"7","\u2078":"8","\u2079":"9","\u1D2C":"A","\u1D2E":"B","\u1D30":"D","\u1D31":"E","\u1D33":"G","\u1D34":"H","\u1D35":"I","\u1D36":"J","\u1D37":"K","\u1D38":"L","\u1D39":"M","\u1D3A":"N","\u1D3C":"O","\u1D3E":"P","\u1D3F":"R","\u1D40":"T","\u1D41":"U","\u2C7D":"V","\u1D42":"W","\u1D43":"a","\u1D47":"b","\u1D9C":"c","\u1D48":"d","\u1D49":"e","\u1DA0":"f","\u1D4D":"g",\u02B0:"h","\u2071":"i",\u02B2:"j","\u1D4F":"k",\u02E1:"l","\u1D50":"m",\u207F:"n","\u1D52":"o","\u1D56":"p",\u02B3:"r",\u02E2:"s","\u1D57":"t","\u1D58":"u","\u1D5B":"v",\u02B7:"w",\u02E3:"x",\u02B8:"y","\u1DBB":"z","\u1D5D":"\u03B2","\u1D5E":"\u03B3","\u1D5F":"\u03B4","\u1D60":"\u03D5","\u1D61":"\u03C7","\u1DBF":"\u03B8"}),x7={"\u0301":{text:"\\'",math:"\\acute"},"\u0300":{text:"\\`",math:"\\grave"},"\u0308":{text:'\\"',math:"\\ddot"},"\u0303":{text:"\\~",math:"\\tilde"},"\u0304":{text:"\\=",math:"\\bar"},"\u0306":{text:"\\u",math:"\\breve"},"\u030C":{text:"\\v",math:"\\check"},"\u0302":{text:"\\^",math:"\\hat"},"\u0307":{text:"\\.",math:"\\dot"},"\u030A":{text:"\\r",math:"\\mathring"},"\u030B":{text:"\\H"},"\u0327":{text:"\\c"}},AG={\u00E1:"a\u0301",\u00E0:"a\u0300",\u00E4:"a\u0308",\u01DF:"a\u0308\u0304",\u00E3:"a\u0303",\u0101:"a\u0304",\u0103:"a\u0306",\u1EAF:"a\u0306\u0301",\u1EB1:"a\u0306\u0300",\u1EB5:"a\u0306\u0303",\u01CE:"a\u030C",\u00E2:"a\u0302",\u1EA5:"a\u0302\u0301",\u1EA7:"a\u0302\u0300",\u1EAB:"a\u0302\u0303",\u0227:"a\u0307",\u01E1:"a\u0307\u0304",\u00E5:"a\u030A",\u01FB:"a\u030A\u0301",\u1E03:"b\u0307",\u0107:"c\u0301",\u1E09:"c\u0327\u0301",\u010D:"c\u030C",\u0109:"c\u0302",\u010B:"c\u0307",\u00E7:"c\u0327",\u010F:"d\u030C",\u1E0B:"d\u0307",\u1E11:"d\u0327",\u00E9:"e\u0301",\u00E8:"e\u0300",\u00EB:"e\u0308",\u1EBD:"e\u0303",\u0113:"e\u0304",\u1E17:"e\u0304\u0301",\u1E15:"e\u0304\u0300",\u0115:"e\u0306",\u1E1D:"e\u0327\u0306",\u011B:"e\u030C",\u00EA:"e\u0302",\u1EBF:"e\u0302\u0301",\u1EC1:"e\u0302\u0300",\u1EC5:"e\u0302\u0303",\u0117:"e\u0307",\u0229:"e\u0327",\u1E1F:"f\u0307",\u01F5:"g\u0301",\u1E21:"g\u0304",\u011F:"g\u0306",\u01E7:"g\u030C",\u011D:"g\u0302",\u0121:"g\u0307",\u0123:"g\u0327",\u1E27:"h\u0308",\u021F:"h\u030C",\u0125:"h\u0302",\u1E23:"h\u0307",\u1E29:"h\u0327",\u00ED:"i\u0301",\u00EC:"i\u0300",\u00EF:"i\u0308",\u1E2F:"i\u0308\u0301",\u0129:"i\u0303",\u012B:"i\u0304",\u012D:"i\u0306",\u01D0:"i\u030C",\u00EE:"i\u0302",\u01F0:"j\u030C",\u0135:"j\u0302",\u1E31:"k\u0301",\u01E9:"k\u030C",\u0137:"k\u0327",\u013A:"l\u0301",\u013E:"l\u030C",\u013C:"l\u0327",\u1E3F:"m\u0301",\u1E41:"m\u0307",\u0144:"n\u0301",\u01F9:"n\u0300",\u00F1:"n\u0303",\u0148:"n\u030C",\u1E45:"n\u0307",\u0146:"n\u0327",\u00F3:"o\u0301",\u00F2:"o\u0300",\u00F6:"o\u0308",\u022B:"o\u0308\u0304",\u00F5:"o\u0303",\u1E4D:"o\u0303\u0301",\u1E4F:"o\u0303\u0308",\u022D:"o\u0303\u0304",\u014D:"o\u0304",\u1E53:"o\u0304\u0301",\u1E51:"o\u0304\u0300",\u014F:"o\u0306",\u01D2:"o\u030C",\u00F4:"o\u0302",\u1ED1:"o\u0302\u0301",\u1ED3:"o\u0302\u0300",\u1ED7:"o\u0302\u0303",\u022F:"o\u0307",\u0231:"o\u0307\u0304",\u0151:"o\u030B",\u1E55:"p\u0301",\u1E57:"p\u0307",\u0155:"r\u0301",\u0159:"r\u030C",\u1E59:"r\u0307",\u0157:"r\u0327",\u015B:"s\u0301",\u1E65:"s\u0301\u0307",\u0161:"s\u030C",\u1E67:"s\u030C\u0307",\u015D:"s\u0302",\u1E61:"s\u0307",\u015F:"s\u0327",\u1E97:"t\u0308",\u0165:"t\u030C",\u1E6B:"t\u0307",\u0163:"t\u0327",\u00FA:"u\u0301",\u00F9:"u\u0300",\u00FC:"u\u0308",\u01D8:"u\u0308\u0301",\u01DC:"u\u0308\u0300",\u01D6:"u\u0308\u0304",\u01DA:"u\u0308\u030C",\u0169:"u\u0303",\u1E79:"u\u0303\u0301",\u016B:"u\u0304",\u1E7B:"u\u0304\u0308",\u016D:"u\u0306",\u01D4:"u\u030C",\u00FB:"u\u0302",\u016F:"u\u030A",\u0171:"u\u030B",\u1E7D:"v\u0303",\u1E83:"w\u0301",\u1E81:"w\u0300",\u1E85:"w\u0308",\u0175:"w\u0302",\u1E87:"w\u0307",\u1E98:"w\u030A",\u1E8D:"x\u0308",\u1E8B:"x\u0307",\u00FD:"y\u0301",\u1EF3:"y\u0300",\u00FF:"y\u0308",\u1EF9:"y\u0303",\u0233:"y\u0304",\u0177:"y\u0302",\u1E8F:"y\u0307",\u1E99:"y\u030A",\u017A:"z\u0301",\u017E:"z\u030C",\u1E91:"z\u0302",\u017C:"z\u0307",\u00C1:"A\u0301",\u00C0:"A\u0300",\u00C4:"A\u0308",\u01DE:"A\u0308\u0304",\u00C3:"A\u0303",\u0100:"A\u0304",\u0102:"A\u0306",\u1EAE:"A\u0306\u0301",\u1EB0:"A\u0306\u0300",\u1EB4:"A\u0306\u0303",\u01CD:"A\u03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this.expect("}"),this.nextToken=r,n}parseExpression(e,r){for(var n=[];;){this.mode==="math"&&this.consumeSpaces();var i=this.fetch();if(t.endOfExpression.indexOf(i.text)!==-1||r&&i.text===r||e&&fh[i.text]&&fh[i.text].infix)break;var a=this.parseAtom(r);if(a){if(a.type==="internal")continue}else break;n.push(a)}return this.mode==="text"&&this.formLigatures(n),this.handleInfixNodes(n)}handleInfixNodes(e){for(var r=-1,n,i=0;i=0&&this.settings.reportNonstrict("unicodeTextInMathMode",'Latin-1/Unicode text character "'+r[0]+'" used in math mode',e);var l=wn[this.mode][r].group,u=Xs.range(e),h;if(hxe.hasOwnProperty(l)){var f=l;h={type:"atom",mode:this.mode,family:f,loc:u,text:r}}else h={type:l,mode:this.mode,loc:u,text:r};s=h}else if(r.charCodeAt(0)>=128)this.settings.strict&&(LG(r.charCodeAt(0))?this.mode==="math"&&this.settings.reportNonstrict("unicodeTextInMathMode",'Unicode text character "'+r[0]+'" used in math mode',e):this.settings.reportNonstrict("unknownSymbol",'Unrecognized Unicode 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2:if(Hs=this.productions_[us[1]][1],sh.$=Ot[Ot.length-Hs],sh._$={first_line:pe[pe.length-(Hs||1)].first_line,last_line:pe[pe.length-1].last_line,first_column:pe[pe.length-(Hs||1)].first_column,last_column:pe[pe.length-1].last_column},ah&&(sh._$.range=[pe[pe.length-(Hs||1)].range[0],pe[pe.length-1].range[1]]),Wl=this.performAction.apply(sh,[be,Xc,Ir,ko.yy,us[1],Ot,pe].concat(O1)),typeof Wl<"u")return Wl;Hs&&($t=$t.slice(0,-1*Hs*2),Ot=Ot.slice(0,-1*Hs),pe=pe.slice(0,-1*Hs)),$t.push(this.productions_[us[1]][0]),Ot.push(sh.$),pe.push(sh._$),B1=ur[$t[$t.length-2]][$t[$t.length-1]],$t.push(B1);break;case 3:return!0}}return!0},"parse")},_a=function(){var qi={EOF:1,parseError:o(function(At,$t){if(this.yy.parser)this.yy.parser.parseError(At,$t);else throw new Error(At)},"parseError"),setInput:o(function(ht,At){return this.yy=At||this.yy||{},this._input=ht,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},"setInput"),input:o(function(){var ht=this._input[0];this.yytext+=ht,this.yyleng++,this.offset++,this.match+=ht,this.matched+=ht;var At=ht.match(/(?:\r\n?|\n).*/g);return At?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),ht},"input"),unput:o(function(ht){var At=ht.length,$t=ht.split(/(?:\r\n?|\n)/g);this._input=ht+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-At),this.offset-=At;var rt=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),$t.length-1&&(this.yylineno-=$t.length-1);var Ot=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:$t?($t.length===rt.length?this.yylloc.first_column:0)+rt[rt.length-$t.length].length-$t[0].length:this.yylloc.first_column-At},this.options.ranges&&(this.yylloc.range=[Ot[0],Ot[0]+this.yyleng-At]),this.yyleng=this.yytext.length,this},"unput"),more:o(function(){return this._more=!0,this},"more"),reject:o(function(){if(this.options.backtrack_lexer)this._backtrack=!0;else return this.parseError("Lexical error on line "+(this.yylineno+1)+`. You can only invoke reject() in the lexer when the lexer is of the backtracking persuasion (options.backtrack_lexer = true). -`+this.showPosition(),{text:"",token:null,line:this.yylineno});return this},"reject"),less:o(function(ht){this.unput(this.match.slice(ht))},"less"),pastInput:o(function(){var ht=this.matched.substr(0,this.matched.length-this.match.length);return(ht.length>20?"...":"")+ht.substr(-20).replace(/\n/g,"")},"pastInput"),upcomingInput:o(function(){var ht=this.match;return ht.length<20&&(ht+=this._input.substr(0,20-ht.length)),(ht.substr(0,20)+(ht.length>20?"...":"")).replace(/\n/g,"")},"upcomingInput"),showPosition:o(function(){var ht=this.pastInput(),At=new Array(ht.length+1).join("-");return ht+this.upcomingInput()+` -`+At+"^"},"showPosition"),test_match:o(function(ht,At){var $t,rt,Ot;if(this.options.backtrack_lexer&&(Ot={yylineno:this.yylineno,yylloc:{first_line:this.yylloc.first_line,last_line:this.last_line,first_column:this.yylloc.first_column,last_column:this.yylloc.last_column},yytext:this.yytext,match:this.match,matches:this.matches,matched:this.matched,yyleng:this.yyleng,offset:this.offset,_more:this._more,_input:this._input,yy:this.yy,conditionStack:this.conditionStack.slice(0),done:this.done},this.options.ranges&&(Ot.yylloc.range=this.yylloc.range.slice(0))),rt=ht[0].match(/(?:\r\n?|\n).*/g),rt&&(this.yylineno+=rt.length),this.yylloc={first_line:this.yylloc.last_line,last_line:this.yylineno+1,first_column:this.yylloc.last_column,last_column:rt?rt[rt.length-1].length-rt[rt.length-1].match(/\r?\n?/)[0].length:this.yylloc.last_column+ht[0].length},this.yytext+=ht[0],this.match+=ht[0],this.matches=ht,this.yyleng=this.yytext.length,this.options.ranges&&(this.yylloc.range=[this.offset,this.offset+=this.yyleng]),this._more=!1,this._backtrack=!1,this._input=this._input.slice(ht[0].length),this.matched+=ht[0],$t=this.performAction.call(this,this.yy,this,At,this.conditionStack[this.conditionStack.length-1]),this.done&&this._input&&(this.done=!1),$t)return $t;if(this._backtrack){for(var pe in Ot)this[pe]=Ot[pe];return!1}return!1},"test_match"),next:o(function(){if(this.done)return this.EOF;this._input||(this.done=!0);var ht,At,$t,rt;this._more||(this.yytext="",this.match="");for(var Ot=this._currentRules(),pe=0;peAt[0].length)){if(At=$t,rt=pe,this.options.backtrack_lexer){if(ht=this.test_match($t,Ot[pe]),ht!==!1)return ht;if(this._backtrack){At=!1;continue}else return!1}else if(!this.options.flex)break}return At?(ht=this.test_match(At,Ot[rt]),ht!==!1?ht:!1):this._input===""?this.EOF:this.parseError("Lexical error on line "+(this.yylineno+1)+`. Unrecognized text. -`+this.showPosition(),{text:"",token:null,line:this.yylineno})},"next"),lex:o(function(){var At=this.next();return At||this.lex()},"lex"),begin:o(function(At){this.conditionStack.push(At)},"begin"),popState:o(function(){var At=this.conditionStack.length-1;return At>0?this.conditionStack.pop():this.conditionStack[0]},"popState"),_currentRules:o(function(){return this.conditionStack.length&&this.conditionStack[this.conditionStack.length-1]?this.conditions[this.conditionStack[this.conditionStack.length-1]].rules:this.conditions.INITIAL.rules},"_currentRules"),topState:o(function(At){return At=this.conditionStack.length-1-Math.abs(At||0),At>=0?this.conditionStack[At]:"INITIAL"},"topState"),pushState:o(function(At){this.begin(At)},"pushState"),stateStackSize:o(function(){return this.conditionStack.length},"stateStackSize"),options:{},performAction:o(function(At,$t,rt,Ot){var pe=Ot;switch(rt){case 0:return this.begin("acc_title"),34;break;case 1:return this.popState(),"acc_title_value";break;case 2:return this.begin("acc_descr"),36;break;case 3:return this.popState(),"acc_descr_value";break;case 4:this.begin("acc_descr_multiline");break;case 5:this.popState();break;case 6:return"acc_descr_multiline_value";case 7:this.begin("callbackname");break;case 8:this.popState();break;case 9:this.popState(),this.begin("callbackargs");break;case 10:return 92;case 11:this.popState();break;case 12:return 93;case 13:return"MD_STR";case 14:this.popState();break;case 15:this.begin("md_string");break;case 16:return"STR";case 17:this.popState();break;case 18:this.pushState("string");break;case 19:return 81;case 20:return 99;case 21:return 82;case 22:return 101;case 23:return 83;case 24:return 84;case 25:return 94;case 26:this.begin("click");break;case 27:this.popState();break;case 28:return 85;case 29:return At.lex.firstGraph()&&this.begin("dir"),12;break;case 30:return At.lex.firstGraph()&&this.begin("dir"),12;break;case 31:return At.lex.firstGraph()&&this.begin("dir"),12;break;case 32:return 27;case 33:return 32;case 34:return 95;case 35:return 95;case 36:return 95;case 37:return 95;case 38:return this.popState(),13;break;case 39:return this.popState(),14;break;case 40:return this.popState(),14;break;case 41:return this.popState(),14;break;case 42:return this.popState(),14;break;case 43:return this.popState(),14;break;case 44:return this.popState(),14;break;case 45:return this.popState(),14;break;case 46:return this.popState(),14;break;case 47:return this.popState(),14;break;case 48:return this.popState(),14;break;case 49:return 118;case 50:return 119;case 51:return 120;case 52:return 121;case 53:return 102;case 54:return 108;case 55:return 44;case 56:return 58;case 57:return 42;case 58:return 8;case 59:return 103;case 60:return 112;case 61:return this.popState(),75;break;case 62:return this.pushState("edgeText"),73;break;case 63:return 116;case 64:return this.popState(),75;break;case 65:return this.pushState("thickEdgeText"),73;break;case 66:return 116;case 67:return this.popState(),75;break;case 68:return this.pushState("dottedEdgeText"),73;break;case 69:return 116;case 70:return 75;case 71:return this.popState(),51;break;case 72:return"TEXT";case 73:return this.pushState("ellipseText"),50;break;case 74:return this.popState(),53;break;case 75:return this.pushState("text"),52;break;case 76:return this.popState(),55;break;case 77:return this.pushState("text"),54;break;case 78:return 56;case 79:return this.pushState("text"),65;break;case 80:return this.popState(),62;break;case 81:return this.pushState("text"),61;break;case 82:return this.popState(),47;break;case 83:return this.pushState("text"),46;break;case 84:return this.popState(),67;break;case 85:return this.popState(),69;break;case 86:return 114;case 87:return this.pushState("trapText"),66;break;case 88:return this.pushState("trapText"),68;break;case 89:return 115;case 90:return 65;case 91:return 87;case 92:return"SEP";case 93:return 86;case 94:return 112;case 95:return 108;case 96:return 42;case 97:return 106;case 98:return 111;case 99:return 113;case 100:return this.popState(),60;break;case 101:return this.pushState("text"),60;break;case 102:return this.popState(),49;break;case 103:return this.pushState("text"),48;break;case 104:return this.popState(),31;break;case 105:return this.pushState("text"),29;break;case 106:return this.popState(),64;break;case 107:return this.pushState("text"),63;break;case 108:return"TEXT";case 109:return"QUOTE";case 110:return 9;case 111:return 10;case 112:return 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24:this.$={attributeType:C[D-2],attributeName:C[D-1],attributeKeyTypeList:C[D]};break;case 25:this.$={attributeType:C[D-2],attributeName:C[D-1],attributeComment:C[D]};break;case 26:this.$={attributeType:C[D-3],attributeName:C[D-2],attributeKeyTypeList:C[D-1],attributeComment:C[D]};break;case 27:case 28:case 31:this.$=C[D];break;case 30:C[D-2].push(C[D]),this.$=C[D-2];break;case 32:this.$=C[D].replace(/"/g,"");break;case 33:this.$={cardA:C[D],relType:C[D-1],cardB:C[D-2]};break;case 34:this.$=k.Cardinality.ZERO_OR_ONE;break;case 35:this.$=k.Cardinality.ZERO_OR_MORE;break;case 36:this.$=k.Cardinality.ONE_OR_MORE;break;case 37:this.$=k.Cardinality.ONLY_ONE;break;case 38:this.$=k.Cardinality.MD_PARENT;break;case 39:this.$=k.Identification.NON_IDENTIFYING;break;case 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N=new Error(L);throw N.hash=M,N}},"parseError"),parse:o(function(L){var M=this,N=[0],k=[],I=[null],C=[],O=this.table,D="",P=0,F=0,B=0,$=2,z=1,Y=C.slice.call(arguments,1),Q=Object.create(this.lexer),X={yy:{}};for(var ie in this.yy)Object.prototype.hasOwnProperty.call(this.yy,ie)&&(X.yy[ie]=this.yy[ie]);Q.setInput(L,X.yy),X.yy.lexer=Q,X.yy.parser=this,typeof Q.yylloc>"u"&&(Q.yylloc={});var j=Q.yylloc;C.push(j);var J=Q.options&&Q.options.ranges;typeof X.yy.parseError=="function"?this.parseError=X.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;function Z(Pe){N.length=N.length-2*Pe,I.length=I.length-Pe,C.length=C.length-Pe}o(Z,"popStack");function H(){var Pe;return Pe=k.pop()||Q.lex()||z,typeof Pe!="number"&&(Pe instanceof Array&&(k=Pe,Pe=k.pop()),Pe=M.symbols_[Pe]||Pe),Pe}o(H,"lex");for(var q,K,se,ce,ue,te,De={},oe,ke,Ie,Se;;){if(se=N[N.length-1],this.defaultActions[se]?ce=this.defaultActions[se]:((q===null||typeof q>"u")&&(q=H()),ce=O[se]&&O[se][q]),typeof 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I=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:N?(N.length===k.length?this.yylloc.first_column:0)+k[k.length-N.length].length-N[0].length:this.yylloc.first_column-M},this.options.ranges&&(this.yylloc.range=[I[0],I[0]+this.yyleng-M]),this.yyleng=this.yytext.length,this},"unput"),more:o(function(){return this._more=!0,this},"more"),reject:o(function(){if(this.options.backtrack_lexer)this._backtrack=!0;else return this.parseError("Lexical error on line "+(this.yylineno+1)+`. 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this.popState(),"acc_title_value";break;case 2:return this.begin("acc_descr"),24;break;case 3:return this.popState(),"acc_descr_value";break;case 4:this.begin("acc_descr_multiline");break;case 5:this.popState();break;case 6:return"acc_descr_multiline_value";case 7:return 10;case 8:break;case 9:return 8;case 10:return 28;case 11:return 48;case 12:return 4;case 13:return this.begin("block"),15;break;case 14:return 36;case 15:break;case 16:return 37;case 17:return 34;case 18:return 34;case 19:return 38;case 20:break;case 21:return this.popState(),17;break;case 22:return N.yytext[0];case 23:return 18;case 24:return 19;case 25:return 41;case 26:return 43;case 27:return 43;case 28:return 43;case 29:return 41;case 30:return 41;case 31:return 42;case 32:return 42;case 33:return 42;case 34:return 42;case 35:return 42;case 36:return 43;case 37:return 42;case 38:return 43;case 39:return 44;case 40:return 44;case 41:return 44;case 42:return 44;case 43:return 41;case 44:return 42;case 45:return 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n=this.config.traceInitPerf;n===!0?(this.traceInitMaxIdent=1/0,this.traceInitPerf=!0):typeof n=="number"&&(this.traceInitMaxIdent=n,this.traceInitPerf=!0),this.traceInitIndent=-1,this.TRACE_INIT("Lexer Constructor",()=>{let i,a=!0;this.TRACE_INIT("Lexer Config handling",()=>{if(this.config.lineTerminatorsPattern===a2.lineTerminatorsPattern)this.config.lineTerminatorsPattern=kie;else if(this.config.lineTerminatorCharacters===a2.lineTerminatorCharacters)throw Error(`Error: Missing property on the Lexer config. - For details See: https://chevrotain.io/docs/guide/resolving_lexer_errors.html#MISSING_LINE_TERM_CHARS`);if(r.safeMode&&r.ensureOptimizations)throw Error('"safeMode" and "ensureOptimizations" flags are mutually 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h;this.TRACE_INIT("analyzeTokenTypes",()=>{h=yie(l,{lineTerminatorCharacters:this.config.lineTerminatorCharacters,positionTracking:r.positionTracking,ensureOptimizations:r.ensureOptimizations,safeMode:r.safeMode,tracer:this.TRACE_INIT})}),this.patternIdxToConfig[u]=h.patternIdxToConfig,this.charCodeToPatternIdxToConfig[u]=h.charCodeToPatternIdxToConfig,this.emptyGroups=pa({},this.emptyGroups,h.emptyGroups),this.hasCustom=h.hasCustom||this.hasCustom,this.canModeBeOptimized[u]=h.canBeOptimized}})}),this.defaultMode=i.defaultMode,!Qt(this.lexerDefinitionErrors)&&!this.config.deferDefinitionErrorsHandling){let u=qe(this.lexerDefinitionErrors,h=>h.message).join(`----------------------- -`);throw new Error(`Errors detected in definition of Lexer: -`+u)}Ee(this.lexerDefinitionWarning,l=>{e2(l.message)}),this.TRACE_INIT("Choosing sub-methods implementations",()=>{if(fN?(this.chopInput=ea,this.match=this.matchWithTest):(this.updateLastIndex=qn,this.match=this.matchWithExec),a&&(this.handleModes=qn),this.trackStartLines===!1&&(this.computeNewColumn=ea),this.trackEndLines===!1&&(this.updateTokenEndLineColumnLocation=qn),/full/i.test(this.config.positionTracking))this.createTokenInstance=this.createFullToken;else if(/onlyStart/i.test(this.config.positionTracking))this.createTokenInstance=this.createStartOnlyToken;else if(/onlyOffset/i.test(this.config.positionTracking))this.createTokenInstance=this.createOffsetOnlyToken;else throw Error(`Invalid config option: "${this.config.positionTracking}"`);this.hasCustom?(this.addToken=this.addTokenUsingPush,this.handlePayload=this.handlePayloadWithCustom):(this.addToken=this.addTokenUsingMemberAccess,this.handlePayload=this.handlePayloadNoCustom)}),this.TRACE_INIT("Failed Optimization Warnings",()=>{let l=Vr(this.canModeBeOptimized,(u,h,f)=>(h===!1&&u.push(f),u),[]);if(r.ensureOptimizations&&!Qt(l))throw Error(`Lexer Modes: < ${l.join(", ")} > cannot be optimized. - Disable the "ensureOptimizations" lexer config flag to silently ignore this and run the lexer in an un-optimized mode. - Or inspect the console log for details on how to resolve these issues.`)}),this.TRACE_INIT("clearRegExpParserCache",()=>{uie()}),this.TRACE_INIT("toFastProperties",()=>{r2(this)})})}tokenize(e,r=this.defaultMode){if(!Qt(this.lexerDefinitionErrors)){let i=qe(this.lexerDefinitionErrors,a=>a.message).join(`----------------------- -`);throw new Error(`Unable to Tokenize because Errors detected in definition of Lexer: -`+i)}return this.tokenizeInternal(e,r)}tokenizeInternal(e,r){let n,i,a,s,l,u,h,f,d,p,m,g,y,v,x,b,w=e,S=w.length,T=0,E=0,_=this.hasCustom?0:Math.floor(e.length/10),A=new Array(_),L=[],M=this.trackStartLines?1:void 0,N=this.trackStartLines?1:void 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ie,j=this.config.recoveryEnabled;for(;Tu.length){u=s,h=f,ie=ce;break}}}break}}if(u!==null){if(d=u.length,p=ie.group,p!==void 0&&(m=ie.tokenTypeIdx,g=this.createTokenInstance(u,T,m,ie.tokenType,M,N,d),this.handlePayload(g,h),p===!1?E=this.addToken(A,E,g):k[p].push(g)),e=this.chopInput(e,d),T=T+d,N=this.computeNewColumn(N,d),I===!0&&ie.canLineTerminator===!0){let q=0,K,se;C.lastIndex=0;do K=C.test(u),K===!0&&(se=C.lastIndex-1,q++);while(K===!0);q!==0&&(M=M+q,N=d-se,this.updateTokenEndLineColumnLocation(g,p,se,q,M,N,d))}this.handleModes(ie,Q,X,g)}else{let q=T,K=M,se=N,ce=j===!1;for(;ce===!1&&T{"use strict";Pt();i2();s0();o(zu,"tokenLabel");o(gN,"hasTokenLabel");EIe="parent",Rie="categories",Nie="label",Mie="group",Iie="push_mode",Oie="pop_mode",Pie="longer_alt",Bie="line_breaks",Fie="start_chars_hint";o(VT,"createToken");o(CIe,"createTokenInternal");fo=VT({name:"EOF",pattern:ni.NA});Fu([fo]);o(o0,"createTokenInstance");o(s2,"tokenMatcher")});var Gu,zie,Ol,Jm=R(()=>{"use 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easy to accomplish by using the convention that Terminal names start with an uppercase letter -and Non-Terminal names start with a lower case letter.`},buildAlternationPrefixAmbiguityError(t){let e=qe(t.prefixPath,i=>zu(i)).join(", "),r=t.alternation.idx===0?"":t.alternation.idx;return`Ambiguous alternatives: <${t.ambiguityIndices.join(" ,")}> due to common lookahead prefix -in inside <${t.topLevelRule.name}> Rule, -<${e}> may appears as a prefix path in all these alternatives. -See: https://chevrotain.io/docs/guide/resolving_grammar_errors.html#COMMON_PREFIX -For Further details.`},buildAlternationAmbiguityError(t){let e=qe(t.prefixPath,i=>zu(i)).join(", "),r=t.alternation.idx===0?"":t.alternation.idx,n=`Ambiguous Alternatives Detected: <${t.ambiguityIndices.join(" ,")}> in inside <${t.topLevelRule.name}> Rule, -<${e}> may appears as a prefix path in all these alternatives. -`;return n=n+`See: https://chevrotain.io/docs/guide/resolving_grammar_errors.html#AMBIGUOUS_ALTERNATIVES -For Further details.`,n},buildEmptyRepetitionError(t){let e=Rs(t.repetition);return t.repetition.idx!==0&&(e+=t.repetition.idx),`The repetition <${e}> within Rule <${t.topLevelRule.name}> can never consume any tokens. -This could lead to an infinite loop.`},buildTokenNameError(t){return"deprecated"},buildEmptyAlternationError(t){return`Ambiguous empty alternative: <${t.emptyChoiceIdx+1}> in inside <${t.topLevelRule.name}> Rule. -Only the last alternative may be an empty alternative.`},buildTooManyAlternativesError(t){return`An Alternation cannot have more than 256 alternatives: - inside <${t.topLevelRule.name}> Rule. - has ${t.alternation.definition.length+1} alternatives.`},buildLeftRecursionError(t){let e=t.topLevelRule.name,r=qe(t.leftRecursionPath,a=>a.name),n=`${e} --> ${r.concat([e]).join(" --> ")}`;return`Left Recursion found in grammar. -rule: <${e}> can be invoked from itself (directly or indirectly) -without consuming any Tokens. The grammar path that causes this is: - ${n} - To fix this refactor your grammar to remove the left recursion. -see: https://en.wikipedia.org/wiki/LL_parser#Left_factoring.`},buildInvalidRuleNameError(t){return"deprecated"},buildDuplicateRuleNameError(t){let e;return t.topLevelRule instanceof ts?e=t.topLevelRule.name:e=t.topLevelRule,`Duplicate definition, rule: ->${e}<- is already defined in the grammar: ->${t.grammarName}<-`}}});function Gie(t,e){let r=new yN(t,e);return r.resolveRefs(),r.errors}var yN,$ie=R(()=>{"use strict";Ns();Pt();ns();o(Gie,"resolveGrammar");yN=class extends rs{static{o(this,"GastRefResolverVisitor")}constructor(e,r){super(),this.nameToTopRule=e,this.errMsgProvider=r,this.errors=[]}resolveRefs(){Ee(or(this.nameToTopRule),e=>{this.currTopLevel=e,e.accept(this)})}visitNonTerminal(e){let r=this.nameToTopRule[e.nonTerminalName];if(r)e.referencedRule=r;else{let n=this.errMsgProvider.buildRuleNotFoundError(this.currTopLevel,e);this.errors.push({message:n,type:Pi.UNRESOLVED_SUBRULE_REF,ruleName:this.currTopLevel.name,unresolvedRefName:e.nonTerminalName})}}}});function WT(t,e,r=[]){r=Qr(r);let n=[],i=0;function a(l){return l.concat(fi(t,i+1))}o(a,"remainingPathWith");function s(l){let u=WT(a(l),e,r);return n.concat(u)}for(o(s,"getAlternativesForProd");r.length{Qt(u.definition)===!1&&(n=s(u.definition))}),n;if(l instanceof fr)r.push(l.terminalType);else throw Error("non exhaustive match")}i++}return n.push({partialPath:r,suffixDef:fi(t,i)}),n}function qT(t,e,r,n){let i="EXIT_NONE_TERMINAL",a=[i],s="EXIT_ALTERNATIVE",l=!1,u=e.length,h=u-n-1,f=[],d=[];for(d.push({idx:-1,def:t,ruleStack:[],occurrenceStack:[]});!Qt(d);){let p=d.pop();if(p===s){l&&ma(d).idx<=h&&d.pop();continue}let m=p.def,g=p.idx,y=p.ruleStack,v=p.occurrenceStack;if(Qt(m))continue;let x=m[0];if(x===i){let b={idx:g,def:fi(m),ruleStack:Ru(y),occurrenceStack:Ru(v)};d.push(b)}else if(x 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this.ruleStack=Qr(this.path.ruleStack).reverse(),this.occurrenceStack=Qr(this.path.occurrenceStack).reverse(),this.ruleStack.pop(),this.occurrenceStack.pop(),this.updateExpectedNext(),this.walk(this.topProd),this.possibleTokTypes}walk(e,r=[]){this.found||super.walk(e,r)}walkProdRef(e,r,n){if(e.referencedRule.name===this.nextProductionName&&e.idx===this.nextProductionOccurrence){let i=r.concat(n);this.updateExpectedNext(),this.walk(e.referencedRule,i)}}updateExpectedNext(){Qt(this.ruleStack)?(this.nextProductionName="",this.nextProductionOccurrence=0,this.isAtEndOfPath=!0):(this.nextProductionName=this.ruleStack.pop(),this.nextProductionOccurrence=this.occurrenceStack.pop())}},UT=class extends vN{static{o(this,"NextAfterTokenWalker")}constructor(e,r){super(e,r),this.path=r,this.nextTerminalName="",this.nextTerminalOccurrence=0,this.nextTerminalName=this.path.lastTok.name,this.nextTerminalOccurrence=this.path.lastTokOccurrence}walkTerminal(e,r,n){if(this.isAtEndOfPath&&e.terminalType.name===this.nextTerminalName&&e.idx===this.nextTerminalOccurrence&&!this.found){let i=r.concat(n),a=new Sn({definition:i});this.possibleTokTypes=i0(a),this.found=!0}}},eg=class extends Pu{static{o(this,"AbstractNextTerminalAfterProductionWalker")}constructor(e,r){super(),this.topRule=e,this.occurrence=r,this.result={token:void 0,occurrence:void 0,isEndOfRule:void 0}}startWalking(){return this.walk(this.topRule),this.result}},HT=class extends eg{static{o(this,"NextTerminalAfterManyWalker")}walkMany(e,r,n){if(e.idx===this.occurrence){let i=na(r.concat(n));this.result.isEndOfRule=i===void 0,i instanceof fr&&(this.result.token=i.terminalType,this.result.occurrence=i.idx)}else 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i=[],a=Vr(t,(l,u,h)=>(e.definition[h].ignoreAmbiguities===!0||Ee(u,f=>{let d=[h];Ee(t,(p,m)=>{h!==m&&KT(p,f)&&e.definition[m].ignoreAmbiguities!==!0&&d.push(m)}),d.length>1&&!KT(i,f)&&(i.push(f),l.push({alts:d,path:f}))}),l),[]);return qe(a,l=>{let u=qe(l.alts,f=>f+1);return{message:n.buildAlternationAmbiguityError({topLevelRule:r,alternation:e,ambiguityIndices:u,prefixPath:l.path}),type:Pi.AMBIGUOUS_ALTS,ruleName:r.name,occurrence:e.idx,alternatives:l.alts}})}function MIe(t,e,r,n){let i=Vr(t,(s,l,u)=>{let h=qe(l,f=>({idx:u,path:f}));return s.concat(h)},[]);return wc(ga(i,s=>{if(e.definition[s.idx].ignoreAmbiguities===!0)return[];let u=s.idx,h=s.path,f=$r(i,p=>e.definition[p.idx].ignoreAmbiguities!==!0&&p.idx{let m=[p.idx+1,u+1],g=e.idx===0?"":e.idx;return{message:n.buildAlternationPrefixAmbiguityError({topLevelRule:r,alternation:e,ambiguityIndices:m,prefixPath:p.path}),type:Pi.AMBIGUOUS_PREFIX_ALTS,ruleName:r.name,occurrence:g,alternatives:m}})}))}function IIe(t,e,r){let n=[],i=qe(e,a=>a.name);return Ee(t,a=>{let s=a.name;if(Fn(i,s)){let l=r.buildNamespaceConflictError(a);n.push({message:l,type:Pi.CONFLICT_TOKENS_RULES_NAMESPACE,ruleName:s})}}),n}var wN,h2,TN,f2=R(()=>{"use strict";Pt();Ns();ns();ng();c2();s0();o(Kie,"validateLookahead");o(Qie,"validateGrammar");o(_Ie,"validateDuplicateProductions");o(LIe,"identifyProductionForDuplicates");o(Zie,"getExtraProductionArgument");wN=class extends 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l=this.getKeyForAutomaticLookahead(n,i),u=this.firstAfterRepMap[l];if(u===void 0){let p=this.getCurrRuleFullName(),m=this.getGAstProductions()[p];u=new a(m,i).startWalking(),this.firstAfterRepMap[l]=u}let h=u.token,f=u.occurrence,d=u.isEndOfRule;this.RULE_STACK.length===1&&d&&h===void 0&&(h=fo,f=1),!(h===void 0||f===void 0)&&this.shouldInRepetitionRecoveryBeTried(h,f,s)&&this.tryInRepetitionRecovery(t,e,r,h)}var EN,SN,CN,ZT,AN=R(()=>{"use strict";l0();Pt();ag();aN();Ns();EN={},SN="InRuleRecoveryException",CN=class extends Error{static{o(this,"InRuleRecoveryException")}constructor(e){super(e),this.name=SN}},ZT=class{static{o(this,"Recoverable")}initRecoverable(e){this.firstAfterRepMap={},this.resyncFollows={},this.recoveryEnabled=Xe(e,"recoveryEnabled")?e.recoveryEnabled:is.recoveryEnabled,this.recoveryEnabled&&(this.attemptInRepetitionRecovery=OIe)}getTokenToInsert(e){let r=o0(e,"",NaN,NaN,NaN,NaN,NaN,NaN);return 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strict";Pt();mae();o(FIe,"defaultVisit");o(gae,"createBaseSemanticVisitorConstructor");o(yae,"createBaseVisitorConstructorWithDefaults");(function(t){t[t.REDUNDANT_METHOD=0]="REDUNDANT_METHOD",t[t.MISSING_METHOD=1]="MISSING_METHOD"})(ON||(ON={}));o(zIe,"validateVisitor");o(GIe,"validateMissingCstMethods")});var sk,xae=R(()=>{"use strict";pae();Pt();vae();Ns();sk=class{static{o(this,"TreeBuilder")}initTreeBuilder(e){if(this.CST_STACK=[],this.outputCst=e.outputCst,this.nodeLocationTracking=Xe(e,"nodeLocationTracking")?e.nodeLocationTracking:is.nodeLocationTracking,!this.outputCst)this.cstInvocationStateUpdate=qn,this.cstFinallyStateUpdate=qn,this.cstPostTerminal=qn,this.cstPostNonTerminal=qn,this.cstPostRule=qn;else if(/full/i.test(this.nodeLocationTracking))this.recoveryEnabled?(this.setNodeLocationFromToken=MN,this.setNodeLocationFromNode=MN,this.cstPostRule=qn,this.setInitialNodeLocation=this.setInitialNodeLocationFullRecovery):(this.setNodeLocationFromToken=qn,this.setNodeLocationFromNode=qn,this.cstPostRule=this.cstPostRuleFull,this.setInitialNodeLocation=this.setInitialNodeLocationFullRegular);else if(/onlyOffset/i.test(this.nodeLocationTracking))this.recoveryEnabled?(this.setNodeLocationFromToken=NN,this.setNodeLocationFromNode=NN,this.cstPostRule=qn,this.setInitialNodeLocation=this.setInitialNodeLocationOnlyOffsetRecovery):(this.setNodeLocationFromToken=qn,this.setNodeLocationFromNode=qn,this.cstPostRule=this.cstPostRuleOnlyOffset,this.setInitialNodeLocation=this.setInitialNodeLocationOnlyOffsetRegular);else if(/none/i.test(this.nodeLocationTracking))this.setNodeLocationFromToken=qn,this.setNodeLocationFromNode=qn,this.cstPostRule=qn,this.setInitialNodeLocation=qn;else throw Error(`Invalid config option: "${e.nodeLocationTracking}"`)}setInitialNodeLocationOnlyOffsetRecovery(e){e.location={startOffset:NaN,endOffset:NaN}}setInitialNodeLocationOnlyOffsetRegular(e){e.location={startOffset:this.LA(1).startOffset,endOffset:NaN}}setInitialNodeLocationFullRecovery(e){e.location={startOffset:NaN,startLine:NaN,startColumn:NaN,endOffset:NaN,endLine:NaN,endColumn:NaN}}setInitialNodeLocationFullRegular(e){let r=this.LA(1);e.location={startOffset:r.startOffset,startLine:r.startLine,startColumn:r.startColumn,endOffset:NaN,endLine:NaN,endColumn:NaN}}cstInvocationStateUpdate(e){let r={name:e,children:Object.create(null)};this.setInitialNodeLocation(r),this.CST_STACK.push(r)}cstFinallyStateUpdate(){this.CST_STACK.pop()}cstPostRuleFull(e){let r=this.LA(0),n=e.location;n.startOffset<=r.startOffset?(n.endOffset=r.endOffset,n.endLine=r.endLine,n.endColumn=r.endColumn):(n.startOffset=NaN,n.startLine=NaN,n.startColumn=NaN)}cstPostRuleOnlyOffset(e){let r=this.LA(0),n=e.location;n.startOffset<=r.startOffset?n.endOffset=r.endOffset:n.startOffset=NaN}cstPostTerminal(e,r){let n=this.CST_STACK[this.CST_STACK.length-1];fae(n,r,e),this.setNodeLocationFromToken(n.location,r)}cstPostNonTerminal(e,r){let n=this.CST_STACK[this.CST_STACK.length-1];dae(n,r,e),this.setNodeLocationFromNode(n.location,e.location)}getBaseCstVisitorConstructor(){if(er(this.baseCstVisitorConstructor)){let e=gae(this.className,Dr(this.gastProductionsCache));return this.baseCstVisitorConstructor=e,e}return this.baseCstVisitorConstructor}getBaseCstVisitorConstructorWithDefaults(){if(er(this.baseCstVisitorWithDefaultsConstructor)){let e=yae(this.className,Dr(this.gastProductionsCache),this.getBaseCstVisitorConstructor());return this.baseCstVisitorWithDefaultsConstructor=e,e}return this.baseCstVisitorWithDefaultsConstructor}getLastExplicitRuleShortName(){let e=this.RULE_STACK;return e[e.length-1]}getPreviousExplicitRuleShortName(){let e=this.RULE_STACK;return e[e.length-2]}getLastExplicitRuleOccurrenceIndex(){let e=this.RULE_OCCURRENCE_STACK;return e[e.length-1]}}});var ok,bae=R(()=>{"use strict";Ns();ok=class{static{o(this,"LexerAdapter")}initLexerAdapter(){this.tokVector=[],this.tokVectorLength=0,this.currIdx=-1}set input(e){if(this.selfAnalysisDone!==!0)throw Error("Missing invocation at the end of the Parser's constructor.");this.reset(),this.tokVector=e,this.tokVectorLength=e.length}get input(){return this.tokVector}SKIP_TOKEN(){return this.currIdx<=this.tokVector.length-2?(this.consumeToken(),this.LA(1)):sg}LA(e){let r=this.currIdx+e;return r<0||this.tokVectorLength<=r?sg:this.tokVector[r]}consumeToken(){this.currIdx++}exportLexerState(){return this.currIdx}importLexerState(e){this.currIdx=e}resetLexerState(){this.currIdx=-1}moveToTerminatedState(){this.currIdx=this.tokVector.length-1}getLexerPosition(){return this.exportLexerState()}}});var lk,wae=R(()=>{"use strict";Pt();ag();Ns();Jm();f2();ns();lk=class{static{o(this,"RecognizerApi")}ACTION(e){return e.call(this)}consume(e,r,n){return this.consumeInternal(r,e,n)}subrule(e,r,n){return this.subruleInternal(r,e,n)}option(e,r){return this.optionInternal(r,e)}or(e,r){return this.orInternal(r,e)}many(e,r){return this.manyInternal(e,r)}atLeastOne(e,r){return this.atLeastOneInternal(e,r)}CONSUME(e,r){return this.consumeInternal(e,0,r)}CONSUME1(e,r){return this.consumeInternal(e,1,r)}CONSUME2(e,r){return this.consumeInternal(e,2,r)}CONSUME3(e,r){return this.consumeInternal(e,3,r)}CONSUME4(e,r){return this.consumeInternal(e,4,r)}CONSUME5(e,r){return this.consumeInternal(e,5,r)}CONSUME6(e,r){return this.consumeInternal(e,6,r)}CONSUME7(e,r){return this.consumeInternal(e,7,r)}CONSUME8(e,r){return this.consumeInternal(e,8,r)}CONSUME9(e,r){return this.consumeInternal(e,9,r)}SUBRULE(e,r){return this.subruleInternal(e,0,r)}SUBRULE1(e,r){return this.subruleInternal(e,1,r)}SUBRULE2(e,r){return this.subruleInternal(e,2,r)}SUBRULE3(e,r){return this.subruleInternal(e,3,r)}SUBRULE4(e,r){return this.subruleInternal(e,4,r)}SUBRULE5(e,r){return this.subruleInternal(e,5,r)}SUBRULE6(e,r){return this.subruleInternal(e,6,r)}SUBRULE7(e,r){return this.subruleInternal(e,7,r)}SUBRULE8(e,r){return this.subruleInternal(e,8,r)}SUBRULE9(e,r){return this.subruleInternal(e,9,r)}OPTION(e){return this.optionInternal(e,0)}OPTION1(e){return this.optionInternal(e,1)}OPTION2(e){return this.optionInternal(e,2)}OPTION3(e){return this.optionInternal(e,3)}OPTION4(e){return this.optionInternal(e,4)}OPTION5(e){return this.optionInternal(e,5)}OPTION6(e){return this.optionInternal(e,6)}OPTION7(e){return this.optionInternal(e,7)}OPTION8(e){return this.optionInternal(e,8)}OPTION9(e){return this.optionInternal(e,9)}OR(e){return this.orInternal(e,0)}OR1(e){return this.orInternal(e,1)}OR2(e){return this.orInternal(e,2)}OR3(e){return this.orInternal(e,3)}OR4(e){return this.orInternal(e,4)}OR5(e){return this.orInternal(e,5)}OR6(e){return this.orInternal(e,6)}OR7(e){return this.orInternal(e,7)}OR8(e){return this.orInternal(e,8)}OR9(e){return this.orInternal(e,9)}MANY(e){this.manyInternal(0,e)}MANY1(e){this.manyInternal(1,e)}MANY2(e){this.manyInternal(2,e)}MANY3(e){this.manyInternal(3,e)}MANY4(e){this.manyInternal(4,e)}MANY5(e){this.manyInternal(5,e)}MANY6(e){this.manyInternal(6,e)}MANY7(e){this.manyInternal(7,e)}MANY8(e){this.manyInternal(8,e)}MANY9(e){this.manyInternal(9,e)}MANY_SEP(e){this.manySepFirstInternal(0,e)}MANY_SEP1(e){this.manySepFirstInternal(1,e)}MANY_SEP2(e){this.manySepFirstInternal(2,e)}MANY_SEP3(e){this.manySepFirstInternal(3,e)}MANY_SEP4(e){this.manySepFirstInternal(4,e)}MANY_SEP5(e){this.manySepFirstInternal(5,e)}MANY_SEP6(e){this.manySepFirstInternal(6,e)}MANY_SEP7(e){this.manySepFirstInternal(7,e)}MANY_SEP8(e){this.manySepFirstInternal(8,e)}MANY_SEP9(e){this.manySepFirstInternal(9,e)}AT_LEAST_ONE(e){this.atLeastOneInternal(0,e)}AT_LEAST_ONE1(e){return this.atLeastOneInternal(1,e)}AT_LEAST_ONE2(e){this.atLeastOneInternal(2,e)}AT_LEAST_ONE3(e){this.atLeastOneInternal(3,e)}AT_LEAST_ONE4(e){this.atLeastOneInternal(4,e)}AT_LEAST_ONE5(e){this.atLeastOneInternal(5,e)}AT_LEAST_ONE6(e){this.atLeastOneInternal(6,e)}AT_LEAST_ONE7(e){this.atLeastOneInternal(7,e)}AT_LEAST_ONE8(e){this.atLeastOneInternal(8,e)}AT_LEAST_ONE9(e){this.atLeastOneInternal(9,e)}AT_LEAST_ONE_SEP(e){this.atLeastOneSepFirstInternal(0,e)}AT_LEAST_ONE_SEP1(e){this.atLeastOneSepFirstInternal(1,e)}AT_LEAST_ONE_SEP2(e){this.atLeastOneSepFirstInternal(2,e)}AT_LEAST_ONE_SEP3(e){this.atLeastOneSepFirstInternal(3,e)}AT_LEAST_ONE_SEP4(e){this.atLeastOneSepFirstInternal(4,e)}AT_LEAST_ONE_SEP5(e){this.atLeastOneSepFirstInternal(5,e)}AT_LEAST_ONE_SEP6(e){this.atLeastOneSepFirstInternal(6,e)}AT_LEAST_ONE_SEP7(e){this.atLeastOneSepFirstInternal(7,e)}AT_LEAST_ONE_SEP8(e){this.atLeastOneSepFirstInternal(8,e)}AT_LEAST_ONE_SEP9(e){this.atLeastOneSepFirstInternal(9,e)}RULE(e,r,n=og){if(Fn(this.definedRulesNames,e)){let s={message:Ol.buildDuplicateRuleNameError({topLevelRule:e,grammarName:this.className}),type:Pi.DUPLICATE_RULE_NAME,ruleName:e};this.definitionErrors.push(s)}this.definedRulesNames.push(e);let i=this.defineRule(e,r,n);return this[e]=i,i}OVERRIDE_RULE(e,r,n=og){let i=Jie(e,this.definedRulesNames,this.className);this.definitionErrors=this.definitionErrors.concat(i);let a=this.defineRule(e,r,n);return this[e]=a,a}BACKTRACK(e,r){return function(){this.isBackTrackingStack.push(1);let n=this.saveRecogState();try{return e.apply(this,r),!0}catch(i){if(nf(i))return!1;throw i}finally{this.reloadRecogState(n),this.isBackTrackingStack.pop()}}}getGAstProductions(){return this.gastProductionsCache}getSerializedGastProductions(){return NT(or(this.gastProductionsCache))}}});var ck,Tae=R(()=>{"use strict";Pt();ek();ag();ng();c2();Ns();AN();l0();s0();ck=class{static{o(this,"RecognizerEngine")}initRecognizerEngine(e,r){if(this.className=this.constructor.name,this.shortRuleNameToFull={},this.fullRuleNameToShort={},this.ruleShortNameIdx=256,this.tokenMatcher=Zm,this.subruleIdx=0,this.definedRulesNames=[],this.tokensMap={},this.isBackTrackingStack=[],this.RULE_STACK=[],this.RULE_OCCURRENCE_STACK=[],this.gastProductionsCache={},Xe(r,"serializedGrammar"))throw Error(`The Parser's configuration can no longer contain a property. - See: https://chevrotain.io/docs/changes/BREAKING_CHANGES.html#_6-0-0 - For Further details.`);if(wt(e)){if(Qt(e))throw Error(`A Token Vocabulary cannot be empty. - Note that the first argument for the parser constructor - is no longer a Token vector (since v4.0).`);if(typeof e[0].startOffset=="number")throw Error(`The Parser constructor no longer accepts a token vector as the first argument. - See: https://chevrotain.io/docs/changes/BREAKING_CHANGES.html#_4-0-0 - For Further details.`)}if(wt(e))this.tokensMap=Vr(e,(a,s)=>(a[s.name]=s,a),{});else if(Xe(e,"modes")&&Ia(Gr(or(e.modes)),Die)){let a=Gr(or(e.modes)),s=Pm(a);this.tokensMap=Vr(s,(l,u)=>(l[u.name]=u,l),{})}else if(pn(e))this.tokensMap=Qr(e);else throw new Error(" argument must be An Array of Token constructors, A dictionary of Token constructors or an IMultiModeLexerDefinition");this.tokensMap.EOF=fo;let n=Xe(e,"modes")?Gr(or(e.modes)):or(e),i=Ia(n,a=>Qt(a.categoryMatches));this.tokenMatcher=i?Zm:Bu,Fu(or(this.tokensMap))}defineRule(e,r,n){if(this.selfAnalysisDone)throw Error(`Grammar rule <${e}> may not be defined after the 'performSelfAnalysis' method has been called' -Make sure that all grammar rule definitions are done before 'performSelfAnalysis' is called.`);let i=Xe(n,"resyncEnabled")?n.resyncEnabled:og.resyncEnabled,a=Xe(n,"recoveryValueFunc")?n.recoveryValueFunc:og.recoveryValueFunc,s=this.ruleShortNameIdx<<12;this.ruleShortNameIdx++,this.shortRuleNameToFull[s]=e,this.fullRuleNameToShort[e]=s;let l;return this.outputCst===!0?l=o(function(...f){try{this.ruleInvocationStateUpdate(s,e,this.subruleIdx),r.apply(this,f);let d=this.CST_STACK[this.CST_STACK.length-1];return this.cstPostRule(d),d}catch(d){return this.invokeRuleCatch(d,i,a)}finally{this.ruleFinallyStateUpdate()}},"invokeRuleWithTry"):l=o(function(...f){try{return this.ruleInvocationStateUpdate(s,e,this.subruleIdx),r.apply(this,f)}catch(d){return this.invokeRuleCatch(d,i,a)}finally{this.ruleFinallyStateUpdate()}},"invokeRuleWithTryCst"),Object.assign(l,{ruleName:e,originalGrammarAction:r})}invokeRuleCatch(e,r,n){let i=this.RULE_STACK.length===1,a=r&&!this.isBackTracking()&&this.recoveryEnabled;if(nf(e)){let s=e;if(a){let l=this.findReSyncTokenType();if(this.isInCurrentRuleReSyncSet(l))if(s.resyncedTokens=this.reSyncTo(l),this.outputCst){let u=this.CST_STACK[this.CST_STACK.length-1];return u.recoveredNode=!0,u}else return n(e);else{if(this.outputCst){let u=this.CST_STACK[this.CST_STACK.length-1];u.recoveredNode=!0,s.partialCstResult=u}throw s}}else{if(i)return this.moveToTerminatedState(),n(e);throw s}}else throw e}optionInternal(e,r){let n=this.getKeyForAutomaticLookahead(512,r);return this.optionInternalLogic(e,r,n)}optionInternalLogic(e,r,n){let i=this.getLaFuncFromCache(n),a;if(typeof e!="function"){a=e.DEF;let s=e.GATE;if(s!==void 0){let l=i;i=o(()=>s.call(this)&&l.call(this),"lookAheadFunc")}}else a=e;if(i.call(this)===!0)return a.call(this)}atLeastOneInternal(e,r){let n=this.getKeyForAutomaticLookahead(1024,e);return this.atLeastOneInternalLogic(e,r,n)}atLeastOneInternalLogic(e,r,n){let i=this.getLaFuncFromCache(n),a;if(typeof r!="function"){a=r.DEF;let s=r.GATE;if(s!==void 0){let l=i;i=o(()=>s.call(this)&&l.call(this),"lookAheadFunc")}}else a=r;if(i.call(this)===!0){let s=this.doSingleRepetition(a);for(;i.call(this)===!0&&s===!0;)s=this.doSingleRepetition(a)}else throw this.raiseEarlyExitException(e,$n.REPETITION_MANDATORY,r.ERR_MSG);this.attemptInRepetitionRecovery(this.atLeastOneInternal,[e,r],i,1024,e,YT)}atLeastOneSepFirstInternal(e,r){let n=this.getKeyForAutomaticLookahead(1536,e);this.atLeastOneSepFirstInternalLogic(e,r,n)}atLeastOneSepFirstInternalLogic(e,r,n){let i=r.DEF,a=r.SEP;if(this.getLaFuncFromCache(n).call(this)===!0){i.call(this);let l=o(()=>this.tokenMatcher(this.LA(1),a),"separatorLookAheadFunc");for(;this.tokenMatcher(this.LA(1),a)===!0;)this.CONSUME(a),i.call(this);this.attemptInRepetitionRecovery(this.repetitionSepSecondInternal,[e,a,l,i,l2],l,1536,e,l2)}else throw this.raiseEarlyExitException(e,$n.REPETITION_MANDATORY_WITH_SEPARATOR,r.ERR_MSG)}manyInternal(e,r){let n=this.getKeyForAutomaticLookahead(768,e);return this.manyInternalLogic(e,r,n)}manyInternalLogic(e,r,n){let i=this.getLaFuncFromCache(n),a;if(typeof r!="function"){a=r.DEF;let l=r.GATE;if(l!==void 0){let u=i;i=o(()=>l.call(this)&&u.call(this),"lookaheadFunction")}}else a=r;let s=!0;for(;i.call(this)===!0&&s===!0;)s=this.doSingleRepetition(a);this.attemptInRepetitionRecovery(this.manyInternal,[e,r],i,768,e,HT,s)}manySepFirstInternal(e,r){let n=this.getKeyForAutomaticLookahead(1280,e);this.manySepFirstInternalLogic(e,r,n)}manySepFirstInternalLogic(e,r,n){let i=r.DEF,a=r.SEP;if(this.getLaFuncFromCache(n).call(this)===!0){i.call(this);let l=o(()=>this.tokenMatcher(this.LA(1),a),"separatorLookAheadFunc");for(;this.tokenMatcher(this.LA(1),a)===!0;)this.CONSUME(a),i.call(this);this.attemptInRepetitionRecovery(this.repetitionSepSecondInternal,[e,a,l,i,o2],l,1280,e,o2)}}repetitionSepSecondInternal(e,r,n,i,a){for(;n();)this.CONSUME(r),i.call(this);this.attemptInRepetitionRecovery(this.repetitionSepSecondInternal,[e,r,n,i,a],n,1536,e,a)}doSingleRepetition(e){let r=this.getLexerPosition();return e.call(this),this.getLexerPosition()>r}orInternal(e,r){let n=this.getKeyForAutomaticLookahead(256,r),i=wt(e)?e:e.DEF,s=this.getLaFuncFromCache(n).call(this,i);if(s!==void 0)return i[s].ALT.call(this);this.raiseNoAltException(r,e.ERR_MSG)}ruleFinallyStateUpdate(){if(this.RULE_STACK.pop(),this.RULE_OCCURRENCE_STACK.pop(),this.cstFinallyStateUpdate(),this.RULE_STACK.length===0&&this.isAtEndOfInput()===!1){let e=this.LA(1),r=this.errorMessageProvider.buildNotAllInputParsedMessage({firstRedundant:e,ruleName:this.getCurrRuleFullName()});this.SAVE_ERROR(new p2(r,e))}}subruleInternal(e,r,n){let i;try{let a=n!==void 0?n.ARGS:void 0;return this.subruleIdx=r,i=e.apply(this,a),this.cstPostNonTerminal(i,n!==void 0&&n.LABEL!==void 0?n.LABEL:e.ruleName),i}catch(a){throw this.subruleInternalError(a,n,e.ruleName)}}subruleInternalError(e,r,n){throw nf(e)&&e.partialCstResult!==void 0&&(this.cstPostNonTerminal(e.partialCstResult,r!==void 0&&r.LABEL!==void 0?r.LABEL:n),delete e.partialCstResult),e}consumeInternal(e,r,n){let i;try{let a=this.LA(1);this.tokenMatcher(a,e)===!0?(this.consumeToken(),i=a):this.consumeInternalError(e,a,n)}catch(a){i=this.consumeInternalRecovery(e,r,a)}return this.cstPostTerminal(n!==void 0&&n.LABEL!==void 0?n.LABEL:e.name,i),i}consumeInternalError(e,r,n){let i,a=this.LA(0);throw n!==void 0&&n.ERR_MSG?i=n.ERR_MSG:i=this.errorMessageProvider.buildMismatchTokenMessage({expected:e,actual:r,previous:a,ruleName:this.getCurrRuleFullName()}),this.SAVE_ERROR(new c0(i,r,a))}consumeInternalRecovery(e,r,n){if(this.recoveryEnabled&&n.name==="MismatchedTokenException"&&!this.isBackTracking()){let i=this.getFollowsForInRuleRecovery(e,r);try{return this.tryInRuleRecovery(e,i)}catch(a){throw a.name===SN?n:a}}else throw n}saveRecogState(){let e=this.errors,r=Qr(this.RULE_STACK);return{errors:e,lexerState:this.exportLexerState(),RULE_STACK:r,CST_STACK:this.CST_STACK}}reloadRecogState(e){this.errors=e.errors,this.importLexerState(e.lexerState),this.RULE_STACK=e.RULE_STACK}ruleInvocationStateUpdate(e,r,n){this.RULE_OCCURRENCE_STACK.push(n),this.RULE_STACK.push(e),this.cstInvocationStateUpdate(r)}isBackTracking(){return this.isBackTrackingStack.length!==0}getCurrRuleFullName(){let e=this.getLastExplicitRuleShortName();return this.shortRuleNameToFull[e]}shortRuleNameToFullName(e){return this.shortRuleNameToFull[e]}isAtEndOfInput(){return this.tokenMatcher(this.LA(1),fo)}reset(){this.resetLexerState(),this.subruleIdx=0,this.isBackTrackingStack=[],this.errors=[],this.RULE_STACK=[],this.CST_STACK=[],this.RULE_OCCURRENCE_STACK=[]}}});var uk,kae=R(()=>{"use strict";ag();Pt();ng();Ns();uk=class{static{o(this,"ErrorHandler")}initErrorHandler(e){this._errors=[],this.errorMessageProvider=Xe(e,"errorMessageProvider")?e.errorMessageProvider:is.errorMessageProvider}SAVE_ERROR(e){if(nf(e))return e.context={ruleStack:this.getHumanReadableRuleStack(),ruleOccurrenceStack:Qr(this.RULE_OCCURRENCE_STACK)},this._errors.push(e),e;throw Error("Trying to save an Error which is not a RecognitionException")}get errors(){return Qr(this._errors)}set errors(e){this._errors=e}raiseEarlyExitException(e,r,n){let i=this.getCurrRuleFullName(),a=this.getGAstProductions()[i],l=rg(e,a,r,this.maxLookahead)[0],u=[];for(let f=1;f<=this.maxLookahead;f++)u.push(this.LA(f));let h=this.errorMessageProvider.buildEarlyExitMessage({expectedIterationPaths:l,actual:u,previous:this.LA(0),customUserDescription:n,ruleName:i});throw this.SAVE_ERROR(new m2(h,this.LA(1),this.LA(0)))}raiseNoAltException(e,r){let n=this.getCurrRuleFullName(),i=this.getGAstProductions()[n],a=tg(e,i,this.maxLookahead),s=[];for(let h=1;h<=this.maxLookahead;h++)s.push(this.LA(h));let l=this.LA(0),u=this.errorMessageProvider.buildNoViableAltMessage({expectedPathsPerAlt:a,actual:s,previous:l,customUserDescription:r,ruleName:this.getCurrRuleFullName()});throw this.SAVE_ERROR(new d2(u,this.LA(1),l))}}});var hk,Eae=R(()=>{"use strict";c2();Pt();hk=class{static{o(this,"ContentAssist")}initContentAssist(){}computeContentAssist(e,r){let n=this.gastProductionsCache[e];if(er(n))throw Error(`Rule ->${e}<- does not exist in this grammar.`);return qT([n],r,this.tokenMatcher,this.maxLookahead)}getNextPossibleTokenTypes(e){let r=na(e.ruleStack),i=this.getGAstProductions()[r];return new UT(i,e).startWalking()}}});function y2(t,e,r,n=!1){dk(r);let i=ma(this.recordingProdStack),a=wi(e)?e:e.DEF,s=new t({definition:[],idx:r});return n&&(s.separator=e.SEP),Xe(e,"MAX_LOOKAHEAD")&&(s.maxLookahead=e.MAX_LOOKAHEAD),this.recordingProdStack.push(s),a.call(this),i.definition.push(s),this.recordingProdStack.pop(),pk}function UIe(t,e){dk(e);let r=ma(this.recordingProdStack),n=wt(t)===!1,i=n===!1?t:t.DEF,a=new gn({definition:[],idx:e,ignoreAmbiguities:n&&t.IGNORE_AMBIGUITIES===!0});Xe(t,"MAX_LOOKAHEAD")&&(a.maxLookahead=t.MAX_LOOKAHEAD);let s=Nv(i,l=>wi(l.GATE));return a.hasPredicates=s,r.definition.push(a),Ee(i,l=>{let u=new Sn({definition:[]});a.definition.push(u),Xe(l,"IGNORE_AMBIGUITIES")?u.ignoreAmbiguities=l.IGNORE_AMBIGUITIES:Xe(l,"GATE")&&(u.ignoreAmbiguities=!0),this.recordingProdStack.push(u),l.ALT.call(this),this.recordingProdStack.pop()}),pk}function Aae(t){return t===0?"":`${t}`}function dk(t){if(t<0||t>Sae){let e=new Error(`Invalid DSL Method idx value: <${t}> - Idx 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Ge=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),Ae.length-1&&(this.yylineno-=Ae.length-1);var Me=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:Ae?(Ae.length===Ge.length?this.yylloc.first_column:0)+Ge[Ge.length-Ae.length].length-Ae[0].length:this.yylloc.first_column-Ce},this.options.ranges&&(this.yylloc.range=[Me[0],Me[0]+this.yyleng-Ce]),this.yyleng=this.yytext.length,this},"unput"),more:o(function(){return this._more=!0,this},"more"),reject:o(function(){if(this.options.backtrack_lexer)this._backtrack=!0;else return this.parseError("Lexical error on line "+(this.yylineno+1)+`. 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32:this.popState(),this.begin("point_y");break;case 33:return this.popState(),46;break;case 34:return 28;case 35:return 4;case 36:return 11;case 37:return 64;case 38:return 10;case 39:return 65;case 40:return 65;case 41:return 14;case 42:return 13;case 43:return 67;case 44:return 66;case 45:return 12;case 46:return 8;case 47:return 5;case 48:return 18;case 49:return 56;case 50:return 63;case 51:return 57}},"anonymous"),rules:[/^(?:%%(?!\{)[^\n]*)/i,/^(?:[^\}]%%[^\n]*)/i,/^(?:[\n\r]+)/i,/^(?:%%[^\n]*)/i,/^(?:title\b)/i,/^(?:(?!\n||)*[^\n]*)/i,/^(?:accTitle\s*:\s*)/i,/^(?:(?!\n||)*[^\n]*)/i,/^(?:accDescr\s*:\s*)/i,/^(?:(?!\n||)*[^\n]*)/i,/^(?:accDescr\s*\{\s*)/i,/^(?:[\}])/i,/^(?:[^\}]*)/i,/^(?: *x-axis *)/i,/^(?: *y-axis *)/i,/^(?: *--+> *)/i,/^(?: *quadrant-1 *)/i,/^(?: *quadrant-2 *)/i,/^(?: *quadrant-3 *)/i,/^(?: *quadrant-4 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Map,V.info("clear called")}setData(e){this.data={...this.data,...e}}addPoints(e){this.data.points=[...e,...this.data.points]}addClass(e,r){this.classes.set(e,r)}setConfig(e){V.trace("setConfig called with: ",e),this.config={...this.config,...e}}setThemeConfig(e){V.trace("setThemeConfig called with: ",e),this.themeConfig={...this.themeConfig,...e}}calculateSpace(e,r,n,i){let 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this.textDimensionCalculator.getMaxDimension(this.getTickValues().map(e=>e.toString()),this.axisConfig.labelFontSize)}recalculateOuterPaddingToDrawBar(){.7*this.getTickDistance()>this.outerPadding*2&&(this.outerPadding=Math.floor(.7*this.getTickDistance()/2)),this.recalculateScale()}calculateSpaceIfDrawnHorizontally(e){let r=e.height;if(this.axisConfig.showAxisLine&&r>this.axisConfig.axisLineWidth&&(r-=this.axisConfig.axisLineWidth,this.showAxisLine=!0),this.axisConfig.showLabel){let n=this.getLabelDimension(),i=.2*e.width;this.outerPadding=Math.min(n.width/2,i);let a=n.height+this.axisConfig.labelPadding*2;this.labelTextHeight=n.height,a<=r&&(r-=a,this.showLabel=!0)}if(this.axisConfig.showTick&&r>=this.axisConfig.tickLength&&(this.showTick=!0,r-=this.axisConfig.tickLength),this.axisConfig.showTitle&&this.title){let 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49:te[oe-1].draw="participant",te[oe-1].type="addParticipant",this.$=te[oe-1];break;case 50:te[oe-3].draw="actor",te[oe-3].type="addParticipant",te[oe-3].description=ce.parseMessage(te[oe-1]),this.$=te[oe-3];break;case 51:te[oe-1].draw="actor",te[oe-1].type="addParticipant",this.$=te[oe-1];break;case 52:te[oe-1].type="destroyParticipant",this.$=te[oe-1];break;case 53:this.$=[te[oe-1],{type:"addNote",placement:te[oe-2],actor:te[oe-1].actor,text:te[oe]}];break;case 54:te[oe-2]=[].concat(te[oe-1],te[oe-1]).slice(0,2),te[oe-2][0]=te[oe-2][0].actor,te[oe-2][1]=te[oe-2][1].actor,this.$=[te[oe-1],{type:"addNote",placement:ce.PLACEMENT.OVER,actor:te[oe-2].slice(0,2),text:te[oe]}];break;case 55:this.$=[te[oe-1],{type:"addLinks",actor:te[oe-1].actor,text:te[oe]}];break;case 56:this.$=[te[oe-1],{type:"addALink",actor:te[oe-1].actor,text:te[oe]}];break;case 57:this.$=[te[oe-1],{type:"addProperties",actor:te[oe-1].actor,text:te[oe]}];break;case 58:this.$=[te[oe-1],{type:"addDetails",actor:te[oe-1].actor,text:te[oe]}];break;case 61:this.$=[te[oe-2],te[oe]];break;case 62:this.$=te[oe];break;case 63:this.$=ce.PLACEMENT.LEFTOF;break;case 64:this.$=ce.PLACEMENT.RIGHTOF;break;case 65:this.$=[te[oe-4],te[oe-1],{type:"addMessage",from:te[oe-4].actor,to:te[oe-1].actor,signalType:te[oe-3],msg:te[oe],activate:!0},{type:"activeStart",signalType:ce.LINETYPE.ACTIVE_START,actor:te[oe-1].actor}];break;case 66:this.$=[te[oe-4],te[oe-1],{type:"addMessage",from:te[oe-4].actor,to:te[oe-1].actor,signalType:te[oe-3],msg:te[oe]},{type:"activeEnd",signalType:ce.LINETYPE.ACTIVE_END,actor:te[oe-4].actor}];break;case 67:this.$=[te[oe-3],te[oe-1],{type:"addMessage",from:te[oe-3].actor,to:te[oe-1].actor,signalType:te[oe-2],msg:te[oe]}];break;case 68:this.$={type:"addParticipant",actor:te[oe]};break;case 69:this.$=ce.LINETYPE.SOLID_OPEN;break;case 70:this.$=ce.LINETYPE.DOTTED_OPEN;break;case 71:this.$=ce.LINETYPE.SOLID;break;case 72:this.$=ce.LINETYPE.BIDIRECTIONAL_SOLID;break;case 73:this.$=ce.LINETYPE.DOTTED;break;case 74:this.$=ce.LINETYPE.BIDIRECTIONAL_DOTTED;break;case 75:this.$=ce.LINETYPE.SOLID_CROSS;break;case 76:this.$=ce.LINETYPE.DOTTED_CROSS;break;case 77:this.$=ce.LINETYPE.SOLID_POINT;break;case 78:this.$=ce.LINETYPE.DOTTED_POINT;break;case 79:this.$=ce.parseMessage(te[oe].trim().substring(1));break}},"anonymous"),table:[{3:1,4:e,5:r,6:n},{1:[3]},{3:5,4:e,5:r,6:n},{3:6,4:e,5:r,6:n},t([1,4,5,13,14,18,21,23,29,30,31,33,35,36,37,38,39,41,43,44,46,50,52,53,54,59,60,61,62,70],i,{7:7}),{1:[2,1]},{1:[2,2]},{1:[2,3],4:a,5:s,8:8,9:10,12:12,13:l,14:u,17:15,18:h,21:f,22:40,23:d,24:19,25:20,26:21,27:22,28:23,29:p,30:m,31:g,33:y,35:v,36:x,37:b,38:w,39:S,41:T,43:E,44:_,46:A,50:L,52:M,53:N,54:k,59:I,60:C,61:O,62:D,70:P},t(F,[2,5]),{9:47,12:12,13:l,14:u,17:15,18:h,21:f,22:40,23:d,24:19,25:20,26:21,27:22,28:23,29:p,30:m,31:g,33:y,35:v,36:x,37:b,38:w,39:S,41:T,43:E,44:_,46:A,50:L,52:M,53:N,54:k,59:I,60:C,61:O,62:D,70:P},t(F,[2,7]),t(F,[2,8]),t(F,[2,14]),{12:48,50:L,52:M,53:N},{15:[1,49]},{5:[1,50]},{5:[1,53],19:[1,51],20:[1,52]},{22:54,70:P},{22:55,70:P},{5:[1,56]},{5:[1,57]},{5:[1,58]},{5:[1,59]},{5:[1,60]},t(F,[2,29]),t(F,[2,30]),{32:[1,61]},{34:[1,62]},t(F,[2,33]),{15:[1,63]},{15:[1,64]},{15:[1,65]},{15:[1,66]},{15:[1,67]},{15:[1,68]},{15:[1,69]},{15:[1,70]},{22:71,70:P},{22:72,70:P},{22:73,70:P},{67:74,71:[1,75],72:[1,76],73:[1,77],74:[1,78],75:[1,79],76:[1,80],77:[1,81],78:[1,82],79:[1,83],80:[1,84]},{55:85,57:[1,86],65:[1,87],66:[1,88]},{22:89,70:P},{22:90,70:P},{22:91,70:P},{22:92,70:P},t([5,51,64,71,72,73,74,75,76,77,78,79,80,81],[2,68]),t(F,[2,6]),t(F,[2,15]),t(B,[2,9],{10:93}),t(F,[2,17]),{5:[1,95],19:[1,94]},{5:[1,96]},t(F,[2,21]),{5:[1,97]},{5:[1,98]},t(F,[2,24]),t(F,[2,25]),t(F,[2,26]),t(F,[2,27]),t(F,[2,28]),t(F,[2,31]),t(F,[2,32]),t($,i,{7:99}),t($,i,{7:100}),t($,i,{7:101}),t(z,i,{40:102,7:103}),t(Y,i,{42:104,7:105}),t(Y,i,{7:105,42:106}),t(Q,i,{45:107,7:108}),t($,i,{7:109}),{5:[1,111],51:[1,110]},{5:[1,113],51:[1,112]},{5:[1,114]},{22:117,68:[1,115],69:[1,116],70:P},t(X,[2,69]),t(X,[2,70]),t(X,[2,71]),t(X,[2,72]),t(X,[2,73]),t(X,[2,74]),t(X,[2,75]),t(X,[2,76]),t(X,[2,77]),t(X,[2,78]),{22:118,70:P},{22:120,58:119,70:P},{70:[2,63]},{70:[2,64]},{56:121,81:ie},{56:123,81:ie},{56:124,81:ie},{56:125,81:ie},{4:[1,128],5:[1,130],11:127,12:129,16:[1,126],50:L,52:M,53:N},{5:[1,131]},t(F,[2,19]),t(F,[2,20]),t(F,[2,22]),t(F,[2,23]),{4:a,5:s,8:8,9:10,12:12,13:l,14:u,16:[1,132],17:15,18:h,21:f,22:40,23:d,24:19,25:20,26:21,27:22,28:23,29:p,30:m,31:g,33:y,35:v,36:x,37:b,38:w,39:S,41:T,43:E,44:_,46:A,50:L,52:M,53:N,54:k,59:I,60:C,61:O,62:D,70:P},{4:a,5:s,8:8,9:10,12:12,13:l,14:u,16:[1,133],17:15,18:h,21:f,22:40,23:d,24:19,25:20,26:21,27:22,28:23,29:p,30:m,31:g,33:y,35:v,36:x,37:b,38:w,39:S,41:T,43:E,44:_,46:A,50:L,52:M,53:N,54:k,59:I,60:C,61:O,62:D,70:P},{4:a,5:s,8:8,9:10,12:12,13:l,14:u,16:[1,134],17:15,18:h,21:f,22:40,23:d,24:19,25:20,26:21,27:22,28:23,29:p,30:m,31:g,33:y,35:v,36:x,37:b,38:w,39:S,41:T,43:E,44:_,46:A,50:L,52:M,53:N,54:k,59:I,60:C,61:O,62:D,70:P},{16:[1,135]},{4:a,5:s,8:8,9:10,12:12,13:l,14:u,16:[2,46],17:15,18:h,21:f,22:40,23:d,24:19,25:20,26:21,27:22,28:23,29:p,30:m,31:g,33:y,35:v,36:x,37:b,38:w,39:S,41:T,43:E,44:_,46:A,49:[1,136],50:L,52:M,53:N,54:k,59:I,60:C,61:O,62:D,70:P},{16:[1,137]},{4:a,5:s,8:8,9:10,12:12,13:l,14:u,16:[2,44],17:15,18:h,21:f,22:40,23:d,24:19,25:20,26:21,27:22,28:23,29:p,30:m,31:g,33:y,35:v,36:x,37:b,38:w,39:S,41:T,43:E,44:_,46:A,48:[1,138],50:L,52:M,53:N,54:k,59:I,60:C,61:O,62:D,70:P},{16:[1,139]},{16:[1,140]},{4:a,5:s,8:8,9:10,12:12,13:l,14:u,16:[2,42],17:15,18:h,21:f,22:40,23:d,24:19,25:20,26:21,27:22,28:23,29:p,30:m,31:g,33:y,35:v,36:x,37:b,38:w,39:S,41:T,43:E,44:_,46:A,47:[1,141],50:L,52:M,53:N,54:k,59:I,60:C,61:O,62:D,70:P},{4:a,5:s,8:8,9:10,12:12,13:l,14:u,16:[1,142],17:15,18:h,21:f,22:40,23:d,24:19,25:20,26:21,27:22,28:23,29:p,30:m,31:g,33:y,35:v,36:x,37:b,38:w,39:S,41:T,43:E,44:_,46:A,50:L,52:M,53:N,54:k,59:I,60:C,61:O,62:D,70:P},{15:[1,143]},t(F,[2,49]),{15:[1,144]},t(F,[2,51]),t(F,[2,52]),{22:145,70:P},{22:146,70:P},{56:147,81:ie},{56:148,81:ie},{56:149,81:ie},{64:[1,150],81:[2,62]},{5:[2,55]},{5:[2,79]},{5:[2,56]},{5:[2,57]},{5:[2,58]},t(F,[2,16]),t(B,[2,10]),{12:151,50:L,52:M,53:N},t(B,[2,12]),t(B,[2,13]),t(F,[2,18]),t(F,[2,34]),t(F,[2,35]),t(F,[2,36]),t(F,[2,37]),{15:[1,152]},t(F,[2,38]),{15:[1,153]},t(F,[2,39]),t(F,[2,40]),{15:[1,154]},t(F,[2,41]),{5:[1,155]},{5:[1,156]},{56:157,81:ie},{56:158,81:ie},{5:[2,67]},{5:[2,53]},{5:[2,54]},{22:159,70:P},t(B,[2,11]),t(z,i,{7:103,40:160}),t(Y,i,{7:105,42:161}),t(Q,i,{7:108,45:162}),t(F,[2,48]),t(F,[2,50]),{5:[2,65]},{5:[2,66]},{81:[2,61]},{16:[2,47]},{16:[2,45]},{16:[2,43]}],defaultActions:{5:[2,1],6:[2,2],87:[2,63],88:[2,64],121:[2,55],122:[2,79],123:[2,56],124:[2,57],125:[2,58],147:[2,67],148:[2,53],149:[2,54],157:[2,65],158:[2,66],159:[2,61],160:[2,47],161:[2,45],162:[2,43]},parseError:o(function(q,K){if(K.recoverable)this.trace(q);else{var se=new Error(q);throw se.hash=K,se}},"parseError"),parse:o(function(q){var K=this,se=[0],ce=[],ue=[null],te=[],De=this.table,oe="",ke=0,Ie=0,Se=0,Ue=2,Pe=1,_e=te.slice.call(arguments,1),me=Object.create(this.lexer),W={yy:{}};for(var fe in this.yy)Object.prototype.hasOwnProperty.call(this.yy,fe)&&(W.yy[fe]=this.yy[fe]);me.setInput(q,W.yy),W.yy.lexer=me,W.yy.parser=this,typeof me.yylloc>"u"&&(me.yylloc={});var ge=me.yylloc;te.push(ge);var re=me.options&&me.options.ranges;typeof W.yy.parseError=="function"?this.parseError=W.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;function he(yt){se.length=se.length-2*yt,ue.length=ue.length-yt,te.length=te.length-yt}o(he,"popStack");function ne(){var yt;return yt=ce.pop()||me.lex()||Pe,typeof yt!="number"&&(yt instanceof Array&&(ce=yt,yt=ce.pop()),yt=K.symbols_[yt]||yt),yt}o(ne,"lex");for(var ae,we,Te,Ce,Ae,Ge,Me={},ye,He,ze,Ze;;){if(Te=se[se.length-1],this.defaultActions[Te]?Ce=this.defaultActions[Te]:((ae===null||typeof ae>"u")&&(ae=ne()),Ce=De[Te]&&De[Te][ae]),typeof Ce>"u"||!Ce.length||!Ce[0]){var gt="";Ze=[];for(ye in De[Te])this.terminals_[ye]&&ye>Ue&&Ze.push("'"+this.terminals_[ye]+"'");me.showPosition?gt="Parse error on line "+(ke+1)+`: -`+me.showPosition()+` -Expecting `+Ze.join(", ")+", got '"+(this.terminals_[ae]||ae)+"'":gt="Parse error on line "+(ke+1)+": Unexpected "+(ae==Pe?"end of input":"'"+(this.terminals_[ae]||ae)+"'"),this.parseError(gt,{text:me.match,token:this.terminals_[ae]||ae,line:me.yylineno,loc:ge,expected:Ze})}if(Ce[0]instanceof Array&&Ce.length>1)throw new Error("Parse Error: multiple actions possible at state: "+Te+", token: "+ae);switch(Ce[0]){case 1:se.push(ae),ue.push(me.yytext),te.push(me.yylloc),se.push(Ce[1]),ae=null,we?(ae=we,we=null):(Ie=me.yyleng,oe=me.yytext,ke=me.yylineno,ge=me.yylloc,Se>0&&Se--);break;case 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this.yy=K||this.yy||{},this._input=q,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},"setInput"),input:o(function(){var q=this._input[0];this.yytext+=q,this.yyleng++,this.offset++,this.match+=q,this.matched+=q;var K=q.match(/(?:\r\n?|\n).*/g);return K?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),q},"input"),unput:o(function(q){var K=q.length,se=q.split(/(?:\r\n?|\n)/g);this._input=q+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-K),this.offset-=K;var ce=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),se.length-1&&(this.yylineno-=se.length-1);var ue=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:se?(se.length===ce.length?this.yylloc.first_column:0)+ce[ce.length-se.length].length-se[0].length:this.yylloc.first_column-K},this.options.ranges&&(this.yylloc.range=[ue[0],ue[0]+this.yyleng-K]),this.yyleng=this.yytext.length,this},"unput"),more:o(function(){return this._more=!0,this},"more"),reject:o(function(){if(this.options.backtrack_lexer)this._backtrack=!0;else return this.parseError("Lexical error on line "+(this.yylineno+1)+`. You can only invoke reject() in the lexer when the lexer is of the backtracking persuasion (options.backtrack_lexer = true). -`+this.showPosition(),{text:"",token:null,line:this.yylineno});return this},"reject"),less:o(function(q){this.unput(this.match.slice(q))},"less"),pastInput:o(function(){var q=this.matched.substr(0,this.matched.length-this.match.length);return(q.length>20?"...":"")+q.substr(-20).replace(/\n/g,"")},"pastInput"),upcomingInput:o(function(){var q=this.match;return q.length<20&&(q+=this._input.substr(0,20-q.length)),(q.substr(0,20)+(q.length>20?"...":"")).replace(/\n/g,"")},"upcomingInput"),showPosition:o(function(){var q=this.pastInput(),K=new Array(q.length+1).join("-");return q+this.upcomingInput()+` -`+K+"^"},"showPosition"),test_match:o(function(q,K){var se,ce,ue;if(this.options.backtrack_lexer&&(ue={yylineno:this.yylineno,yylloc:{first_line:this.yylloc.first_line,last_line:this.last_line,first_column:this.yylloc.first_column,last_column:this.yylloc.last_column},yytext:this.yytext,match:this.match,matches:this.matches,matched:this.matched,yyleng:this.yyleng,offset:this.offset,_more:this._more,_input:this._input,yy:this.yy,conditionStack:this.conditionStack.slice(0),done:this.done},this.options.ranges&&(ue.yylloc.range=this.yylloc.range.slice(0))),ce=q[0].match(/(?:\r\n?|\n).*/g),ce&&(this.yylineno+=ce.length),this.yylloc={first_line:this.yylloc.last_line,last_line:this.yylineno+1,first_column:this.yylloc.last_column,last_column:ce?ce[ce.length-1].length-ce[ce.length-1].match(/\r?\n?/)[0].length:this.yylloc.last_column+q[0].length},this.yytext+=q[0],this.match+=q[0],this.matches=q,this.yyleng=this.yytext.length,this.options.ranges&&(this.yylloc.range=[this.offset,this.offset+=this.yyleng]),this._more=!1,this._backtrack=!1,this._input=this._input.slice(q[0].length),this.matched+=q[0],se=this.performAction.call(this,this.yy,this,K,this.conditionStack[this.conditionStack.length-1]),this.done&&this._input&&(this.done=!1),se)return se;if(this._backtrack){for(var te in ue)this[te]=ue[te];return!1}return!1},"test_match"),next:o(function(){if(this.done)return this.EOF;this._input||(this.done=!0);var q,K,se,ce;this._more||(this.yytext="",this.match="");for(var ue=this._currentRules(),te=0;teK[0].length)){if(K=se,ce=te,this.options.backtrack_lexer){if(q=this.test_match(se,ue[te]),q!==!1)return q;if(this._backtrack){K=!1;continue}else return!1}else if(!this.options.flex)break}return K?(q=this.test_match(K,ue[ce]),q!==!1?q:!1):this._input===""?this.EOF:this.parseError("Lexical error on line "+(this.yylineno+1)+`. Unrecognized text. -`+this.showPosition(),{text:"",token:null,line:this.yylineno})},"next"),lex:o(function(){var K=this.next();return K||this.lex()},"lex"),begin:o(function(K){this.conditionStack.push(K)},"begin"),popState:o(function(){var K=this.conditionStack.length-1;return K>0?this.conditionStack.pop():this.conditionStack[0]},"popState"),_currentRules:o(function(){return this.conditionStack.length&&this.conditionStack[this.conditionStack.length-1]?this.conditions[this.conditionStack[this.conditionStack.length-1]].rules:this.conditions.INITIAL.rules},"_currentRules"),topState:o(function(K){return K=this.conditionStack.length-1-Math.abs(K||0),K>=0?this.conditionStack[K]:"INITIAL"},"topState"),pushState:o(function(K){this.begin(K)},"pushState"),stateStackSize:o(function(){return this.conditionStack.length},"stateStackSize"),options:{"case-insensitive":!0},performAction:o(function(K,se,ce,ue){var te=ue;switch(ce){case 0:return 5;case 1:break;case 2:break;case 3:break;case 4:break;case 5:break;case 6:return 19;case 7:return this.begin("LINE"),14;break;case 8:return this.begin("ID"),50;break;case 9:return this.begin("ID"),52;break;case 10:return 13;case 11:return this.begin("ID"),53;break;case 12:return se.yytext=se.yytext.trim(),this.begin("ALIAS"),70;break;case 13:return this.popState(),this.popState(),this.begin("LINE"),51;break;case 14:return this.popState(),this.popState(),5;break;case 15:return this.begin("LINE"),36;break;case 16:return this.begin("LINE"),37;break;case 17:return this.begin("LINE"),38;break;case 18:return this.begin("LINE"),39;break;case 19:return this.begin("LINE"),49;break;case 20:return this.begin("LINE"),41;break;case 21:return this.begin("LINE"),43;break;case 22:return this.begin("LINE"),48;break;case 23:return this.begin("LINE"),44;break;case 24:return this.begin("LINE"),47;break;case 25:return this.begin("LINE"),46;break;case 26:return this.popState(),15;break;case 27:return 16;case 28:return 65;case 29:return 66;case 30:return 59;case 31:return 60;case 32:return 61;case 33:return 62;case 34:return 57;case 35:return 54;case 36:return this.begin("ID"),21;break;case 37:return this.begin("ID"),23;break;case 38:return 29;case 39:return 30;case 40:return this.begin("acc_title"),31;break;case 41:return this.popState(),"acc_title_value";break;case 42:return this.begin("acc_descr"),33;break;case 43:return this.popState(),"acc_descr_value";break;case 44:this.begin("acc_descr_multiline");break;case 45:this.popState();break;case 46:return"acc_descr_multiline_value";case 47:return 6;case 48:return 18;case 49:return 20;case 50:return 64;case 51:return 5;case 52:return se.yytext=se.yytext.trim(),70;break;case 53:return 73;case 54:return 74;case 55:return 75;case 56:return 76;case 57:return 71;case 58:return 72;case 59:return 77;case 60:return 78;case 61:return 79;case 62:return 80;case 63:return 81;case 64:return 68;case 65:return 69;case 66:return 5;case 67:return"INVALID"}},"anonymous"),rules:[/^(?:[\n]+)/i,/^(?:\s+)/i,/^(?:((?!\n)\s)+)/i,/^(?:#[^\n]*)/i,/^(?:%(?!\{)[^\n]*)/i,/^(?:[^\}]%%[^\n]*)/i,/^(?:[0-9]+(?=[ \n]+))/i,/^(?:box\b)/i,/^(?:participant\b)/i,/^(?:actor\b)/i,/^(?:create\b)/i,/^(?:destroy\b)/i,/^(?:[^\<->\->:\n,;]+?([\-]*[^\<->\->:\n,;]+?)*?(?=((?!\n)\s)+as(?!\n)\s|[#\n;]|$))/i,/^(?:as\b)/i,/^(?:(?:))/i,/^(?:loop\b)/i,/^(?:rect\b)/i,/^(?:opt\b)/i,/^(?:alt\b)/i,/^(?:else\b)/i,/^(?:par\b)/i,/^(?:par_over\b)/i,/^(?:and\b)/i,/^(?:critical\b)/i,/^(?:option\b)/i,/^(?:break\b)/i,/^(?:(?:[:]?(?:no)?wrap)?[^#\n;]*)/i,/^(?:end\b)/i,/^(?:left of\b)/i,/^(?:right of\b)/i,/^(?:links\b)/i,/^(?:link\b)/i,/^(?:properties\b)/i,/^(?:details\b)/i,/^(?:over\b)/i,/^(?:note\b)/i,/^(?:activate\b)/i,/^(?:deactivate\b)/i,/^(?:title\s[^#\n;]+)/i,/^(?:title:\s[^#\n;]+)/i,/^(?:accTitle\s*:\s*)/i,/^(?:(?!\n||)*[^\n]*)/i,/^(?:accDescr\s*:\s*)/i,/^(?:(?!\n||)*[^\n]*)/i,/^(?:accDescr\s*\{\s*)/i,/^(?:[\}])/i,/^(?:[^\}]*)/i,/^(?:sequenceDiagram\b)/i,/^(?:autonumber\b)/i,/^(?:off\b)/i,/^(?:,)/i,/^(?:;)/i,/^(?:[^\+\<->\->:\n,;]+((?!(-x|--x|-\)|--\)))[\-]*[^\+\<->\->:\n,;]+)*)/i,/^(?:->>)/i,/^(?:<<->>)/i,/^(?:-->>)/i,/^(?:<<-->>)/i,/^(?:->)/i,/^(?:-->)/i,/^(?:-[x])/i,/^(?:--[x])/i,/^(?:-[\)])/i,/^(?:--[\)])/i,/^(?::(?:(?:no)?wrap)?[^#\n;]+)/i,/^(?:\+)/i,/^(?:-)/i,/^(?:$)/i,/^(?:.)/i],conditions:{acc_descr_multiline:{rules:[45,46],inclusive:!1},acc_descr:{rules:[43],inclusive:!1},acc_title:{rules:[41],inclusive:!1},ID:{rules:[2,3,12],inclusive:!1},ALIAS:{rules:[2,3,13,14],inclusive:!1},LINE:{rules:[2,3,26],inclusive:!1},INITIAL:{rules:[0,1,3,4,5,6,7,8,9,10,11,15,16,17,18,19,20,21,22,23,24,25,27,28,29,30,31,32,33,34,35,36,37,38,39,40,42,44,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67],inclusive:!0}}};return H}();j.lexer=J;function Z(){this.yy={}}return o(Z,"Parser"),Z.prototype=j,j.Parser=Z,new Z}();hO.parser=hO;Wue=hO});function dO(t,e){if(t.links==null)t.links=e;else for(let r in e)t.links[r]=e[r]}function Zue(t,e){if(t.properties==null)t.properties=e;else for(let r in e)t.properties[r]=e[r]}function SGe(){Mt.records.currentBox=void 0}var Mt,aGe,fO,sGe,oGe,pi,lGe,cGe,uGe,hGe,fGe,dGe,pGe,xx,mGe,gGe,yGe,vGe,xGe,Xue,A0,bGe,wGe,TGe,vx,kGe,EGe,jue,Kue,CGe,Que,Jue,AGe,ehe,pO,the=R(()=>{"use strict";_t();ut();Jk();rr();bi();Mt=new uf(()=>({prevActor:void 0,actors:new Map,createdActors:new Map,destroyedActors:new Map,boxes:[],messages:[],notes:[],sequenceNumbersEnabled:!1,wrapEnabled:void 0,currentBox:void 0,lastCreated:void 0,lastDestroyed:void 0})),aGe=o(function(t){Mt.records.boxes.push({name:t.text,wrap:t.wrap??A0(),fill:t.color,actorKeys:[]}),Mt.records.currentBox=Mt.records.boxes.slice(-1)[0]},"addBox"),fO=o(function(t,e,r,n){let i=Mt.records.currentBox,a=Mt.records.actors.get(t);if(a){if(Mt.records.currentBox&&a.box&&Mt.records.currentBox!==a.box)throw new Error(`A same participant should only be defined in one Box: ${a.name} can't be in '${a.box.name}' and in '${Mt.records.currentBox.name}' at the same time.`);if(i=a.box?a.box:Mt.records.currentBox,a.box=i,a&&e===a.name&&r==null)return}if(r?.text==null&&(r={text:e,type:n}),(n==null||r.text==null)&&(r={text:e,type:n}),Mt.records.actors.set(t,{box:i,name:e,description:r.text,wrap:r.wrap??A0(),prevActor:Mt.records.prevActor,links:{},properties:{},actorCnt:null,rectData:null,type:n??"participant"}),Mt.records.prevActor){let s=Mt.records.actors.get(Mt.records.prevActor);s&&(s.nextActor=t)}Mt.records.currentBox&&Mt.records.currentBox.actorKeys.push(t),Mt.records.prevActor=t},"addActor"),sGe=o(t=>{let e,r=0;if(!t)return 0;for(e=0;e>-",token:"->>-",line:"1",loc:{first_line:1,last_line:1,first_column:1,last_column:1},expected:["'ACTIVE_PARTICIPANT'"]},s}return Mt.records.messages.push({from:t,to:e,message:r?.text??"",wrap:r?.wrap??A0(),type:n,activate:i}),!0},"addSignal"),lGe=o(function(){return Mt.records.boxes.length>0},"hasAtLeastOneBox"),cGe=o(function(){return Mt.records.boxes.some(t=>t.name)},"hasAtLeastOneBoxWithTitle"),uGe=o(function(){return Mt.records.messages},"getMessages"),hGe=o(function(){return Mt.records.boxes},"getBoxes"),fGe=o(function(){return Mt.records.actors},"getActors"),dGe=o(function(){return Mt.records.createdActors},"getCreatedActors"),pGe=o(function(){return Mt.records.destroyedActors},"getDestroyedActors"),xx=o(function(t){return Mt.records.actors.get(t)},"getActor"),mGe=o(function(){return[...Mt.records.actors.keys()]},"getActorKeys"),gGe=o(function(){Mt.records.sequenceNumbersEnabled=!0},"enableSequenceNumbers"),yGe=o(function(){Mt.records.sequenceNumbersEnabled=!1},"disableSequenceNumbers"),vGe=o(()=>Mt.records.sequenceNumbersEnabled,"showSequenceNumbers"),xGe=o(function(t){Mt.records.wrapEnabled=t},"setWrap"),Xue=o(t=>{if(t===void 0)return{};t=t.trim();let e=/^:?wrap:/.exec(t)!==null?!0:/^:?nowrap:/.exec(t)!==null?!1:void 0;return{cleanedText:(e===void 0?t:t.replace(/^:?(?:no)?wrap:/,"")).trim(),wrap:e}},"extractWrap"),A0=o(()=>Mt.records.wrapEnabled!==void 0?Mt.records.wrapEnabled:de().sequence?.wrap??!1,"autoWrap"),bGe=o(function(){Mt.reset(),vr()},"clear"),wGe=o(function(t){let e=t.trim(),{wrap:r,cleanedText:n}=Xue(e),i={text:n,wrap:r};return V.debug(`parseMessage: ${JSON.stringify(i)}`),i},"parseMessage"),TGe=o(function(t){let e=/^((?:rgba?|hsla?)\s*\(.*\)|\w*)(.*)$/.exec(t),r=e?.[1]?e[1].trim():"transparent",n=e?.[2]?e[2].trim():void 0;if(window?.CSS)window.CSS.supports("color",r)||(r="transparent",n=t.trim());else{let s=new Option().style;s.color=r,s.color!==r&&(r="transparent",n=t.trim())}let{wrap:i,cleanedText:a}=Xue(n);return{text:a?qr(a,de()):void 0,color:r,wrap:i}},"parseBoxData"),vx={SOLID:0,DOTTED:1,NOTE:2,SOLID_CROSS:3,DOTTED_CROSS:4,SOLID_OPEN:5,DOTTED_OPEN:6,LOOP_START:10,LOOP_END:11,ALT_START:12,ALT_ELSE:13,ALT_END:14,OPT_START:15,OPT_END:16,ACTIVE_START:17,ACTIVE_END:18,PAR_START:19,PAR_AND:20,PAR_END:21,RECT_START:22,RECT_END:23,SOLID_POINT:24,DOTTED_POINT:25,AUTONUMBER:26,CRITICAL_START:27,CRITICAL_OPTION:28,CRITICAL_END:29,BREAK_START:30,BREAK_END:31,PAR_OVER_START:32,BIDIRECTIONAL_SOLID:33,BIDIRECTIONAL_DOTTED:34},kGe={FILLED:0,OPEN:1},EGe={LEFTOF:0,RIGHTOF:1,OVER:2},jue=o(function(t,e,r){let n={actor:t,placement:e,message:r.text,wrap:r.wrap??A0()},i=[].concat(t,t);Mt.records.notes.push(n),Mt.records.messages.push({from:i[0],to:i[1],message:r.text,wrap:r.wrap??A0(),type:vx.NOTE,placement:e})},"addNote"),Kue=o(function(t,e){let r=xx(t);try{let n=qr(e.text,de());n=n.replace(/&/g,"&"),n=n.replace(/=/g,"=");let i=JSON.parse(n);dO(r,i)}catch(n){V.error("error while parsing actor link text",n)}},"addLinks"),CGe=o(function(t,e){let r=xx(t);try{let n={},i=qr(e.text,de()),a=i.indexOf("@");i=i.replace(/&/g,"&"),i=i.replace(/=/g,"=");let s=i.slice(0,a-1).trim(),l=i.slice(a+1).trim();n[s]=l,dO(r,n)}catch(n){V.error("error while parsing actor link text",n)}},"addALink");o(dO,"insertLinks");Que=o(function(t,e){let r=xx(t);try{let n=qr(e.text,de()),i=JSON.parse(n);Zue(r,i)}catch(n){V.error("error while parsing actor properties text",n)}},"addProperties");o(Zue,"insertProperties");o(SGe,"boxEnd");Jue=o(function(t,e){let r=xx(t),n=document.getElementById(e.text);try{let i=n.innerHTML,a=JSON.parse(i);a.properties&&Zue(r,a.properties),a.links&&dO(r,a.links)}catch(i){V.error("error while parsing actor details text",i)}},"addDetails"),AGe=o(function(t,e){if(t?.properties!==void 0)return t.properties[e]},"getActorProperty"),ehe=o(function(t){if(Array.isArray(t))t.forEach(function(e){ehe(e)});else switch(t.type){case"sequenceIndex":Mt.records.messages.push({from:void 0,to:void 0,message:{start:t.sequenceIndex,step:t.sequenceIndexStep,visible:t.sequenceVisible},wrap:!1,type:t.signalType});break;case"addParticipant":fO(t.actor,t.actor,t.description,t.draw);break;case"createParticipant":if(Mt.records.actors.has(t.actor))throw new Error("It is not possible to have actors with the same id, even if one is destroyed before the next is created. Use 'AS' aliases to simulate the behavior");Mt.records.lastCreated=t.actor,fO(t.actor,t.actor,t.description,t.draw),Mt.records.createdActors.set(t.actor,Mt.records.messages.length);break;case"destroyParticipant":Mt.records.lastDestroyed=t.actor,Mt.records.destroyedActors.set(t.actor,Mt.records.messages.length);break;case"activeStart":pi(t.actor,void 0,void 0,t.signalType);break;case"activeEnd":pi(t.actor,void 0,void 0,t.signalType);break;case"addNote":jue(t.actor,t.placement,t.text);break;case"addLinks":Kue(t.actor,t.text);break;case"addALink":CGe(t.actor,t.text);break;case"addProperties":Que(t.actor,t.text);break;case"addDetails":Jue(t.actor,t.text);break;case"addMessage":if(Mt.records.lastCreated){if(t.to!==Mt.records.lastCreated)throw new Error("The created participant "+Mt.records.lastCreated.name+" does not have an associated creating message after its declaration. Please check the sequence diagram.");Mt.records.lastCreated=void 0}else if(Mt.records.lastDestroyed){if(t.to!==Mt.records.lastDestroyed&&t.from!==Mt.records.lastDestroyed)throw new Error("The destroyed participant "+Mt.records.lastDestroyed.name+" does not have an associated destroying message after its declaration. Please check the sequence diagram.");Mt.records.lastDestroyed=void 0}pi(t.from,t.to,t.msg,t.signalType,t.activate);break;case"boxStart":aGe(t.boxData);break;case"boxEnd":SGe();break;case"loopStart":pi(void 0,void 0,t.loopText,t.signalType);break;case"loopEnd":pi(void 0,void 0,void 0,t.signalType);break;case"rectStart":pi(void 0,void 0,t.color,t.signalType);break;case"rectEnd":pi(void 0,void 0,void 0,t.signalType);break;case"optStart":pi(void 0,void 0,t.optText,t.signalType);break;case"optEnd":pi(void 0,void 0,void 0,t.signalType);break;case"altStart":pi(void 0,void 0,t.altText,t.signalType);break;case"else":pi(void 0,void 0,t.altText,t.signalType);break;case"altEnd":pi(void 0,void 0,void 0,t.signalType);break;case"setAccTitle":kr(t.text);break;case"parStart":pi(void 0,void 0,t.parText,t.signalType);break;case"and":pi(void 0,void 0,t.parText,t.signalType);break;case"parEnd":pi(void 0,void 0,void 0,t.signalType);break;case"criticalStart":pi(void 0,void 0,t.criticalText,t.signalType);break;case"option":pi(void 0,void 0,t.optionText,t.signalType);break;case"criticalEnd":pi(void 0,void 0,void 0,t.signalType);break;case"breakStart":pi(void 0,void 0,t.breakText,t.signalType);break;case"breakEnd":pi(void 0,void 0,void 0,t.signalType);break}},"apply"),pO={addActor:fO,addMessage:oGe,addSignal:pi,addLinks:Kue,addDetails:Jue,addProperties:Que,autoWrap:A0,setWrap:xGe,enableSequenceNumbers:gGe,disableSequenceNumbers:yGe,showSequenceNumbers:vGe,getMessages:uGe,getActors:fGe,getCreatedActors:dGe,getDestroyedActors:pGe,getActor:xx,getActorKeys:mGe,getActorProperty:AGe,getAccTitle:Ar,getBoxes:hGe,getDiagramTitle:Xr,setDiagramTitle:nn,getConfig:o(()=>de().sequence,"getConfig"),clear:bGe,parseMessage:wGe,parseBoxData:TGe,LINETYPE:vx,ARROWTYPE:kGe,PLACEMENT:EGe,addNote:jue,setAccTitle:kr,apply:ehe,setAccDescription:_r,getAccDescription:Lr,hasAtLeastOneBox:lGe,hasAtLeastOneBoxWithTitle:cGe}});var _Ge,rhe,nhe=R(()=>{"use strict";_Ge=o(t=>`.actor { - stroke: ${t.actorBorder}; - fill: ${t.actorBkg}; - } - - text.actor > tspan { - fill: ${t.actorTextColor}; - stroke: none; - } - - .actor-line { - stroke: ${t.actorLineColor}; - } - - .messageLine0 { - stroke-width: 1.5; - stroke-dasharray: none; - stroke: ${t.signalColor}; - } - - .messageLine1 { - stroke-width: 1.5; - stroke-dasharray: 2, 2; - stroke: ${t.signalColor}; - } - - #arrowhead path { - fill: ${t.signalColor}; - stroke: ${t.signalColor}; - } - - .sequenceNumber { - fill: ${t.sequenceNumberColor}; - } - - #sequencenumber { - fill: ${t.signalColor}; - } - - #crosshead path { - fill: ${t.signalColor}; - stroke: ${t.signalColor}; - } - - .messageText { - fill: ${t.signalTextColor}; - stroke: none; - } - - .labelBox { - stroke: ${t.labelBoxBorderColor}; - fill: ${t.labelBoxBkgColor}; - } - - .labelText, .labelText > tspan { - fill: ${t.labelTextColor}; - stroke: none; - } - - .loopText, .loopText > tspan { - fill: ${t.loopTextColor}; - stroke: none; - } - - .loopLine { - stroke-width: 2px; - stroke-dasharray: 2, 2; 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function(t,e,r,n){let{startx:i,stopx:a,starty:s,message:l,type:u,sequenceIndex:h,sequenceVisible:f}=e,d=Lt.calculateTextDimensions(l,L0(Ne)),p=Ky();p.x=i,p.y=s+10,p.width=a-i,p.class="messageText",p.dy="1em",p.text=l,p.fontFamily=Ne.messageFontFamily,p.fontSize=Ne.messageFontSize,p.fontWeight=Ne.messageFontWeight,p.anchor=Ne.messageAlign,p.valign="center",p.textMargin=Ne.wrapPadding,p.tspan=!1,Ni(p.text)?await bx(t,p,{startx:i,stopx:a,starty:r}):_0(t,p);let m=d.width,g;i===a?Ne.rightAngles?g=t.append("path").attr("d",`M ${i},${r} H ${i+We.getMax(Ne.width/2,m/2)} V ${r+25} H ${i}`):g=t.append("path").attr("d","M "+i+","+r+" C "+(i+60)+","+(r-10)+" "+(i+60)+","+(r+30)+" "+i+","+(r+20)):(g=t.append("line"),g.attr("x1",i),g.attr("y1",r),g.attr("x2",a),g.attr("y2",r)),u===n.db.LINETYPE.DOTTED||u===n.db.LINETYPE.DOTTED_CROSS||u===n.db.LINETYPE.DOTTED_POINT||u===n.db.LINETYPE.DOTTED_OPEN||u===n.db.LINETYPE.BIDIRECTIONAL_DOTTED?(g.style("stroke-dasharray","3, 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y="";Ne.arrowMarkerAbsolute&&(y=window.location.protocol+"//"+window.location.host+window.location.pathname+window.location.search,y=y.replace(/\(/g,"\\("),y=y.replace(/\)/g,"\\)")),g.attr("stroke-width",2),g.attr("stroke","none"),g.style("fill","none"),(u===n.db.LINETYPE.SOLID||u===n.db.LINETYPE.DOTTED)&&g.attr("marker-end","url("+y+"#arrowhead)"),(u===n.db.LINETYPE.BIDIRECTIONAL_SOLID||u===n.db.LINETYPE.BIDIRECTIONAL_DOTTED)&&(g.attr("marker-start","url("+y+"#arrowhead)"),g.attr("marker-end","url("+y+"#arrowhead)")),(u===n.db.LINETYPE.SOLID_POINT||u===n.db.LINETYPE.DOTTED_POINT)&&g.attr("marker-end","url("+y+"#filled-head)"),(u===n.db.LINETYPE.SOLID_CROSS||u===n.db.LINETYPE.DOTTED_CROSS)&&g.attr("marker-end","url("+y+"#crosshead)"),(f||Ne.showSequenceNumbers)&&(g.attr("marker-start","url("+y+"#sequencenumber)"),t.append("text").attr("x",i).attr("y",r+4).attr("font-family","sans-serif").attr("font-size","12px").attr("text-anchor","middle").attr("class","sequenceNumber").text(h))},"drawMessage"),QGe=o(function(t,e,r,n,i,a,s){let l=0,u=0,h,f=0;for(let d of n){let p=e.get(d),m=p.box;h&&h!=m&&(s||Ke.models.addBox(h),u+=Ne.boxMargin+h.margin),m&&m!=h&&(s||(m.x=l+u,m.y=i),u+=m.margin),p.width=p.width||Ne.width,p.height=We.getMax(p.height||Ne.height,Ne.height),p.margin=p.margin||Ne.actorMargin,f=We.getMax(f,p.height),r.get(p.name)&&(u+=p.width/2),p.x=l+u,p.starty=Ke.getVerticalPos(),Ke.insert(p.x,i,p.x+p.width,p.height),l+=p.width+u,p.box&&(p.box.width=l+m.margin-p.box.x),u=p.margin,h=p.box,Ke.models.addActor(p)}h&&!s&&Ke.models.addBox(h),Ke.bumpVerticalPos(f)},"addActorRenderingData"),bO=o(async function(t,e,r,n){if(n){let i=0;Ke.bumpVerticalPos(Ne.boxMargin*2);for(let a of r){let s=e.get(a);s.stopy||(s.stopy=Ke.getVerticalPos());let l=await si.drawActor(t,s,Ne,!0);i=We.getMax(i,l)}Ke.bumpVerticalPos(i+Ne.boxMargin)}else for(let i of r){let a=e.get(i);await si.drawActor(t,a,Ne,!1)}},"drawActors"),fhe=o(function(t,e,r,n){let i=0,a=0;for(let s of r){let l=e.get(s),u=t$e(l),h=si.drawPopup(t,l,u,Ne,Ne.forceMenus,n);h.height>i&&(i=h.height),h.width+l.x>a&&(a=h.width+l.x)}return{maxHeight:i,maxWidth:a}},"drawActorsPopup"),dhe=o(function(t){On(Ne,t),t.fontFamily&&(Ne.actorFontFamily=Ne.noteFontFamily=Ne.messageFontFamily=t.fontFamily),t.fontSize&&(Ne.actorFontSize=Ne.noteFontSize=Ne.messageFontSize=t.fontSize),t.fontWeight&&(Ne.actorFontWeight=Ne.noteFontWeight=Ne.messageFontWeight=t.fontWeight)},"setConf"),bE=o(function(t){return Ke.activations.filter(function(e){return e.actor===t})},"actorActivations"),hhe=o(function(t,e){let r=e.get(t),n=bE(t),i=n.reduce(function(s,l){return We.getMin(s,l.startx)},r.x+r.width/2-1),a=n.reduce(function(s,l){return We.getMax(s,l.stopx)},r.x+r.width/2+1);return[i,a]},"activationBounds");o(Fc,"adjustLoopHeightForWrap");o(ZGe,"adjustCreatedDestroyedData");JGe=o(async function(t,e,r,n){let{securityLevel:i,sequence:a}=de();Ne=a;let s;i==="sandbox"&&(s=$e("#i"+e));let l=i==="sandbox"?$e(s.nodes()[0].contentDocument.body):$e("body"),u=i==="sandbox"?s.nodes()[0].contentDocument:document;Ke.init(),V.debug(n.db);let h=i==="sandbox"?l.select(`[id="${e}"]`):$e(`[id="${e}"]`),f=n.db.getActors(),d=n.db.getCreatedActors(),p=n.db.getDestroyedActors(),m=n.db.getBoxes(),g=n.db.getActorKeys(),y=n.db.getMessages(),v=n.db.getDiagramTitle(),x=n.db.hasAtLeastOneBox(),b=n.db.hasAtLeastOneBoxWithTitle(),w=await e$e(f,y,n);if(Ne.height=await r$e(f,w,m),si.insertComputerIcon(h),si.insertDatabaseIcon(h),si.insertClockIcon(h),x&&(Ke.bumpVerticalPos(Ne.boxMargin),b&&Ke.bumpVerticalPos(m[0].textMaxHeight)),Ne.hideUnusedParticipants===!0){let F=new Set;y.forEach(B=>{F.add(B.from),F.add(B.to)}),g=g.filter(B=>F.has(B))}QGe(h,f,d,g,0,y,!1);let S=await a$e(y,f,w,n);si.insertArrowHead(h),si.insertArrowCrossHead(h),si.insertArrowFilledHead(h),si.insertSequenceNumber(h);function T(F,B){let $=Ke.endActivation(F);$.starty+18>B&&($.starty=B-6,B+=12),si.drawActivation(h,$,B,Ne,bE(F.from).length),Ke.insert($.startx,B-10,$.stopx,B)}o(T,"activeEnd");let E=1,_=1,A=[],L=[],M=0;for(let F of y){let B,$,z;switch(F.type){case n.db.LINETYPE.NOTE:Ke.resetVerticalPos(),$=F.noteModel,await XGe(h,$);break;case n.db.LINETYPE.ACTIVE_START:Ke.newActivation(F,h,f);break;case n.db.LINETYPE.ACTIVE_END:T(F,Ke.getVerticalPos());break;case n.db.LINETYPE.LOOP_START:Fc(S,F,Ne.boxMargin,Ne.boxMargin+Ne.boxTextMargin,Y=>Ke.newLoop(Y));break;case n.db.LINETYPE.LOOP_END:B=Ke.endLoop(),await si.drawLoop(h,B,"loop",Ne),Ke.bumpVerticalPos(B.stopy-Ke.getVerticalPos()),Ke.models.addLoop(B);break;case n.db.LINETYPE.RECT_START:Fc(S,F,Ne.boxMargin,Ne.boxMargin,Y=>Ke.newLoop(void 0,Y.message));break;case n.db.LINETYPE.RECT_END:B=Ke.endLoop(),L.push(B),Ke.models.addLoop(B),Ke.bumpVerticalPos(B.stopy-Ke.getVerticalPos());break;case n.db.LINETYPE.OPT_START:Fc(S,F,Ne.boxMargin,Ne.boxMargin+Ne.boxTextMargin,Y=>Ke.newLoop(Y));break;case n.db.LINETYPE.OPT_END:B=Ke.endLoop(),await si.drawLoop(h,B,"opt",Ne),Ke.bumpVerticalPos(B.stopy-Ke.getVerticalPos()),Ke.models.addLoop(B);break;case n.db.LINETYPE.ALT_START:Fc(S,F,Ne.boxMargin,Ne.boxMargin+Ne.boxTextMargin,Y=>Ke.newLoop(Y));break;case n.db.LINETYPE.ALT_ELSE:Fc(S,F,Ne.boxMargin+Ne.boxTextMargin,Ne.boxMargin,Y=>Ke.addSectionToLoop(Y));break;case n.db.LINETYPE.ALT_END:B=Ke.endLoop(),await si.drawLoop(h,B,"alt",Ne),Ke.bumpVerticalPos(B.stopy-Ke.getVerticalPos()),Ke.models.addLoop(B);break;case n.db.LINETYPE.PAR_START:case n.db.LINETYPE.PAR_OVER_START:Fc(S,F,Ne.boxMargin,Ne.boxMargin+Ne.boxTextMargin,Y=>Ke.newLoop(Y)),Ke.saveVerticalPos();break;case n.db.LINETYPE.PAR_AND:Fc(S,F,Ne.boxMargin+Ne.boxTextMargin,Ne.boxMargin,Y=>Ke.addSectionToLoop(Y));break;case n.db.LINETYPE.PAR_END:B=Ke.endLoop(),await si.drawLoop(h,B,"par",Ne),Ke.bumpVerticalPos(B.stopy-Ke.getVerticalPos()),Ke.models.addLoop(B);break;case n.db.LINETYPE.AUTONUMBER:E=F.message.start||E,_=F.message.step||_,F.message.visible?n.db.enableSequenceNumbers():n.db.disableSequenceNumbers();break;case n.db.LINETYPE.CRITICAL_START:Fc(S,F,Ne.boxMargin,Ne.boxMargin+Ne.boxTextMargin,Y=>Ke.newLoop(Y));break;case n.db.LINETYPE.CRITICAL_OPTION:Fc(S,F,Ne.boxMargin+Ne.boxTextMargin,Ne.boxMargin,Y=>Ke.addSectionToLoop(Y));break;case n.db.LINETYPE.CRITICAL_END:B=Ke.endLoop(),await si.drawLoop(h,B,"critical",Ne),Ke.bumpVerticalPos(B.stopy-Ke.getVerticalPos()),Ke.models.addLoop(B);break;case n.db.LINETYPE.BREAK_START:Fc(S,F,Ne.boxMargin,Ne.boxMargin+Ne.boxTextMargin,Y=>Ke.newLoop(Y));break;case n.db.LINETYPE.BREAK_END:B=Ke.endLoop(),await si.drawLoop(h,B,"break",Ne),Ke.bumpVerticalPos(B.stopy-Ke.getVerticalPos()),Ke.models.addLoop(B);break;default:try{z=F.msgModel,z.starty=Ke.getVerticalPos(),z.sequenceIndex=E,z.sequenceVisible=n.db.showSequenceNumbers();let Y=await jGe(h,z);ZGe(F,z,Y,M,f,d,p),A.push({messageModel:z,lineStartY:Y}),Ke.models.addMessage(z)}catch(Y){V.error("error while drawing message",Y)}}[n.db.LINETYPE.SOLID_OPEN,n.db.LINETYPE.DOTTED_OPEN,n.db.LINETYPE.SOLID,n.db.LINETYPE.DOTTED,n.db.LINETYPE.SOLID_CROSS,n.db.LINETYPE.DOTTED_CROSS,n.db.LINETYPE.SOLID_POINT,n.db.LINETYPE.DOTTED_POINT,n.db.LINETYPE.BIDIRECTIONAL_SOLID,n.db.LINETYPE.BIDIRECTIONAL_DOTTED].includes(F.type)&&(E=E+_),M++}V.debug("createdActors",d),V.debug("destroyedActors",p),await bO(h,f,g,!1);for(let F of A)await KGe(h,F.messageModel,F.lineStartY,n);Ne.mirrorActors&&await bO(h,f,g,!0),L.forEach(F=>si.drawBackgroundRect(h,F)),yO(h,f,g,Ne);for(let F of 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this.yy=me||this.yy||{},this._input=_e,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},"setInput"),input:o(function(){var _e=this._input[0];this.yytext+=_e,this.yyleng++,this.offset++,this.match+=_e,this.matched+=_e;var me=_e.match(/(?:\r\n?|\n).*/g);return me?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),_e},"input"),unput:o(function(_e){var me=_e.length,W=_e.split(/(?:\r\n?|\n)/g);this._input=_e+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-me),this.offset-=me;var 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v=g.value,x=y.value,b=v[0]===x[0]&&v[1]===x[1]&&v[2]===x[2]&&(v[3]===x[3]||(v[3]==null||v[3]===1)&&(x[3]==null||x[3]===1));if(b)return!1}return{name:t,value:d,strValue:""+e,mapped:m,field:d[1],fieldMin:parseFloat(d[2]),fieldMax:parseFloat(d[3]),valueMin:g.value,valueMax:y.value,bypass:r}}}if(h.multiple&&n!=="multiple"){var w;if(u?w=e.split(/\s+/):vn(e)?w=e:w=[e],h.evenMultiple&&w.length%2!==0)return null;for(var S=[],T=[],E=[],_="",A=!1,L=0;L0?" 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i=e.boundingBox(),a=this.width(),s=this.height();r=r===void 0?this._private.zoom:r;var l={x:(a-r*(i.x1+i.x2))/2,y:(s-r*(i.y1+i.y2))/2};return l}}},"getCenterPan"),reset:o(function(){return!this._private.panningEnabled||!this._private.zoomingEnabled?this:(this.viewport({pan:{x:0,y:0},zoom:1}),this)},"reset"),invalidateSize:o(function(){this._private.sizeCache=null},"invalidateSize"),size:o(function(){var e=this._private,r=e.container,n=this;return e.sizeCache=e.sizeCache||(r?function(){var i=n.window().getComputedStyle(r),a=o(function(l){return parseFloat(i.getPropertyValue(l))},"val");return{width:r.clientWidth-a("padding-left")-a("padding-right"),height:r.clientHeight-a("padding-top")-a("padding-bottom")}}():{width:1,height:1})},"size"),width:o(function(){return this.size().width},"width"),height:o(function(){return this.size().height},"height"),extent:o(function(){var e=this._private.pan,r=this._private.zoom,n=this.renderedExtent(),i={x1:(n.x1-e.x)/r,x2:(n.x2-e.x)/r,y1:(n.y1-e.y)/r,y2:(n.y2-e.y)/r};return i.w=i.x2-i.x1,i.h=i.y2-i.y1,i},"extent"),renderedExtent:o(function(){var e=this.width(),r=this.height();return{x1:0,y1:0,x2:e,y2:r,w:e,h:r}},"renderedExtent"),multiClickDebounceTime:o(function(e){if(e)this._private.multiClickDebounceTime=e;else return this._private.multiClickDebounceTime;return this},"multiClickDebounceTime")};q0.centre=q0.center;q0.autolockNodes=q0.autolock;q0.autoungrabifyNodes=q0.autoungrabify;jx={data:en.data({field:"data",bindingEvent:"data",allowBinding:!0,allowSetting:!0,settingEvent:"data",settingTriggersEvent:!0,triggerFnName:"trigger",allowGetting:!0,updateStyle:!0}),removeData:en.removeData({field:"data",event:"data",triggerFnName:"trigger",triggerEvent:!0,updateStyle:!0}),scratch:en.data({field:"scratch",bindingEvent:"scratch",allowBinding:!0,allowSetting:!0,settingEvent:"scratch",settingTriggersEvent:!0,triggerFnName:"trigger",allowGetting:!0,updateStyle:!0}),removeScratch:en.removeData({field:"scratch",event:"scratch",triggerFnName:"trigger",triggerEvent:!0,updateStyle:!0})};jx.attr=jx.data;jx.removeAttr=jx.removeData;Kx=o(function(e){var r=this;e=Wt({},e);var n=e.container;n&&!k6(n)&&k6(n[0])&&(n=n[0]);var i=n?n._cyreg:null;i=i||{},i&&i.cy&&(i.cy.destroy(),i={});var a=i.readies=i.readies||[];n&&(n._cyreg=i),i.cy=r;var s=Vi!==void 0&&n!==void 0&&!e.headless,l=e;l.layout=Wt({name:s?"grid":"null"},l.layout),l.renderer=Wt({name:s?"canvas":"null"},l.renderer);var u=o(function(g,y,v){return y!==void 0?y:v!==void 0?v:g},"defVal"),h=this._private={container:n,ready:!1,options:l,elements:new Ca(this),listeners:[],aniEles:new Ca(this),data:l.data||{},scratch:{},layout:null,renderer:null,destroyed:!1,notificationsEnabled:!0,minZoom:1e-50,maxZoom:1e50,zoomingEnabled:u(!0,l.zoomingEnabled),userZoomingEnabled:u(!0,l.userZoomingEnabled),panningEnabled:u(!0,l.panningEnabled),userPanningEnabled:u(!0,l.userPanningEnabled),boxSelectionEnabled:u(!0,l.boxSelectionEnabled),autolock:u(!1,l.autolock,l.autolockNodes),autoungrabify:u(!1,l.autoungrabify,l.autoungrabifyNodes),autounselectify:u(!1,l.autounselectify),styleEnabled:l.styleEnabled===void 0?s:l.styleEnabled,zoom:ft(l.zoom)?l.zoom:1,pan:{x:Mr(l.pan)&&ft(l.pan.x)?l.pan.x:0,y:Mr(l.pan)&&ft(l.pan.y)?l.pan.y:0},animation:{current:[],queue:[]},hasCompoundNodes:!1,multiClickDebounceTime:u(250,l.multiClickDebounceTime)};this.createEmitter(),this.selectionType(l.selectionType),this.zoomRange({min:l.minZoom,max:l.maxZoom});var f=o(function(g,y){var v=g.some(zHe);if(v)return u1.all(g).then(y);y(g)},"loadExtData");h.styleEnabled&&r.setStyle([]);var d=Wt({},l,l.renderer);r.initRenderer(d);var p=o(function(g,y,v){r.notifications(!1);var x=r.mutableElements();x.length>0&&x.remove(),g!=null&&(Mr(g)||vn(g))&&r.add(g),r.one("layoutready",function(w){r.notifications(!0),r.emit(w),r.one("load",y),r.emitAndNotify("load")}).one("layoutstop",function(){r.one("done",v),r.emit("done")});var b=Wt({},r._private.options.layout);b.eles=r.elements(),r.layout(b).run()},"setElesAndLayout");f([l.style,l.elements],function(m){var g=m[0],y=m[1];h.styleEnabled&&r.style().append(g),p(y,function(){r.startAnimationLoop(),h.ready=!0,jn(l.ready)&&r.on("ready",l.ready);for(var v=0;v0,u=Gs(e.boundingBox?e.boundingBox:{x1:0,y1:0,w:r.width(),h:r.height()}),h;if(xo(e.roots))h=e.roots;else if(vn(e.roots)){for(var f=[],d=0;d0;){var O=C(),D=M(O,k);if(D)O.outgoers().filter(function(ue){return ue.isNode()&&n.has(ue)}).forEach(I);else if(D===null){tn("Detected double maximal shift for node `"+O.id()+"`. 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s=Gs(e.boundingBox?e.boundingBox:{x1:0,y1:0,w:r.width(),h:r.height()}),l={x:s.x1+s.w/2,y:s.y1+s.h/2},u=e.sweep===void 0?2*Math.PI-2*Math.PI/a.length:e.sweep,h=u/Math.max(1,a.length-1),f,d=0,p=0;p1&&e.avoidOverlap){d*=1.75;var x=Math.cos(h)-Math.cos(0),b=Math.sin(h)-Math.sin(0),w=Math.sqrt(d*d/(x*x+b*b));f=Math.max(w,f)}var S=o(function(E,_){var A=e.startAngle+_*h*(i?1:-1),L=f*Math.cos(A),M=f*Math.sin(A),N={x:l.x+L,y:l.y+M};return N},"getPos");return n.nodes().layoutPositions(this,e,S),this};GKe={fit:!0,padding:30,startAngle:3/2*Math.PI,sweep:void 0,clockwise:!0,equidistant:!1,minNodeSpacing:10,boundingBox:void 0,avoidOverlap:!0,nodeDimensionsIncludeLabels:!1,height:void 0,width:void 0,spacingFactor:void 0,concentric:o(function(e){return e.degree()},"concentric"),levelWidth:o(function(e){return e.maxDegree()/4},"levelWidth"),animate:!1,animationDuration:500,animationEasing:void 0,animateFilter:o(function(e,r){return!0},"animateFilter"),ready:void 0,stop:void 0,transform:o(function(e,r){return r},"transform")};o(Lme,"ConcentricLayout");Lme.prototype.run=function(){for(var t=this.options,e=t,r=e.counterclockwise!==void 0?!e.counterclockwise:e.clockwise,n=t.cy,i=e.eles,a=i.nodes().not(":parent"),s=Gs(e.boundingBox?e.boundingBox:{x1:0,y1:0,w:n.width(),h:n.height()}),l={x:s.x1+s.w/2,y:s.y1+s.h/2},u=[],h=0,f=0;f0){var T=Math.abs(b[0].value-S.value);T>=v&&(b=[],x.push(b))}b.push(S)}var E=h+e.minNodeSpacing;if(!e.avoidOverlap){var _=x.length>0&&x[0].length>1,A=Math.min(s.w,s.h)/2-E,L=A/(x.length+_?1:0);E=Math.min(E,L)}for(var M=0,N=0;N1&&e.avoidOverlap){var O=Math.cos(C)-Math.cos(0),D=Math.sin(C)-Math.sin(0),P=Math.sqrt(E*E/(O*O+D*D));M=Math.max(P,M)}k.r=M,M+=E}if(e.equidistant){for(var F=0,B=0,$=0;$=t.numIter||(XKe(n,t),n.temperature=n.temperature*t.coolingFactor,n.temperature=t.animationThreshold&&a(),E6(d)}},"frame");f()}else{for(;h;)h=s(u),u++;rpe(n,t),l()}return this};j6.prototype.stop=function(){return this.stopped=!0,this.thread&&this.thread.stop(),this.emit("layoutstop"),this};j6.prototype.destroy=function(){return this.thread&&this.thread.stop(),this};VKe=o(function(e,r,n){for(var i=n.eles.edges(),a=n.eles.nodes(),s=Gs(n.boundingBox?n.boundingBox:{x1:0,y1:0,w:e.width(),h:e.height()}),l={isCompound:e.hasCompoundNodes(),layoutNodes:[],idToIndex:{},nodeSize:a.size(),graphSet:[],indexToGraph:[],layoutEdges:[],edgeSize:i.size(),temperature:n.initialTemp,clientWidth:s.w,clientHeight:s.h,boundingBox:s},u=n.eles.components(),h={},f=0;f0){l.graphSet.push(A);for(var f=0;fi.count?0:i.graph},"findLCA"),HKe=o(function t(e,r,n,i){var a=i.graphSet[n];if(-10)var d=i.nodeOverlap*f,p=Math.sqrt(l*l+u*u),m=d*l/p,g=d*u/p;else var y=R6(e,l,u),v=R6(r,-1*l,-1*u),x=v.x-y.x,b=v.y-y.y,w=x*x+b*b,p=Math.sqrt(w),d=(e.nodeRepulsion+r.nodeRepulsion)/w,m=d*x/p,g=d*b/p;e.isLocked||(e.offsetX-=m,e.offsetY-=g),r.isLocked||(r.offsetX+=m,r.offsetY+=g)}},"nodeRepulsion"),QKe=o(function(e,r,n,i){if(n>0)var a=e.maxX-r.minX;else var a=r.maxX-e.minX;if(i>0)var s=e.maxY-r.minY;else var s=r.maxY-e.minY;return a>=0&&s>=0?Math.sqrt(a*a+s*s):0},"nodesOverlap"),R6=o(function(e,r,n){var i=e.positionX,a=e.positionY,s=e.height||1,l=e.width||1,u=n/r,h=s/l,f={};return r===0&&0n?(f.x=i,f.y=a+s/2,f):0r&&-1*h<=u&&u<=h?(f.x=i-l/2,f.y=a-l*n/2/r,f):0=h)?(f.x=i+s*r/2/n,f.y=a+s/2,f):(0>n&&(u<=-1*h||u>=h)&&(f.x=i-s*r/2/n,f.y=a-s/2),f)},"findClippingPoint"),ZKe=o(function(e,r){for(var n=0;nn){var v=r.gravity*m/y,x=r.gravity*g/y;p.offsetX+=v,p.offsetY+=x}}}}},"calculateGravityForces"),eQe=o(function(e,r){var n=[],i=0,a=-1;for(n.push.apply(n,e.graphSet[0]),a+=e.graphSet[0].length;i<=a;){var s=n[i++],l=e.idToIndex[s],u=e.layoutNodes[l],h=u.children;if(0n)var a={x:n*e/i,y:n*r/i};else var a={x:e,y:r};return a},"limitForce"),nQe=o(function t(e,r){var n=e.parentId;if(n!=null){var i=r.layoutNodes[r.idToIndex[n]],a=!1;if((i.maxX==null||e.maxX+i.padRight>i.maxX)&&(i.maxX=e.maxX+i.padRight,a=!0),(i.minX==null||e.minX-i.padLefti.maxY)&&(i.maxY=e.maxY+i.padBottom,a=!0),(i.minY==null||e.minY-i.padTopx&&(g+=v+r.componentSpacing,m=0,y=0,v=0)}}},"separateComponents"),iQe={fit:!0,padding:30,boundingBox:void 0,avoidOverlap:!0,avoidOverlapPadding:10,nodeDimensionsIncludeLabels:!1,spacingFactor:void 0,condense:!1,rows:void 0,cols:void 0,position:o(function(e){},"position"),sort:void 0,animate:!1,animationDuration:500,animationEasing:void 0,animateFilter:o(function(e,r){return!0},"animateFilter"),ready:void 0,stop:void 0,transform:o(function(e,r){return r},"transform")};o(Rme,"GridLayout");Rme.prototype.run=function(){var t=this.options,e=t,r=t.cy,n=e.eles,i=n.nodes().not(":parent");e.sort&&(i=i.sort(e.sort));var a=Gs(e.boundingBox?e.boundingBox:{x1:0,y1:0,w:r.width(),h:r.height()});if(a.h===0||a.w===0)n.nodes().layoutPositions(this,e,function(Q){return{x:a.x1,y:a.y1}});else{var s=i.size(),l=Math.sqrt(s*a.h/a.w),u=Math.round(l),h=Math.round(a.w/a.h*l),f=o(function(X){if(X==null)return Math.min(u,h);var ie=Math.min(u,h);ie==u?u=X:h=X},"small"),d=o(function(X){if(X==null)return Math.max(u,h);var ie=Math.max(u,h);ie==u?u=X:h=X},"large"),p=e.rows,m=e.cols!=null?e.cols:e.columns;if(p!=null&&m!=null)u=p,h=m;else if(p!=null&&m==null)u=p,h=Math.ceil(s/u);else if(p==null&&m!=null)h=m,u=Math.ceil(s/h);else if(h*u>s){var g=f(),y=d();(g-1)*y>=s?f(g-1):(y-1)*g>=s&&d(y-1)}else for(;h*u=s?d(x+1):f(v+1)}var b=a.w/h,w=a.h/u;if(e.condense&&(b=0,w=0),e.avoidOverlap)for(var S=0;S=h&&(O=0,C++)},"moveToNextCell"),P={},F=0;F(O=_We(t,e,D[P],D[P+1],D[P+2],D[P+3])))return v(_,O),!0}else if(L.edgeType==="bezier"||L.edgeType==="multibezier"||L.edgeType==="self"||L.edgeType==="compound"){for(var D=L.allpts,P=0;P+5(O=AWe(t,e,D[P],D[P+1],D[P+2],D[P+3],D[P+4],D[P+5])))return v(_,O),!0}for(var F=F||A.source,B=B||A.target,$=i.getArrowWidth(M,N),z=[{name:"source",x:L.arrowStartX,y:L.arrowStartY,angle:L.srcArrowAngle},{name:"target",x:L.arrowEndX,y:L.arrowEndY,angle:L.tgtArrowAngle},{name:"mid-source",x:L.midX,y:L.midY,angle:L.midsrcArrowAngle},{name:"mid-target",x:L.midX,y:L.midY,angle:L.midtgtArrowAngle}],P=0;P0&&(x(F),x(B))}o(b,"checkEdge");function w(_,A,L){return Ul(_,A,L)}o(w,"preprop");function S(_,A){var L=_._private,M=p,N;A?N=A+"-":N="",_.boundingBox();var k=L.labelBounds[A||"main"],I=_.pstyle(N+"label").value,C=_.pstyle("text-events").strValue==="yes";if(!(!C||!I)){var O=w(L.rscratch,"labelX",A),D=w(L.rscratch,"labelY",A),P=w(L.rscratch,"labelAngle",A),F=_.pstyle(N+"text-margin-x").pfValue,B=_.pstyle(N+"text-margin-y").pfValue,$=k.x1-M-F,z=k.x2+M-F,Y=k.y1-M-B,Q=k.y2+M-B;if(P){var X=Math.cos(P),ie=Math.sin(P),j=o(function(ce,ue){return ce=ce-O,ue=ue-D,{x:ce*X-ue*ie+O,y:ce*ie+ue*X+D}},"rotate"),J=j($,Y),Z=j($,Q),H=j(z,Y),q=j(z,Q),K=[J.x+F,J.y+B,H.x+F,H.y+B,q.x+F,q.y+B,Z.x+F,Z.y+B];if(zs(t,e,K))return v(_),!0}else if(s1(k,t,e))return v(_),!0}}o(S,"checkLabel");for(var T=s.length-1;T>=0;T--){var E=s[T];E.isNode()?x(E)||S(E):b(E)||S(E)||S(E,"source")||S(E,"target")}return l};j0.getAllInBox=function(t,e,r,n){var i=this.getCachedZSortedEles().interactive,a=[],s=Math.min(t,r),l=Math.max(t,r),u=Math.min(e,n),h=Math.max(e,n);t=s,r=l,e=u,n=h;for(var f=Gs({x1:t,y1:e,x2:r,y2:n}),d=0;d0?-(Math.PI-e.ang):Math.PI+e.ang},"invertVec"),uQe=o(function(e,r,n,i,a){if(e!==ope?lpe(r,e,Gc):cQe(Jo,Gc),lpe(r,n,Jo),ape=Gc.nx*Jo.ny-Gc.ny*Jo.nx,spe=Gc.nx*Jo.nx-Gc.ny*-Jo.ny,ju=Math.asin(Math.max(-1,Math.min(1,ape))),Math.abs(ju)<1e-6){GP=r.x,$P=r.y,z0=Qg=0;return}G0=1,w6=!1,spe<0?ju<0?ju=Math.PI+ju:(ju=Math.PI-ju,G0=-1,w6=!0):ju>0&&(G0=-1,w6=!0),r.radius!==void 0?Qg=r.radius:Qg=i,O0=ju/2,f6=Math.min(Gc.len/2,Jo.len/2),a?(zc=Math.abs(Math.cos(O0)*Qg/Math.sin(O0)),zc>f6?(zc=f6,z0=Math.abs(zc*Math.sin(O0)/Math.cos(O0))):z0=Qg):(zc=Math.min(f6,Qg),z0=Math.abs(zc*Math.sin(O0)/Math.cos(O0))),VP=r.x+Jo.nx*zc,UP=r.y+Jo.ny*zc,GP=VP-Jo.ny*z0*G0,$P=UP+Jo.nx*z0*G0,Ome=r.x+Gc.nx*zc,Pme=r.y+Gc.ny*zc,ope=r},"calcCornerArc");o(Bme,"drawPreparedRoundCorner");o(mB,"getRoundCorner");$a={};$a.findMidptPtsEtc=function(t,e){var r=e.posPts,n=e.intersectionPts,i=e.vectorNormInverse,a,s=t.pstyle("source-endpoint"),l=t.pstyle("target-endpoint"),u=s.units!=null&&l.units!=null,h=o(function(T,E,_,A){var L=A-E,M=_-T,N=Math.sqrt(M*M+L*L);return{x:-L/N,y:M/N}},"recalcVectorNormInverse"),f=t.pstyle("edge-distances").value;switch(f){case"node-position":a=r;break;case"intersection":a=n;break;case"endpoints":{if(u){var d=this.manualEndptToPx(t.source()[0],s),p=$l(d,2),m=p[0],g=p[1],y=this.manualEndptToPx(t.target()[0],l),v=$l(y,2),x=v[0],b=v[1],w={x1:m,y1:g,x2:x,y2:b};i=h(m,g,x,b),a=w}else tn("Edge ".concat(t.id()," has edge-distances:endpoints specified without manual endpoints specified via source-endpoint and target-endpoint. Falling back on edge-distances:intersection (default).")),a=n;break}}return{midptPts:a,vectorNormInverse:i}};$a.findHaystackPoints=function(t){for(var e=0;e0?Math.max(Te-Ce,0):Math.min(Te+Ce,0)},"subDWH"),I=k(M,A),C=k(N,L),O=!1;b===h?x=Math.abs(I)>Math.abs(C)?i:n:b===u||b===l?(x=n,O=!0):(b===a||b===s)&&(x=i,O=!0);var D=x===n,P=D?C:I,F=D?N:M,B=$pe(F),$=!1;!(O&&(S||E))&&(b===l&&F<0||b===u&&F>0||b===a&&F>0||b===s&&F<0)&&(B*=-1,P=B*Math.abs(P),$=!0);var z;if(S){var Y=T<0?1+T:T;z=Y*P}else{var Q=T<0?P:0;z=Q+T*B}var X=o(function(Te){return Math.abs(Te)<_||Math.abs(Te)>=Math.abs(P)},"getIsTooClose"),ie=X(z),j=X(Math.abs(P)-Math.abs(z)),J=ie||j;if(J&&!$)if(D){var Z=Math.abs(F)<=p/2,H=Math.abs(M)<=m/2;if(Z){var q=(f.x1+f.x2)/2,K=f.y1,se=f.y2;r.segpts=[q,K,q,se]}else if(H){var ce=(f.y1+f.y2)/2,ue=f.x1,te=f.x2;r.segpts=[ue,ce,te,ce]}else r.segpts=[f.x1,f.y2]}else{var De=Math.abs(F)<=d/2,oe=Math.abs(N)<=g/2;if(De){var ke=(f.y1+f.y2)/2,Ie=f.x1,Se=f.x2;r.segpts=[Ie,ke,Se,ke]}else if(oe){var Ue=(f.x1+f.x2)/2,Pe=f.y1,_e=f.y2;r.segpts=[Ue,Pe,Ue,_e]}else r.segpts=[f.x2,f.y1]}else if(D){var me=f.y1+z+(v?p/2*B:0),W=f.x1,fe=f.x2;r.segpts=[W,me,fe,me]}else{var ge=f.x1+z+(v?d/2*B:0),re=f.y1,he=f.y2;r.segpts=[ge,re,ge,he]}if(r.isRound){var ne=t.pstyle("taxi-radius").value,ae=t.pstyle("radius-type").value[0]==="arc-radius";r.radii=new Array(r.segpts.length/2).fill(ne),r.isArcRadius=new Array(r.segpts.length/2).fill(ae)}};$a.tryToCorrectInvalidPoints=function(t,e){var r=t._private.rscratch;if(r.edgeType==="bezier"){var n=e.srcPos,i=e.tgtPos,a=e.srcW,s=e.srcH,l=e.tgtW,u=e.tgtH,h=e.srcShape,f=e.tgtShape,d=e.srcCornerRadius,p=e.tgtCornerRadius,m=e.srcRs,g=e.tgtRs,y=!ft(r.startX)||!ft(r.startY),v=!ft(r.arrowStartX)||!ft(r.arrowStartY),x=!ft(r.endX)||!ft(r.endY),b=!ft(r.arrowEndX)||!ft(r.arrowEndY),w=3,S=this.getArrowWidth(t.pstyle("width").pfValue,t.pstyle("arrow-scale").value)*this.arrowShapeWidth,T=w*S,E=H0({x:r.ctrlpts[0],y:r.ctrlpts[1]},{x:r.startX,y:r.startY}),_=EC.poolIndex()){var O=I;I=C,C=O}var D=L.srcPos=I.position(),P=L.tgtPos=C.position(),F=L.srcW=I.outerWidth(),B=L.srcH=I.outerHeight(),$=L.tgtW=C.outerWidth(),z=L.tgtH=C.outerHeight(),Y=L.srcShape=r.nodeShapes[e.getNodeShape(I)],Q=L.tgtShape=r.nodeShapes[e.getNodeShape(C)],X=L.srcCornerRadius=I.pstyle("corner-radius").value==="auto"?"auto":I.pstyle("corner-radius").pfValue,ie=L.tgtCornerRadius=C.pstyle("corner-radius").value==="auto"?"auto":C.pstyle("corner-radius").pfValue,j=L.tgtRs=C._private.rscratch,J=L.srcRs=I._private.rscratch;L.dirCounts={north:0,west:0,south:0,east:0,northwest:0,southwest:0,northeast:0,southeast:0};for(var Z=0;Z0){var se=a,ce=B0(se,Jg(r)),ue=B0(se,Jg(K)),te=ce;if(ue2){var De=B0(se,{x:K[2],y:K[3]});De0){var he=s,ne=B0(he,Jg(r)),ae=B0(he,Jg(re)),we=ne;if(ae2){var Te=B0(he,{x:re[2],y:re[3]});Te=g||_){v={cp:S,segment:E};break}}if(v)break}var A=v.cp,L=v.segment,M=(g-x)/L.length,N=L.t1-L.t0,k=m?L.t0+N*M:L.t1-N*M;k=Hx(0,k,1),e=t1(A.p0,A.p1,A.p2,k),p=fQe(A.p0,A.p1,A.p2,k);break}case"straight":case"segments":case"haystack":{for(var I=0,C,O,D,P,F=n.allpts.length,B=0;B+3=g));B+=2);var $=g-O,z=$/C;z=Hx(0,z,1),e=yWe(D,P,z),p=Gme(D,P);break}}s("labelX",d,e.x),s("labelY",d,e.y),s("labelAutoAngle",d,p)}},"calculateEndProjection");h("source"),h("target"),this.applyLabelDimensions(t)}};Hc.applyLabelDimensions=function(t){this.applyPrefixedLabelDimensions(t),t.isEdge()&&(this.applyPrefixedLabelDimensions(t,"source"),this.applyPrefixedLabelDimensions(t,"target"))};Hc.applyPrefixedLabelDimensions=function(t,e){var r=t._private,n=this.getLabelText(t,e),i=this.calculateLabelDimensions(t,n),a=t.pstyle("line-height").pfValue,s=t.pstyle("text-wrap").strValue,l=Ul(r.rscratch,"labelWrapCachedLines",e)||[],u=s!=="wrap"?1:Math.max(l.length,1),h=i.height/u,f=h*a,d=i.width,p=i.height+(u-1)*(a-1)*h;Tf(r.rstyle,"labelWidth",e,d),Tf(r.rscratch,"labelWidth",e,d),Tf(r.rstyle,"labelHeight",e,p),Tf(r.rscratch,"labelHeight",e,p),Tf(r.rscratch,"labelLineHeight",e,f)};Hc.getLabelText=function(t,e){var r=t._private,n=e?e+"-":"",i=t.pstyle(n+"label").strValue,a=t.pstyle("text-transform").value,s=o(function(Q,X){return X?(Tf(r.rscratch,Q,e,X),X):Ul(r.rscratch,Q,e)},"rscratch");if(!i)return"";a=="none"||(a=="uppercase"?i=i.toUpperCase():a=="lowercase"&&(i=i.toLowerCase()));var l=t.pstyle("text-wrap").value;if(l==="wrap"){var u=s("labelKey");if(u!=null&&s("labelWrapKey")===u)return s("labelWrapCachedText");for(var h="\u200B",f=i.split(` -`),d=t.pstyle("text-max-width").pfValue,p=t.pstyle("text-overflow-wrap").value,m=p==="anywhere",g=[],y=/[\s\u200b]+|$/g,v=0;vd){var T=x.matchAll(y),E="",_=0,A=Tpe(T),L;try{for(A.s();!(L=A.n()).done;){var M=L.value,N=M[0],k=x.substring(_,M.index);_=M.index+N.length;var I=E.length===0?k:E+k+N,C=this.calculateLabelDimensions(t,I),O=C.width;O<=d?E+=k+N:(E&&g.push(E),E=k+N)}}catch(Y){A.e(Y)}finally{A.f()}E.match(/^[\s\u200b]+$/)||g.push(E)}else g.push(x)}s("labelWrapCachedLines",g),i=s("labelWrapCachedText",g.join(` -`)),s("labelWrapKey",u)}else if(l==="ellipsis"){var D=t.pstyle("text-max-width").pfValue,P="",F="\u2026",B=!1;if(this.calculateLabelDimensions(t,i).widthD)break;P+=i[$],$===i.length-1&&(B=!0)}return B||(P+=F),P}return i};Hc.getLabelJustification=function(t){var e=t.pstyle("text-justification").strValue,r=t.pstyle("text-halign").strValue;if(e==="auto")if(t.isNode())switch(r){case"left":return"right";case"right":return"left";default:return"center"}else return"center";else return e};Hc.calculateLabelDimensions=function(t,e){var r=this,n=r.cy.window(),i=n.document,a=U0(e,t._private.labelDimsKey),s=r.labelDimCache||(r.labelDimCache=[]),l=s[a];if(l!=null)return l;var u=0,h=t.pstyle("font-style").strValue,f=t.pstyle("font-size").pfValue,d=t.pstyle("font-family").strValue,p=t.pstyle("font-weight").strValue,m=this.labelCalcCanvas,g=this.labelCalcCanvasContext;if(!m){m=this.labelCalcCanvas=i.createElement("canvas"),g=this.labelCalcCanvasContext=m.getContext("2d");var y=m.style;y.position="absolute",y.left="-9999px",y.top="-9999px",y.zIndex="-1",y.visibility="hidden",y.pointerEvents="none"}g.font="".concat(h," ").concat(p," ").concat(f,"px ").concat(d);for(var v=0,x=0,b=e.split(` -`),w=0;w1&&arguments[1]!==void 0?arguments[1]:!0;if(e.merge(s),l)for(var u=0;u=t.desktopTapThreshold2}var Je=i(W);ze&&(t.hoverData.tapholdCancelled=!0);var Ve=o(function(){var St=t.hoverData.dragDelta=t.hoverData.dragDelta||[];St.length===0?(St.push(ye[0]),St.push(ye[1])):(St[0]+=ye[0],St[1]+=ye[1])},"updateDragDelta");ge=!0,n(Ae,["mousemove","vmousemove","tapdrag"],W,{x:ae[0],y:ae[1]});var je=o(function(){t.data.bgActivePosistion=void 0,t.hoverData.selecting||re.emit({originalEvent:W,type:"boxstart",position:{x:ae[0],y:ae[1]}}),Ce[4]=1,t.hoverData.selecting=!0,t.redrawHint("select",!0),t.redraw()},"goIntoBoxMode");if(t.hoverData.which===3){if(ze){var kt={originalEvent:W,type:"cxtdrag",position:{x:ae[0],y:ae[1]}};Me?Me.emit(kt):re.emit(kt),t.hoverData.cxtDragged=!0,(!t.hoverData.cxtOver||Ae!==t.hoverData.cxtOver)&&(t.hoverData.cxtOver&&t.hoverData.cxtOver.emit({originalEvent:W,type:"cxtdragout",position:{x:ae[0],y:ae[1]}}),t.hoverData.cxtOver=Ae,Ae&&Ae.emit({originalEvent:W,type:"cxtdragover",position:{x:ae[0],y:ae[1]}}))}}else if(t.hoverData.dragging){if(ge=!0,re.panningEnabled()&&re.userPanningEnabled()){var at;if(t.hoverData.justStartedPan){var xt=t.hoverData.mdownPos;at={x:(ae[0]-xt[0])*he,y:(ae[1]-xt[1])*he},t.hoverData.justStartedPan=!1}else at={x:ye[0]*he,y:ye[1]*he};re.panBy(at),re.emit("dragpan"),t.hoverData.dragged=!0}ae=t.projectIntoViewport(W.clientX,W.clientY)}else if(Ce[4]==1&&(Me==null||Me.pannable())){if(ze){if(!t.hoverData.dragging&&re.boxSelectionEnabled()&&(Je||!re.panningEnabled()||!re.userPanningEnabled()))je();else if(!t.hoverData.selecting&&re.panningEnabled()&&re.userPanningEnabled()){var it=a(Me,t.hoverData.downs);it&&(t.hoverData.dragging=!0,t.hoverData.justStartedPan=!0,Ce[4]=0,t.data.bgActivePosistion=Jg(we),t.redrawHint("select",!0),t.redraw())}Me&&Me.pannable()&&Me.active()&&Me.unactivate()}}else{if(Me&&Me.pannable()&&Me.active()&&Me.unactivate(),(!Me||!Me.grabbed())&&Ae!=Ge&&(Ge&&n(Ge,["mouseout","tapdragout"],W,{x:ae[0],y:ae[1]}),Ae&&n(Ae,["mouseover","tapdragover"],W,{x:ae[0],y:ae[1]}),t.hoverData.last=Ae),Me)if(ze){if(re.boxSelectionEnabled()&&Je)Me&&Me.grabbed()&&(v(He),Me.emit("freeon"),He.emit("free"),t.dragData.didDrag&&(Me.emit("dragfreeon"),He.emit("dragfree"))),je();else if(Me&&Me.grabbed()&&t.nodeIsDraggable(Me)){var dt=!t.dragData.didDrag;dt&&t.redrawHint("eles",!0),t.dragData.didDrag=!0,t.hoverData.draggingEles||g(He,{inDragLayer:!0});var lt={x:0,y:0};if(ft(ye[0])&&ft(ye[1])&&(lt.x+=ye[0],lt.y+=ye[1],dt)){var It=t.hoverData.dragDelta;It&&ft(It[0])&&ft(It[1])&&(lt.x+=It[0],lt.y+=It[1])}t.hoverData.draggingEles=!0,He.silentShift(lt).emit("position drag"),t.redrawHint("drag",!0),t.redraw()}}else Ve();ge=!0}if(Ce[2]=ae[0],Ce[3]=ae[1],ge)return W.stopPropagation&&W.stopPropagation(),W.preventDefault&&W.preventDefault(),!1}},"mousemoveHandler"),!1);var M,N,k;t.registerBinding(e,"mouseup",o(function(W){if(!(t.hoverData.which===1&&W.which!==1&&t.hoverData.capture)){var fe=t.hoverData.capture;if(fe){t.hoverData.capture=!1;var ge=t.cy,re=t.projectIntoViewport(W.clientX,W.clientY),he=t.selection,ne=t.findNearestElement(re[0],re[1],!0,!1),ae=t.dragData.possibleDragElements,we=t.hoverData.down,Te=i(W);if(t.data.bgActivePosistion&&(t.redrawHint("select",!0),t.redraw()),t.hoverData.tapholdCancelled=!0,t.data.bgActivePosistion=void 0,we&&we.unactivate(),t.hoverData.which===3){var Ce={originalEvent:W,type:"cxttapend",position:{x:re[0],y:re[1]}};if(we?we.emit(Ce):ge.emit(Ce),!t.hoverData.cxtDragged){var Ae={originalEvent:W,type:"cxttap",position:{x:re[0],y:re[1]}};we?we.emit(Ae):ge.emit(Ae)}t.hoverData.cxtDragged=!1,t.hoverData.which=null}else if(t.hoverData.which===1){if(n(ne,["mouseup","tapend","vmouseup"],W,{x:re[0],y:re[1]}),!t.dragData.didDrag&&!t.hoverData.dragged&&!t.hoverData.selecting&&!t.hoverData.isOverThresholdDrag&&(n(we,["click","tap","vclick"],W,{x:re[0],y:re[1]}),N=!1,W.timeStamp-k<=ge.multiClickDebounceTime()?(M&&clearTimeout(M),N=!0,k=null,n(we,["dblclick","dbltap","vdblclick"],W,{x:re[0],y:re[1]})):(M=setTimeout(function(){N||n(we,["oneclick","onetap","voneclick"],W,{x:re[0],y:re[1]})},ge.multiClickDebounceTime()),k=W.timeStamp)),we==null&&!t.dragData.didDrag&&!t.hoverData.selecting&&!t.hoverData.dragged&&!i(W)&&(ge.$(r).unselect(["tapunselect"]),ae.length>0&&t.redrawHint("eles",!0),t.dragData.possibleDragElements=ae=ge.collection()),ne==we&&!t.dragData.didDrag&&!t.hoverData.selecting&&ne!=null&&ne._private.selectable&&(t.hoverData.dragging||(ge.selectionType()==="additive"||Te?ne.selected()?ne.unselect(["tapunselect"]):ne.select(["tapselect"]):Te||(ge.$(r).unmerge(ne).unselect(["tapunselect"]),ne.select(["tapselect"]))),t.redrawHint("eles",!0)),t.hoverData.selecting){var Ge=ge.collection(t.getAllInBox(he[0],he[1],he[2],he[3]));t.redrawHint("select",!0),Ge.length>0&&t.redrawHint("eles",!0),ge.emit({type:"boxend",originalEvent:W,position:{x:re[0],y:re[1]}});var Me=o(function(ze){return ze.selectable()&&!ze.selected()},"eleWouldBeSelected");ge.selectionType()==="additive"||Te||ge.$(r).unmerge(Ge).unselect(),Ge.emit("box").stdFilter(Me).select().emit("boxselect"),t.redraw()}if(t.hoverData.dragging&&(t.hoverData.dragging=!1,t.redrawHint("select",!0),t.redrawHint("eles",!0),t.redraw()),!he[4]){t.redrawHint("drag",!0),t.redrawHint("eles",!0);var ye=we&&we.grabbed();v(ae),ye&&(we.emit("freeon"),ae.emit("free"),t.dragData.didDrag&&(we.emit("dragfreeon"),ae.emit("dragfree")))}}he[4]=0,t.hoverData.down=null,t.hoverData.cxtStarted=!1,t.hoverData.draggingEles=!1,t.hoverData.selecting=!1,t.hoverData.isOverThresholdDrag=!1,t.dragData.didDrag=!1,t.hoverData.dragged=!1,t.hoverData.dragDelta=[],t.hoverData.mdownPos=null,t.hoverData.mdownGPos=null}}},"mouseupHandler"),!1);var I=o(function(W){if(!t.scrollingPage){var fe=t.cy,ge=fe.zoom(),re=fe.pan(),he=t.projectIntoViewport(W.clientX,W.clientY),ne=[he[0]*ge+re.x,he[1]*ge+re.y];if(t.hoverData.draggingEles||t.hoverData.dragging||t.hoverData.cxtStarted||A()){W.preventDefault();return}if(fe.panningEnabled()&&fe.userPanningEnabled()&&fe.zoomingEnabled()&&fe.userZoomingEnabled()){W.preventDefault(),t.data.wheelZooming=!0,clearTimeout(t.data.wheelTimeout),t.data.wheelTimeout=setTimeout(function(){t.data.wheelZooming=!1,t.redrawHint("eles",!0),t.redraw()},150);var ae;W.deltaY!=null?ae=W.deltaY/-250:W.wheelDeltaY!=null?ae=W.wheelDeltaY/1e3:ae=W.wheelDelta/1e3,ae=ae*t.wheelSensitivity;var we=W.deltaMode===1;we&&(ae*=33);var Te=fe.zoom()*Math.pow(10,ae);W.type==="gesturechange"&&(Te=t.gestureStartZoom*W.scale),fe.zoom({level:Te,renderedPosition:{x:ne[0],y:ne[1]}}),fe.emit(W.type==="gesturechange"?"pinchzoom":"scrollzoom")}}},"wheelHandler");t.registerBinding(t.container,"wheel",I,!0),t.registerBinding(e,"scroll",o(function(W){t.scrollingPage=!0,clearTimeout(t.scrollingPageTimeout),t.scrollingPageTimeout=setTimeout(function(){t.scrollingPage=!1},250)},"scrollHandler"),!0),t.registerBinding(t.container,"gesturestart",o(function(W){t.gestureStartZoom=t.cy.zoom(),t.hasTouchStarted||W.preventDefault()},"gestureStartHandler"),!0),t.registerBinding(t.container,"gesturechange",function(me){t.hasTouchStarted||I(me)},!0),t.registerBinding(t.container,"mouseout",o(function(W){var fe=t.projectIntoViewport(W.clientX,W.clientY);t.cy.emit({originalEvent:W,type:"mouseout",position:{x:fe[0],y:fe[1]}})},"mouseOutHandler"),!1),t.registerBinding(t.container,"mouseover",o(function(W){var fe=t.projectIntoViewport(W.clientX,W.clientY);t.cy.emit({originalEvent:W,type:"mouseover",position:{x:fe[0],y:fe[1]}})},"mouseOverHandler"),!1);var C,O,D,P,F,B,$,z,Y,Q,X,ie,j,J=o(function(W,fe,ge,re){return Math.sqrt((ge-W)*(ge-W)+(re-fe)*(re-fe))},"distance"),Z=o(function(W,fe,ge,re){return(ge-W)*(ge-W)+(re-fe)*(re-fe)},"distanceSq"),H;t.registerBinding(t.container,"touchstart",H=o(function(W){if(t.hasTouchStarted=!0,!!L(W)){b(),t.touchData.capture=!0,t.data.bgActivePosistion=void 0;var fe=t.cy,ge=t.touchData.now,re=t.touchData.earlier;if(W.touches[0]){var he=t.projectIntoViewport(W.touches[0].clientX,W.touches[0].clientY);ge[0]=he[0],ge[1]=he[1]}if(W.touches[1]){var he=t.projectIntoViewport(W.touches[1].clientX,W.touches[1].clientY);ge[2]=he[0],ge[3]=he[1]}if(W.touches[2]){var he=t.projectIntoViewport(W.touches[2].clientX,W.touches[2].clientY);ge[4]=he[0],ge[5]=he[1]}if(W.touches[1]){t.touchData.singleTouchMoved=!0,v(t.dragData.touchDragEles);var ne=t.findContainerClientCoords();Y=ne[0],Q=ne[1],X=ne[2],ie=ne[3],C=W.touches[0].clientX-Y,O=W.touches[0].clientY-Q,D=W.touches[1].clientX-Y,P=W.touches[1].clientY-Q,j=0<=C&&C<=X&&0<=D&&D<=X&&0<=O&&O<=ie&&0<=P&&P<=ie;var ae=fe.pan(),we=fe.zoom();F=J(C,O,D,P),B=Z(C,O,D,P),$=[(C+D)/2,(O+P)/2],z=[($[0]-ae.x)/we,($[1]-ae.y)/we];var Te=200,Ce=Te*Te;if(B=1){for(var gt=t.touchData.startPosition=[null,null,null,null,null,null],yt=0;yt=t.touchTapThreshold2}if(fe&&t.touchData.cxt){W.preventDefault();var gt=W.touches[0].clientX-Y,yt=W.touches[0].clientY-Q,tt=W.touches[1].clientX-Y,Ye=W.touches[1].clientY-Q,Je=Z(gt,yt,tt,Ye),Ve=Je/B,je=150,kt=je*je,at=1.5,xt=at*at;if(Ve>=xt||Je>=kt){t.touchData.cxt=!1,t.data.bgActivePosistion=void 0,t.redrawHint("select",!0);var it={originalEvent:W,type:"cxttapend",position:{x:he[0],y:he[1]}};t.touchData.start?(t.touchData.start.unactivate().emit(it),t.touchData.start=null):re.emit(it)}}if(fe&&t.touchData.cxt){var it={originalEvent:W,type:"cxtdrag",position:{x:he[0],y:he[1]}};t.data.bgActivePosistion=void 0,t.redrawHint("select",!0),t.touchData.start?t.touchData.start.emit(it):re.emit(it),t.touchData.start&&(t.touchData.start._private.grabbed=!1),t.touchData.cxtDragged=!0;var dt=t.findNearestElement(he[0],he[1],!0,!0);(!t.touchData.cxtOver||dt!==t.touchData.cxtOver)&&(t.touchData.cxtOver&&t.touchData.cxtOver.emit({originalEvent:W,type:"cxtdragout",position:{x:he[0],y:he[1]}}),t.touchData.cxtOver=dt,dt&&dt.emit({originalEvent:W,type:"cxtdragover",position:{x:he[0],y:he[1]}}))}else if(fe&&W.touches[2]&&re.boxSelectionEnabled())W.preventDefault(),t.data.bgActivePosistion=void 0,this.lastThreeTouch=+new Date,t.touchData.selecting||re.emit({originalEvent:W,type:"boxstart",position:{x:he[0],y:he[1]}}),t.touchData.selecting=!0,t.touchData.didSelect=!0,ge[4]=1,!ge||ge.length===0||ge[0]===void 0?(ge[0]=(he[0]+he[2]+he[4])/3,ge[1]=(he[1]+he[3]+he[5])/3,ge[2]=(he[0]+he[2]+he[4])/3+1,ge[3]=(he[1]+he[3]+he[5])/3+1):(ge[2]=(he[0]+he[2]+he[4])/3,ge[3]=(he[1]+he[3]+he[5])/3),t.redrawHint("select",!0),t.redraw();else if(fe&&W.touches[1]&&!t.touchData.didSelect&&re.zoomingEnabled()&&re.panningEnabled()&&re.userZoomingEnabled()&&re.userPanningEnabled()){W.preventDefault(),t.data.bgActivePosistion=void 0,t.redrawHint("select",!0);var lt=t.dragData.touchDragEles;if(lt){t.redrawHint("drag",!0);for(var It=0;It0&&!t.hoverData.draggingEles&&!t.swipePanning&&t.data.bgActivePosistion!=null&&(t.data.bgActivePosistion=void 0,t.redrawHint("select",!0),t.redraw())}},"touchmoveHandler"),!1);var K;t.registerBinding(e,"touchcancel",K=o(function(W){var fe=t.touchData.start;t.touchData.capture=!1,fe&&fe.unactivate()},"touchcancelHandler"));var se,ce,ue,te;if(t.registerBinding(e,"touchend",se=o(function(W){var fe=t.touchData.start,ge=t.touchData.capture;if(ge)W.touches.length===0&&(t.touchData.capture=!1),W.preventDefault();else return;var re=t.selection;t.swipePanning=!1,t.hoverData.draggingEles=!1;var he=t.cy,ne=he.zoom(),ae=t.touchData.now,we=t.touchData.earlier;if(W.touches[0]){var Te=t.projectIntoViewport(W.touches[0].clientX,W.touches[0].clientY);ae[0]=Te[0],ae[1]=Te[1]}if(W.touches[1]){var Te=t.projectIntoViewport(W.touches[1].clientX,W.touches[1].clientY);ae[2]=Te[0],ae[3]=Te[1]}if(W.touches[2]){var Te=t.projectIntoViewport(W.touches[2].clientX,W.touches[2].clientY);ae[4]=Te[0],ae[5]=Te[1]}fe&&fe.unactivate();var Ce;if(t.touchData.cxt){if(Ce={originalEvent:W,type:"cxttapend",position:{x:ae[0],y:ae[1]}},fe?fe.emit(Ce):he.emit(Ce),!t.touchData.cxtDragged){var Ae={originalEvent:W,type:"cxttap",position:{x:ae[0],y:ae[1]}};fe?fe.emit(Ae):he.emit(Ae)}t.touchData.start&&(t.touchData.start._private.grabbed=!1),t.touchData.cxt=!1,t.touchData.start=null,t.redraw();return}if(!W.touches[2]&&he.boxSelectionEnabled()&&t.touchData.selecting){t.touchData.selecting=!1;var Ge=he.collection(t.getAllInBox(re[0],re[1],re[2],re[3]));re[0]=void 0,re[1]=void 0,re[2]=void 0,re[3]=void 0,re[4]=0,t.redrawHint("select",!0),he.emit({type:"boxend",originalEvent:W,position:{x:ae[0],y:ae[1]}});var Me=o(function(kt){return kt.selectable()&&!kt.selected()},"eleWouldBeSelected");Ge.emit("box").stdFilter(Me).select().emit("boxselect"),Ge.nonempty()&&t.redrawHint("eles",!0),t.redraw()}if(fe?.unactivate(),W.touches[2])t.data.bgActivePosistion=void 0,t.redrawHint("select",!0);else if(!W.touches[1]){if(!W.touches[0]){if(!W.touches[0]){t.data.bgActivePosistion=void 0,t.redrawHint("select",!0);var ye=t.dragData.touchDragEles;if(fe!=null){var He=fe._private.grabbed;v(ye),t.redrawHint("drag",!0),t.redrawHint("eles",!0),He&&(fe.emit("freeon"),ye.emit("free"),t.dragData.didDrag&&(fe.emit("dragfreeon"),ye.emit("dragfree"))),n(fe,["touchend","tapend","vmouseup","tapdragout"],W,{x:ae[0],y:ae[1]}),fe.unactivate(),t.touchData.start=null}else{var ze=t.findNearestElement(ae[0],ae[1],!0,!0);n(ze,["touchend","tapend","vmouseup","tapdragout"],W,{x:ae[0],y:ae[1]})}var Ze=t.touchData.startPosition[0]-ae[0],gt=Ze*Ze,yt=t.touchData.startPosition[1]-ae[1],tt=yt*yt,Ye=gt+tt,Je=Ye*ne*ne;t.touchData.singleTouchMoved||(fe||he.$(":selected").unselect(["tapunselect"]),n(fe,["tap","vclick"],W,{x:ae[0],y:ae[1]}),ce=!1,W.timeStamp-te<=he.multiClickDebounceTime()?(ue&&clearTimeout(ue),ce=!0,te=null,n(fe,["dbltap","vdblclick"],W,{x:ae[0],y:ae[1]})):(ue=setTimeout(function(){ce||n(fe,["onetap","voneclick"],W,{x:ae[0],y:ae[1]})},he.multiClickDebounceTime()),te=W.timeStamp)),fe!=null&&!t.dragData.didDrag&&fe._private.selectable&&Je"u"){var De=[],oe=o(function(W){return{clientX:W.clientX,clientY:W.clientY,force:1,identifier:W.pointerId,pageX:W.pageX,pageY:W.pageY,radiusX:W.width/2,radiusY:W.height/2,screenX:W.screenX,screenY:W.screenY,target:W.target}},"makeTouch"),ke=o(function(W){return{event:W,touch:oe(W)}},"makePointer"),Ie=o(function(W){De.push(ke(W))},"addPointer"),Se=o(function(W){for(var fe=0;fe0)return Y[0]}return null},"getCurveT"),g=Object.keys(p),y=0;y0?m:Hpe(a,s,e,r,n,i,l,u)},"intersectLine"),checkPoint:o(function(e,r,n,i,a,s,l,u){u=u==="auto"?Y0(i,a):u;var h=2*u;if(Qu(e,r,this.points,s,l,i,a-h,[0,-1],n)||Qu(e,r,this.points,s,l,i-h,a,[0,-1],n))return!0;var f=i/2+2*n,d=a/2+2*n,p=[s-f,l-d,s-f,l,s+f,l,s+f,l-d];return!!(zs(e,r,p)||$0(e,r,h,h,s+i/2-u,l+a/2-u,n)||$0(e,r,h,h,s-i/2+u,l+a/2-u,n))},"checkPoint")}};Ju.registerNodeShapes=function(){var t=this.nodeShapes={},e=this;this.generateEllipse(),this.generatePolygon("triangle",ls(3,0)),this.generateRoundPolygon("round-triangle",ls(3,0)),this.generatePolygon("rectangle",ls(4,0)),t.square=t.rectangle,this.generateRoundRectangle(),this.generateCutRectangle(),this.generateBarrel(),this.generateBottomRoundrectangle();{var r=[0,1,1,0,0,-1,-1,0];this.generatePolygon("diamond",r),this.generateRoundPolygon("round-diamond",r)}this.generatePolygon("pentagon",ls(5,0)),this.generateRoundPolygon("round-pentagon",ls(5,0)),this.generatePolygon("hexagon",ls(6,0)),this.generateRoundPolygon("round-hexagon",ls(6,0)),this.generatePolygon("heptagon",ls(7,0)),this.generateRoundPolygon("round-heptagon",ls(7,0)),this.generatePolygon("octagon",ls(8,0)),this.generateRoundPolygon("round-octagon",ls(8,0));var n=new Array(20);{var i=NP(5,0),a=NP(5,Math.PI/5),s=.5*(3-Math.sqrt(5));s*=1.57;for(var l=0;l=e.deqFastCost*S)break}else if(h){if(b>=e.deqCost*m||b>=e.deqAvgCost*p)break}else if(w>=e.deqNoDrawCost*LP)break;var T=e.deq(n,v,y);if(T.length>0)for(var E=0;E0&&(e.onDeqd(n,g),!h&&e.shouldRedraw(n,g,v,y)&&a())},"dequeue"),l=e.priority||JP;i.beforeRender(s,l(n))}},"setupDequeueingImpl")},"setupDequeueing")},pQe=function(){function t(e){var r=arguments.length>1&&arguments[1]!==void 0?arguments[1]:C6;XP(this,t),this.idsByKey=new Vc,this.keyForId=new Vc,this.cachesByLvl=new Vc,this.lvls=[],this.getKey=e,this.doesEleInvalidateKey=r}return o(t,"ElementTextureCacheLookup"),jP(t,[{key:"getIdsFor",value:o(function(r){r==null&&oi("Can not get id list for null key");var n=this.idsByKey,i=this.idsByKey.get(r);return i||(i=new c1,n.set(r,i)),i},"getIdsFor")},{key:"addIdForKey",value:o(function(r,n){r!=null&&this.getIdsFor(r).add(n)},"addIdForKey")},{key:"deleteIdForKey",value:o(function(r,n){r!=null&&this.getIdsFor(r).delete(n)},"deleteIdForKey")},{key:"getNumberOfIdsForKey",value:o(function(r){return r==null?0:this.getIdsFor(r).size},"getNumberOfIdsForKey")},{key:"updateKeyMappingFor",value:o(function(r){var n=r.id(),i=this.keyForId.get(n),a=this.getKey(r);this.deleteIdForKey(i,n),this.addIdForKey(a,n),this.keyForId.set(n,a)},"updateKeyMappingFor")},{key:"deleteKeyMappingFor",value:o(function(r){var n=r.id(),i=this.keyForId.get(n);this.deleteIdForKey(i,n),this.keyForId.delete(n)},"deleteKeyMappingFor")},{key:"keyHasChangedFor",value:o(function(r){var n=r.id(),i=this.keyForId.get(n),a=this.getKey(r);return i!==a},"keyHasChangedFor")},{key:"isInvalid",value:o(function(r){return this.keyHasChangedFor(r)||this.doesEleInvalidateKey(r)},"isInvalid")},{key:"getCachesAt",value:o(function(r){var n=this.cachesByLvl,i=this.lvls,a=n.get(r);return a||(a=new Vc,n.set(r,a),i.push(r)),a},"getCachesAt")},{key:"getCache",value:o(function(r,n){return this.getCachesAt(n).get(r)},"getCache")},{key:"get",value:o(function(r,n){var i=this.getKey(r),a=this.getCache(i,n);return 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this.rect.x+this.rect.width/2},h.prototype.getCenterY=function(){return this.rect.y+this.rect.height/2},h.prototype.getCenter=function(){return new u(this.rect.x+this.rect.width/2,this.rect.y+this.rect.height/2)},h.prototype.getLocation=function(){return new u(this.rect.x,this.rect.y)},h.prototype.getRect=function(){return this.rect},h.prototype.getDiagonal=function(){return Math.sqrt(this.rect.width*this.rect.width+this.rect.height*this.rect.height)},h.prototype.getHalfTheDiagonal=function(){return Math.sqrt(this.rect.height*this.rect.height+this.rect.width*this.rect.width)/2},h.prototype.setRect=function(d,p){this.rect.x=d.x,this.rect.y=d.y,this.rect.width=p.width,this.rect.height=p.height},h.prototype.setCenter=function(d,p){this.rect.x=d-this.rect.width/2,this.rect.y=p-this.rect.height/2},h.prototype.setLocation=function(d,p){this.rect.x=d,this.rect.y=p},h.prototype.moveBy=function(d,p){this.rect.x+=d,this.rect.y+=p},h.prototype.getEdgeListToNode=function(d){var p=[],m,g=this;return g.edges.forEach(function(y){if(y.target==d){if(y.source!=g)throw"Incorrect edge source!";p.push(y)}}),p},h.prototype.getEdgesBetween=function(d){var p=[],m,g=this;return g.edges.forEach(function(y){if(!(y.source==g||y.target==g))throw"Incorrect edge source and/or target";(y.target==d||y.source==d)&&p.push(y)}),p},h.prototype.getNeighborsList=function(){var d=new Set,p=this;return p.edges.forEach(function(m){if(m.source==p)d.add(m.target);else{if(m.target!=p)throw"Incorrect incidency!";d.add(m.source)}}),d},h.prototype.withChildren=function(){var d=new Set,p,m;if(d.add(this),this.child!=null)for(var g=this.child.getNodes(),y=0;yp&&(this.rect.x-=(this.labelWidth-p)/2,this.setWidth(this.labelWidth)),this.labelHeight>m&&(this.labelPos=="center"?this.rect.y-=(this.labelHeight-m)/2:this.labelPos=="top"&&(this.rect.y-=this.labelHeight-m),this.setHeight(this.labelHeight))}}},h.prototype.getInclusionTreeDepth=function(){if(this.inclusionTreeDepth==i.MAX_VALUE)throw"assert failed";return this.inclusionTreeDepth},h.prototype.transform=function(d){var p=this.rect.x;p>s.WORLD_BOUNDARY?p=s.WORLD_BOUNDARY:p<-s.WORLD_BOUNDARY&&(p=-s.WORLD_BOUNDARY);var m=this.rect.y;m>s.WORLD_BOUNDARY?m=s.WORLD_BOUNDARY:m<-s.WORLD_BOUNDARY&&(m=-s.WORLD_BOUNDARY);var g=new u(p,m),y=d.inverseTransformPoint(g);this.setLocation(y.x,y.y)},h.prototype.getLeft=function(){return this.rect.x},h.prototype.getRight=function(){return this.rect.x+this.rect.width},h.prototype.getTop=function(){return this.rect.y},h.prototype.getBottom=function(){return this.rect.y+this.rect.height},h.prototype.getParent=function(){return this.owner==null?null:this.owner.getParent()},t.exports=h},function(t,e,r){"use strict";function n(i,a){i==null&&a==null?(this.x=0,this.y=0):(this.x=i,this.y=a)}o(n,"PointD"),n.prototype.getX=function(){return this.x},n.prototype.getY=function(){return this.y},n.prototype.setX=function(i){this.x=i},n.prototype.setY=function(i){this.y=i},n.prototype.getDifference=function(i){return new DimensionD(this.x-i.x,this.y-i.y)},n.prototype.getCopy=function(){return new n(this.x,this.y)},n.prototype.translate=function(i){return this.x+=i.width,this.y+=i.height,this},t.exports=n},function(t,e,r){"use strict";var n=r(2),i=r(10),a=r(0),s=r(6),l=r(3),u=r(1),h=r(13),f=r(12),d=r(11);function p(g,y,v){n.call(this,v),this.estimatedSize=i.MIN_VALUE,this.margin=a.DEFAULT_GRAPH_MARGIN,this.edges=[],this.nodes=[],this.isConnected=!1,this.parent=g,y!=null&&y instanceof s?this.graphManager=y:y!=null&&y instanceof Layout&&(this.graphManager=y.graphManager)}o(p,"LGraph"),p.prototype=Object.create(n.prototype);for(var m in n)p[m]=n[m];p.prototype.getNodes=function(){return this.nodes},p.prototype.getEdges=function(){return this.edges},p.prototype.getGraphManager=function(){return this.graphManager},p.prototype.getParent=function(){return this.parent},p.prototype.getLeft=function(){return this.left},p.prototype.getRight=function(){return this.right},p.prototype.getTop=function(){return this.top},p.prototype.getBottom=function(){return this.bottom},p.prototype.isConnected=function(){return this.isConnected},p.prototype.add=function(g,y,v){if(y==null&&v==null){var x=g;if(this.graphManager==null)throw"Graph has no graph mgr!";if(this.getNodes().indexOf(x)>-1)throw"Node already in graph!";return x.owner=this,this.getNodes().push(x),x}else{var b=g;if(!(this.getNodes().indexOf(y)>-1&&this.getNodes().indexOf(v)>-1))throw"Source or target not in graph!";if(!(y.owner==v.owner&&y.owner==this))throw"Both owners must be this graph!";return y.owner!=v.owner?null:(b.source=y,b.target=v,b.isInterGraph=!1,this.getEdges().push(b),y.edges.push(b),v!=y&&v.edges.push(b),b)}},p.prototype.remove=function(g){var y=g;if(g instanceof l){if(y==null)throw"Node is null!";if(!(y.owner!=null&&y.owner==this))throw"Owner graph is invalid!";if(this.graphManager==null)throw"Owner graph manager is invalid!";for(var v=y.edges.slice(),x,b=v.length,w=0;w-1&&E>-1))throw"Source and/or target doesn't know this edge!";x.source.edges.splice(T,1),x.target!=x.source&&x.target.edges.splice(E,1);var S=x.source.owner.getEdges().indexOf(x);if(S==-1)throw"Not in owner's edge list!";x.source.owner.getEdges().splice(S,1)}},p.prototype.updateLeftTop=function(){for(var g=i.MAX_VALUE,y=i.MAX_VALUE,v,x,b,w=this.getNodes(),S=w.length,T=0;Tv&&(g=v),y>x&&(y=x)}return g==i.MAX_VALUE?null:(w[0].getParent().paddingLeft!=null?b=w[0].getParent().paddingLeft:b=this.margin,this.left=y-b,this.top=g-b,new f(this.left,this.top))},p.prototype.updateBounds=function(g){for(var y=i.MAX_VALUE,v=-i.MAX_VALUE,x=i.MAX_VALUE,b=-i.MAX_VALUE,w,S,T,E,_,A=this.nodes,L=A.length,M=0;Mw&&(y=w),vT&&(x=T),bw&&(y=w),vT&&(x=T),b=this.nodes.length){var L=0;v.forEach(function(M){M.owner==g&&L++}),L==this.nodes.length&&(this.isConnected=!0)}},t.exports=p},function(t,e,r){"use strict";var n,i=r(1);function a(s){n=r(5),this.layout=s,this.graphs=[],this.edges=[]}o(a,"LGraphManager"),a.prototype.addRoot=function(){var s=this.layout.newGraph(),l=this.layout.newNode(null),u=this.add(s,l);return this.setRootGraph(u),this.rootGraph},a.prototype.add=function(s,l,u,h,f){if(u==null&&h==null&&f==null){if(s==null)throw"Graph is null!";if(l==null)throw"Parent node is null!";if(this.graphs.indexOf(s)>-1)throw"Graph already in this graph mgr!";if(this.graphs.push(s),s.parent!=null)throw"Already has a parent!";if(l.child!=null)throw"Already has a child!";return s.parent=l,l.child=s,s}else{f=u,h=l,u=s;var d=h.getOwner(),p=f.getOwner();if(!(d!=null&&d.getGraphManager()==this))throw"Source not in this graph mgr!";if(!(p!=null&&p.getGraphManager()==this))throw"Target not in this graph mgr!";if(d==p)return u.isInterGraph=!1,d.add(u,h,f);if(u.isInterGraph=!0,u.source=h,u.target=f,this.edges.indexOf(u)>-1)throw"Edge already in inter-graph edge list!";if(this.edges.push(u),!(u.source!=null&&u.target!=null))throw"Edge source and/or target is null!";if(!(u.source.edges.indexOf(u)==-1&&u.target.edges.indexOf(u)==-1))throw"Edge already in source and/or target incidency list!";return u.source.edges.push(u),u.target.edges.push(u),u}},a.prototype.remove=function(s){if(s instanceof n){var l=s;if(l.getGraphManager()!=this)throw"Graph not in this graph mgr";if(!(l==this.rootGraph||l.parent!=null&&l.parent.graphManager==this))throw"Invalid parent node!";var u=[];u=u.concat(l.getEdges());for(var h,f=u.length,d=0;d=s.getRight()?l[0]+=Math.min(s.getX()-a.getX(),a.getRight()-s.getRight()):s.getX()<=a.getX()&&s.getRight()>=a.getRight()&&(l[0]+=Math.min(a.getX()-s.getX(),s.getRight()-a.getRight())),a.getY()<=s.getY()&&a.getBottom()>=s.getBottom()?l[1]+=Math.min(s.getY()-a.getY(),a.getBottom()-s.getBottom()):s.getY()<=a.getY()&&s.getBottom()>=a.getBottom()&&(l[1]+=Math.min(a.getY()-s.getY(),s.getBottom()-a.getBottom()));var f=Math.abs((s.getCenterY()-a.getCenterY())/(s.getCenterX()-a.getCenterX()));s.getCenterY()===a.getCenterY()&&s.getCenterX()===a.getCenterX()&&(f=1);var d=f*l[0],p=l[1]/f;l[0]d)return l[0]=u,l[1]=m,l[2]=f,l[3]=A,!1;if(hf)return l[0]=p,l[1]=h,l[2]=E,l[3]=d,!1;if(uf?(l[0]=y,l[1]=v,k=!0):(l[0]=g,l[1]=m,k=!0):C===D&&(u>f?(l[0]=p,l[1]=m,k=!0):(l[0]=x,l[1]=v,k=!0)),-O===D?f>u?(l[2]=_,l[3]=A,I=!0):(l[2]=E,l[3]=T,I=!0):O===D&&(f>u?(l[2]=S,l[3]=T,I=!0):(l[2]=L,l[3]=A,I=!0)),k&&I)return!1;if(u>f?h>d?(P=this.getCardinalDirection(C,D,4),F=this.getCardinalDirection(O,D,2)):(P=this.getCardinalDirection(-C,D,3),F=this.getCardinalDirection(-O,D,1)):h>d?(P=this.getCardinalDirection(-C,D,1),F=this.getCardinalDirection(-O,D,3)):(P=this.getCardinalDirection(C,D,2),F=this.getCardinalDirection(O,D,4)),!k)switch(P){case 1:$=m,B=u+-w/D,l[0]=B,l[1]=$;break;case 2:B=x,$=h+b*D,l[0]=B,l[1]=$;break;case 3:$=v,B=u+w/D,l[0]=B,l[1]=$;break;case 4:B=y,$=h+-b*D,l[0]=B,l[1]=$;break}if(!I)switch(F){case 1:Y=T,z=f+-N/D,l[2]=z,l[3]=Y;break;case 2:z=L,Y=d+M*D,l[2]=z,l[3]=Y;break;case 3:Y=A,z=f+N/D,l[2]=z,l[3]=Y;break;case 4:z=_,Y=d+-M*D,l[2]=z,l[3]=Y;break}}return!1},i.getCardinalDirection=function(a,s,l){return a>s?l:1+l%4},i.getIntersection=function(a,s,l,u){if(u==null)return this.getIntersection2(a,s,l);var h=a.x,f=a.y,d=s.x,p=s.y,m=l.x,g=l.y,y=u.x,v=u.y,x=void 0,b=void 0,w=void 0,S=void 0,T=void 0,E=void 0,_=void 0,A=void 0,L=void 0;return w=p-f,T=h-d,_=d*f-h*p,S=v-g,E=m-y,A=y*g-m*v,L=w*E-S*T,L===0?null:(x=(T*A-E*_)/L,b=(S*_-w*A)/L,new n(x,b))},i.angleOfVector=function(a,s,l,u){var h=void 0;return a!==l?(h=Math.atan((u-s)/(l-a)),l0?1:i<0?-1:0},n.floor=function(i){return i<0?Math.ceil(i):Math.floor(i)},n.ceil=function(i){return i<0?Math.floor(i):Math.ceil(i)},t.exports=n},function(t,e,r){"use strict";function n(){}o(n,"Integer"),n.MAX_VALUE=2147483647,n.MIN_VALUE=-2147483648,t.exports=n},function(t,e,r){"use strict";var n=function(){function h(f,d){for(var p=0;p"u"?"undefined":n(a);return a==null||s!="object"&&s!="function"},t.exports=i},function(t,e,r){"use strict";function n(m){if(Array.isArray(m)){for(var g=0,y=Array(m.length);g0&&g;){for(w.push(T[0]);w.length>0&&g;){var E=w[0];w.splice(0,1),b.add(E);for(var _=E.getEdges(),x=0;x<_.length;x++){var A=_[x].getOtherEnd(E);if(S.get(E)!=A)if(!b.has(A))w.push(A),S.set(A,E);else{g=!1;break}}}if(!g)m=[];else{var L=[].concat(n(b));m.push(L);for(var x=0;x-1&&T.splice(N,1)}b=new Set,S=new Map}}return m},p.prototype.createDummyNodesForBendpoints=function(m){for(var g=[],y=m.source,v=this.graphManager.calcLowestCommonAncestor(m.source,m.target),x=0;x0){for(var v=this.edgeToDummyNodes.get(y),x=0;x=0&&g.splice(A,1);var L=S.getNeighborsList();L.forEach(function(k){if(y.indexOf(k)<0){var I=v.get(k),C=I-1;C==1&&E.push(k),v.set(k,C)}})}y=y.concat(E),(g.length==1||g.length==2)&&(x=!0,b=g[0])}return b},p.prototype.setGraphManager=function(m){this.graphManager=m},t.exports=p},function(t,e,r){"use strict";function n(){}o(n,"RandomSeed"),n.seed=1,n.x=0,n.nextDouble=function(){return n.x=Math.sin(n.seed++)*1e4,n.x-Math.floor(n.x)},t.exports=n},function(t,e,r){"use strict";var n=r(4);function i(a,s){this.lworldOrgX=0,this.lworldOrgY=0,this.ldeviceOrgX=0,this.ldeviceOrgY=0,this.lworldExtX=1,this.lworldExtY=1,this.ldeviceExtX=1,this.ldeviceExtY=1}o(i,"Transform"),i.prototype.getWorldOrgX=function(){return this.lworldOrgX},i.prototype.setWorldOrgX=function(a){this.lworldOrgX=a},i.prototype.getWorldOrgY=function(){return this.lworldOrgY},i.prototype.setWorldOrgY=function(a){this.lworldOrgY=a},i.prototype.getWorldExtX=function(){return this.lworldExtX},i.prototype.setWorldExtX=function(a){this.lworldExtX=a},i.prototype.getWorldExtY=function(){return this.lworldExtY},i.prototype.setWorldExtY=function(a){this.lworldExtY=a},i.prototype.getDeviceOrgX=function(){return this.ldeviceOrgX},i.prototype.setDeviceOrgX=function(a){this.ldeviceOrgX=a},i.prototype.getDeviceOrgY=function(){return this.ldeviceOrgY},i.prototype.setDeviceOrgY=function(a){this.ldeviceOrgY=a},i.prototype.getDeviceExtX=function(){return this.ldeviceExtX},i.prototype.setDeviceExtX=function(a){this.ldeviceExtX=a},i.prototype.getDeviceExtY=function(){return this.ldeviceExtY},i.prototype.setDeviceExtY=function(a){this.ldeviceExtY=a},i.prototype.transformX=function(a){var s=0,l=this.lworldExtX;return l!=0&&(s=this.ldeviceOrgX+(a-this.lworldOrgX)*this.ldeviceExtX/l),s},i.prototype.transformY=function(a){var s=0,l=this.lworldExtY;return l!=0&&(s=this.ldeviceOrgY+(a-this.lworldOrgY)*this.ldeviceExtY/l),s},i.prototype.inverseTransformX=function(a){var s=0,l=this.ldeviceExtX;return l!=0&&(s=this.lworldOrgX+(a-this.ldeviceOrgX)*this.lworldExtX/l),s},i.prototype.inverseTransformY=function(a){var s=0,l=this.ldeviceExtY;return l!=0&&(s=this.lworldOrgY+(a-this.ldeviceOrgY)*this.lworldExtY/l),s},i.prototype.inverseTransformPoint=function(a){var s=new n(this.inverseTransformX(a.x),this.inverseTransformY(a.y));return s},t.exports=i},function(t,e,r){"use strict";function n(d){if(Array.isArray(d)){for(var p=0,m=Array(d.length);pa.ADAPTATION_LOWER_NODE_LIMIT&&(this.coolingFactor=Math.max(this.coolingFactor*a.COOLING_ADAPTATION_FACTOR,this.coolingFactor-(d-a.ADAPTATION_LOWER_NODE_LIMIT)/(a.ADAPTATION_UPPER_NODE_LIMIT-a.ADAPTATION_LOWER_NODE_LIMIT)*this.coolingFactor*(1-a.COOLING_ADAPTATION_FACTOR))),this.maxNodeDisplacement=a.MAX_NODE_DISPLACEMENT_INCREMENTAL):(d>a.ADAPTATION_LOWER_NODE_LIMIT?this.coolingFactor=Math.max(a.COOLING_ADAPTATION_FACTOR,1-(d-a.ADAPTATION_LOWER_NODE_LIMIT)/(a.ADAPTATION_UPPER_NODE_LIMIT-a.ADAPTATION_LOWER_NODE_LIMIT)*(1-a.COOLING_ADAPTATION_FACTOR)):this.coolingFactor=1,this.initialCoolingFactor=this.coolingFactor,this.maxNodeDisplacement=a.MAX_NODE_DISPLACEMENT),this.maxIterations=Math.max(this.getAllNodes().length*5,this.maxIterations),this.totalDisplacementThreshold=this.displacementThresholdPerNode*this.getAllNodes().length,this.repulsionRange=this.calcRepulsionRange()},h.prototype.calcSpringForces=function(){for(var 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g=this.child.getNodes(),y=0;yp?(this.rect.x-=(this.labelWidth-p)/2,this.setWidth(this.labelWidth)):this.labelPosHorizontal=="right"&&this.setWidth(p+this.labelWidth)),this.labelHeight&&(this.labelPosVertical=="top"?(this.rect.y-=this.labelHeight,this.setHeight(m+this.labelHeight)):this.labelPosVertical=="center"&&this.labelHeight>m?(this.rect.y-=(this.labelHeight-m)/2,this.setHeight(this.labelHeight)):this.labelPosVertical=="bottom"&&this.setHeight(m+this.labelHeight))}}},h.prototype.getInclusionTreeDepth=function(){if(this.inclusionTreeDepth==i.MAX_VALUE)throw"assert failed";return this.inclusionTreeDepth},h.prototype.transform=function(d){var p=this.rect.x;p>s.WORLD_BOUNDARY?p=s.WORLD_BOUNDARY:p<-s.WORLD_BOUNDARY&&(p=-s.WORLD_BOUNDARY);var m=this.rect.y;m>s.WORLD_BOUNDARY?m=s.WORLD_BOUNDARY:m<-s.WORLD_BOUNDARY&&(m=-s.WORLD_BOUNDARY);var g=new u(p,m),y=d.inverseTransformPoint(g);this.setLocation(y.x,y.y)},h.prototype.getLeft=function(){return this.rect.x},h.prototype.getRight=function(){return this.rect.x+this.rect.width},h.prototype.getTop=function(){return this.rect.y},h.prototype.getBottom=function(){return this.rect.y+this.rect.height},h.prototype.getParent=function(){return this.owner==null?null:this.owner.getParent()},t.exports=h},function(t,e,r){"use strict";var n=r(0);function i(){}o(i,"FDLayoutConstants");for(var a in n)i[a]=n[a];i.MAX_ITERATIONS=2500,i.DEFAULT_EDGE_LENGTH=50,i.DEFAULT_SPRING_STRENGTH=.45,i.DEFAULT_REPULSION_STRENGTH=4500,i.DEFAULT_GRAVITY_STRENGTH=.4,i.DEFAULT_COMPOUND_GRAVITY_STRENGTH=1,i.DEFAULT_GRAVITY_RANGE_FACTOR=3.8,i.DEFAULT_COMPOUND_GRAVITY_RANGE_FACTOR=1.5,i.DEFAULT_USE_SMART_IDEAL_EDGE_LENGTH_CALCULATION=!0,i.DEFAULT_USE_SMART_REPULSION_RANGE_CALCULATION=!0,i.DEFAULT_COOLING_FACTOR_INCREMENTAL=.3,i.COOLING_ADAPTATION_FACTOR=.33,i.ADAPTATION_LOWER_NODE_LIMIT=1e3,i.ADAPTATION_UPPER_NODE_LIMIT=5e3,i.MAX_NODE_DISPLACEMENT_INCREMENTAL=100,i.MAX_NODE_DISPLACEMENT=i.MAX_NODE_DISPLACEMENT_INCREMENTAL*3,i.MIN_REPULSION_DIST=i.DEFAULT_EDGE_LENGTH/10,i.CONVERGENCE_CHECK_PERIOD=100,i.PER_LEVEL_IDEAL_EDGE_LENGTH_FACTOR=.1,i.MIN_EDGE_LENGTH=1,i.GRID_CALCULATION_CHECK_PERIOD=10,t.exports=i},function(t,e,r){"use strict";function n(i,a){i==null&&a==null?(this.x=0,this.y=0):(this.x=i,this.y=a)}o(n,"PointD"),n.prototype.getX=function(){return this.x},n.prototype.getY=function(){return this.y},n.prototype.setX=function(i){this.x=i},n.prototype.setY=function(i){this.y=i},n.prototype.getDifference=function(i){return new DimensionD(this.x-i.x,this.y-i.y)},n.prototype.getCopy=function(){return new n(this.x,this.y)},n.prototype.translate=function(i){return this.x+=i.width,this.y+=i.height,this},t.exports=n},function(t,e,r){"use strict";var n=r(2),i=r(10),a=r(0),s=r(7),l=r(3),u=r(1),h=r(13),f=r(12),d=r(11);function p(g,y,v){n.call(this,v),this.estimatedSize=i.MIN_VALUE,this.margin=a.DEFAULT_GRAPH_MARGIN,this.edges=[],this.nodes=[],this.isConnected=!1,this.parent=g,y!=null&&y instanceof s?this.graphManager=y:y!=null&&y instanceof Layout&&(this.graphManager=y.graphManager)}o(p,"LGraph"),p.prototype=Object.create(n.prototype);for(var m in n)p[m]=n[m];p.prototype.getNodes=function(){return this.nodes},p.prototype.getEdges=function(){return this.edges},p.prototype.getGraphManager=function(){return this.graphManager},p.prototype.getParent=function(){return this.parent},p.prototype.getLeft=function(){return this.left},p.prototype.getRight=function(){return this.right},p.prototype.getTop=function(){return this.top},p.prototype.getBottom=function(){return this.bottom},p.prototype.isConnected=function(){return this.isConnected},p.prototype.add=function(g,y,v){if(y==null&&v==null){var x=g;if(this.graphManager==null)throw"Graph has no graph mgr!";if(this.getNodes().indexOf(x)>-1)throw"Node already in graph!";return x.owner=this,this.getNodes().push(x),x}else{var b=g;if(!(this.getNodes().indexOf(y)>-1&&this.getNodes().indexOf(v)>-1))throw"Source or target not in graph!";if(!(y.owner==v.owner&&y.owner==this))throw"Both owners must be this graph!";return y.owner!=v.owner?null:(b.source=y,b.target=v,b.isInterGraph=!1,this.getEdges().push(b),y.edges.push(b),v!=y&&v.edges.push(b),b)}},p.prototype.remove=function(g){var y=g;if(g instanceof l){if(y==null)throw"Node is null!";if(!(y.owner!=null&&y.owner==this))throw"Owner graph is invalid!";if(this.graphManager==null)throw"Owner graph manager is invalid!";for(var v=y.edges.slice(),x,b=v.length,w=0;w-1&&E>-1))throw"Source and/or target doesn't know this edge!";x.source.edges.splice(T,1),x.target!=x.source&&x.target.edges.splice(E,1);var S=x.source.owner.getEdges().indexOf(x);if(S==-1)throw"Not in owner's edge list!";x.source.owner.getEdges().splice(S,1)}},p.prototype.updateLeftTop=function(){for(var g=i.MAX_VALUE,y=i.MAX_VALUE,v,x,b,w=this.getNodes(),S=w.length,T=0;Tv&&(g=v),y>x&&(y=x)}return g==i.MAX_VALUE?null:(w[0].getParent().paddingLeft!=null?b=w[0].getParent().paddingLeft:b=this.margin,this.left=y-b,this.top=g-b,new f(this.left,this.top))},p.prototype.updateBounds=function(g){for(var y=i.MAX_VALUE,v=-i.MAX_VALUE,x=i.MAX_VALUE,b=-i.MAX_VALUE,w,S,T,E,_,A=this.nodes,L=A.length,M=0;Mw&&(y=w),vT&&(x=T),bw&&(y=w),vT&&(x=T),b=this.nodes.length){var L=0;v.forEach(function(M){M.owner==g&&L++}),L==this.nodes.length&&(this.isConnected=!0)}},t.exports=p},function(t,e,r){"use strict";var n,i=r(1);function a(s){n=r(6),this.layout=s,this.graphs=[],this.edges=[]}o(a,"LGraphManager"),a.prototype.addRoot=function(){var s=this.layout.newGraph(),l=this.layout.newNode(null),u=this.add(s,l);return this.setRootGraph(u),this.rootGraph},a.prototype.add=function(s,l,u,h,f){if(u==null&&h==null&&f==null){if(s==null)throw"Graph is null!";if(l==null)throw"Parent node is null!";if(this.graphs.indexOf(s)>-1)throw"Graph already in this graph mgr!";if(this.graphs.push(s),s.parent!=null)throw"Already has a parent!";if(l.child!=null)throw"Already has a child!";return s.parent=l,l.child=s,s}else{f=u,h=l,u=s;var d=h.getOwner(),p=f.getOwner();if(!(d!=null&&d.getGraphManager()==this))throw"Source not in this graph mgr!";if(!(p!=null&&p.getGraphManager()==this))throw"Target not in this graph mgr!";if(d==p)return u.isInterGraph=!1,d.add(u,h,f);if(u.isInterGraph=!0,u.source=h,u.target=f,this.edges.indexOf(u)>-1)throw"Edge already in inter-graph edge list!";if(this.edges.push(u),!(u.source!=null&&u.target!=null))throw"Edge source and/or target is null!";if(!(u.source.edges.indexOf(u)==-1&&u.target.edges.indexOf(u)==-1))throw"Edge already in source and/or target incidency list!";return u.source.edges.push(u),u.target.edges.push(u),u}},a.prototype.remove=function(s){if(s instanceof n){var l=s;if(l.getGraphManager()!=this)throw"Graph not in this graph mgr";if(!(l==this.rootGraph||l.parent!=null&&l.parent.graphManager==this))throw"Invalid parent node!";var u=[];u=u.concat(l.getEdges());for(var h,f=u.length,d=0;d=s.getRight()?l[0]+=Math.min(s.getX()-a.getX(),a.getRight()-s.getRight()):s.getX()<=a.getX()&&s.getRight()>=a.getRight()&&(l[0]+=Math.min(a.getX()-s.getX(),s.getRight()-a.getRight())),a.getY()<=s.getY()&&a.getBottom()>=s.getBottom()?l[1]+=Math.min(s.getY()-a.getY(),a.getBottom()-s.getBottom()):s.getY()<=a.getY()&&s.getBottom()>=a.getBottom()&&(l[1]+=Math.min(a.getY()-s.getY(),s.getBottom()-a.getBottom()));var f=Math.abs((s.getCenterY()-a.getCenterY())/(s.getCenterX()-a.getCenterX()));s.getCenterY()===a.getCenterY()&&s.getCenterX()===a.getCenterX()&&(f=1);var d=f*l[0],p=l[1]/f;l[0]d)return l[0]=u,l[1]=m,l[2]=f,l[3]=A,!1;if(hf)return l[0]=p,l[1]=h,l[2]=E,l[3]=d,!1;if(uf?(l[0]=y,l[1]=v,k=!0):(l[0]=g,l[1]=m,k=!0):C===D&&(u>f?(l[0]=p,l[1]=m,k=!0):(l[0]=x,l[1]=v,k=!0)),-O===D?f>u?(l[2]=_,l[3]=A,I=!0):(l[2]=E,l[3]=T,I=!0):O===D&&(f>u?(l[2]=S,l[3]=T,I=!0):(l[2]=L,l[3]=A,I=!0)),k&&I)return!1;if(u>f?h>d?(P=this.getCardinalDirection(C,D,4),F=this.getCardinalDirection(O,D,2)):(P=this.getCardinalDirection(-C,D,3),F=this.getCardinalDirection(-O,D,1)):h>d?(P=this.getCardinalDirection(-C,D,1),F=this.getCardinalDirection(-O,D,3)):(P=this.getCardinalDirection(C,D,2),F=this.getCardinalDirection(O,D,4)),!k)switch(P){case 1:$=m,B=u+-w/D,l[0]=B,l[1]=$;break;case 2:B=x,$=h+b*D,l[0]=B,l[1]=$;break;case 3:$=v,B=u+w/D,l[0]=B,l[1]=$;break;case 4:B=y,$=h+-b*D,l[0]=B,l[1]=$;break}if(!I)switch(F){case 1:Y=T,z=f+-N/D,l[2]=z,l[3]=Y;break;case 2:z=L,Y=d+M*D,l[2]=z,l[3]=Y;break;case 3:Y=A,z=f+N/D,l[2]=z,l[3]=Y;break;case 4:z=_,Y=d+-M*D,l[2]=z,l[3]=Y;break}}return!1},i.getCardinalDirection=function(a,s,l){return a>s?l:1+l%4},i.getIntersection=function(a,s,l,u){if(u==null)return this.getIntersection2(a,s,l);var h=a.x,f=a.y,d=s.x,p=s.y,m=l.x,g=l.y,y=u.x,v=u.y,x=void 0,b=void 0,w=void 0,S=void 0,T=void 0,E=void 0,_=void 0,A=void 0,L=void 0;return w=p-f,T=h-d,_=d*f-h*p,S=v-g,E=m-y,A=y*g-m*v,L=w*E-S*T,L===0?null:(x=(T*A-E*_)/L,b=(S*_-w*A)/L,new n(x,b))},i.angleOfVector=function(a,s,l,u){var h=void 0;return a!==l?(h=Math.atan((u-s)/(l-a)),l=0){var v=(-m+Math.sqrt(m*m-4*p*g))/(2*p),x=(-m-Math.sqrt(m*m-4*p*g))/(2*p),b=null;return v>=0&&v<=1?[v]:x>=0&&x<=1?[x]:b}else return null},i.HALF_PI=.5*Math.PI,i.ONE_AND_HALF_PI=1.5*Math.PI,i.TWO_PI=2*Math.PI,i.THREE_PI=3*Math.PI,t.exports=i},function(t,e,r){"use strict";function n(){}o(n,"IMath"),n.sign=function(i){return i>0?1:i<0?-1:0},n.floor=function(i){return i<0?Math.ceil(i):Math.floor(i)},n.ceil=function(i){return i<0?Math.floor(i):Math.ceil(i)},t.exports=n},function(t,e,r){"use strict";function n(){}o(n,"Integer"),n.MAX_VALUE=2147483647,n.MIN_VALUE=-2147483648,t.exports=n},function(t,e,r){"use strict";var n=function(){function h(f,d){for(var p=0;p"u"?"undefined":n(a);return a==null||s!="object"&&s!="function"},t.exports=i},function(t,e,r){"use strict";function n(m){if(Array.isArray(m)){for(var g=0,y=Array(m.length);g0&&g;){for(w.push(T[0]);w.length>0&&g;){var E=w[0];w.splice(0,1),b.add(E);for(var _=E.getEdges(),x=0;x<_.length;x++){var A=_[x].getOtherEnd(E);if(S.get(E)!=A)if(!b.has(A))w.push(A),S.set(A,E);else{g=!1;break}}}if(!g)m=[];else{var L=[].concat(n(b));m.push(L);for(var x=0;x-1&&T.splice(N,1)}b=new Set,S=new Map}}return m},p.prototype.createDummyNodesForBendpoints=function(m){for(var g=[],y=m.source,v=this.graphManager.calcLowestCommonAncestor(m.source,m.target),x=0;x0){for(var v=this.edgeToDummyNodes.get(y),x=0;x=0&&g.splice(A,1);var L=S.getNeighborsList();L.forEach(function(k){if(y.indexOf(k)<0){var I=v.get(k),C=I-1;C==1&&E.push(k),v.set(k,C)}})}y=y.concat(E),(g.length==1||g.length==2)&&(x=!0,b=g[0])}return b},p.prototype.setGraphManager=function(m){this.graphManager=m},t.exports=p},function(t,e,r){"use strict";function n(){}o(n,"RandomSeed"),n.seed=1,n.x=0,n.nextDouble=function(){return n.x=Math.sin(n.seed++)*1e4,n.x-Math.floor(n.x)},t.exports=n},function(t,e,r){"use strict";var n=r(5);function i(a,s){this.lworldOrgX=0,this.lworldOrgY=0,this.ldeviceOrgX=0,this.ldeviceOrgY=0,this.lworldExtX=1,this.lworldExtY=1,this.ldeviceExtX=1,this.ldeviceExtY=1}o(i,"Transform"),i.prototype.getWorldOrgX=function(){return this.lworldOrgX},i.prototype.setWorldOrgX=function(a){this.lworldOrgX=a},i.prototype.getWorldOrgY=function(){return this.lworldOrgY},i.prototype.setWorldOrgY=function(a){this.lworldOrgY=a},i.prototype.getWorldExtX=function(){return this.lworldExtX},i.prototype.setWorldExtX=function(a){this.lworldExtX=a},i.prototype.getWorldExtY=function(){return this.lworldExtY},i.prototype.setWorldExtY=function(a){this.lworldExtY=a},i.prototype.getDeviceOrgX=function(){return this.ldeviceOrgX},i.prototype.setDeviceOrgX=function(a){this.ldeviceOrgX=a},i.prototype.getDeviceOrgY=function(){return this.ldeviceOrgY},i.prototype.setDeviceOrgY=function(a){this.ldeviceOrgY=a},i.prototype.getDeviceExtX=function(){return this.ldeviceExtX},i.prototype.setDeviceExtX=function(a){this.ldeviceExtX=a},i.prototype.getDeviceExtY=function(){return this.ldeviceExtY},i.prototype.setDeviceExtY=function(a){this.ldeviceExtY=a},i.prototype.transformX=function(a){var s=0,l=this.lworldExtX;return l!=0&&(s=this.ldeviceOrgX+(a-this.lworldOrgX)*this.ldeviceExtX/l),s},i.prototype.transformY=function(a){var s=0,l=this.lworldExtY;return l!=0&&(s=this.ldeviceOrgY+(a-this.lworldOrgY)*this.ldeviceExtY/l),s},i.prototype.inverseTransformX=function(a){var s=0,l=this.ldeviceExtX;return l!=0&&(s=this.lworldOrgX+(a-this.ldeviceOrgX)*this.lworldExtX/l),s},i.prototype.inverseTransformY=function(a){var s=0,l=this.ldeviceExtY;return l!=0&&(s=this.lworldOrgY+(a-this.ldeviceOrgY)*this.lworldExtY/l),s},i.prototype.inverseTransformPoint=function(a){var s=new n(this.inverseTransformX(a.x),this.inverseTransformY(a.y));return s},t.exports=i},function(t,e,r){"use strict";function n(d){if(Array.isArray(d)){for(var p=0,m=Array(d.length);pa.ADAPTATION_LOWER_NODE_LIMIT&&(this.coolingFactor=Math.max(this.coolingFactor*a.COOLING_ADAPTATION_FACTOR,this.coolingFactor-(d-a.ADAPTATION_LOWER_NODE_LIMIT)/(a.ADAPTATION_UPPER_NODE_LIMIT-a.ADAPTATION_LOWER_NODE_LIMIT)*this.coolingFactor*(1-a.COOLING_ADAPTATION_FACTOR))),this.maxNodeDisplacement=a.MAX_NODE_DISPLACEMENT_INCREMENTAL):(d>a.ADAPTATION_LOWER_NODE_LIMIT?this.coolingFactor=Math.max(a.COOLING_ADAPTATION_FACTOR,1-(d-a.ADAPTATION_LOWER_NODE_LIMIT)/(a.ADAPTATION_UPPER_NODE_LIMIT-a.ADAPTATION_LOWER_NODE_LIMIT)*(1-a.COOLING_ADAPTATION_FACTOR)):this.coolingFactor=1,this.initialCoolingFactor=this.coolingFactor,this.maxNodeDisplacement=a.MAX_NODE_DISPLACEMENT),this.maxIterations=Math.max(this.getAllNodes().length*5,this.maxIterations),this.displacementThresholdPerNode=3*a.DEFAULT_EDGE_LENGTH/100,this.totalDisplacementThreshold=this.displacementThresholdPerNode*this.getAllNodes().length,this.repulsionRange=this.calcRepulsionRange()},h.prototype.calcSpringForces=function(){for(var d=this.getAllEdges(),p,m=0;m0&&arguments[0]!==void 0?arguments[0]:!0,p=arguments.length>1&&arguments[1]!==void 0?arguments[1]:!1,m,g,y,v,x=this.getAllNodes(),b;if(this.useFRGridVariant)for(this.totalIterations%a.GRID_CALCULATION_CHECK_PERIOD==1&&d&&this.updateGrid(),b=new Set,m=0;mw||b>w)&&(d.gravitationForceX=-this.gravityConstant*y,d.gravitationForceY=-this.gravityConstant*v)):(w=p.getEstimatedSize()*this.compoundGravityRangeFactor,(x>w||b>w)&&(d.gravitationForceX=-this.gravityConstant*y*this.compoundGravityConstant,d.gravitationForceY=-this.gravityConstant*v*this.compoundGravityConstant))},h.prototype.isConverged=function(){var d,p=!1;return this.totalIterations>this.maxIterations/3&&(p=Math.abs(this.totalDisplacement-this.oldTotalDisplacement)<2),d=this.totalDisplacement=x.length||w>=x[0].length)){for(var S=0;Sh},"_defaultCompareFunction")}]),l}();t.exports=s},function(t,e,r){"use strict";function n(){}o(n,"SVD"),n.svd=function(i){this.U=null,this.V=null,this.s=null,this.m=0,this.n=0,this.m=i.length,this.n=i[0].length;var a=Math.min(this.m,this.n);this.s=function(it){for(var dt=[];it-- >0;)dt.push(0);return dt}(Math.min(this.m+1,this.n)),this.U=function(it){var dt=o(function lt(It){if(It.length==0)return 0;for(var mt=[],St=0;St0;)dt.push(0);return dt}(this.n),l=function(it){for(var dt=[];it-- >0;)dt.push(0);return dt}(this.m),u=!0,h=!0,f=Math.min(this.m-1,this.n),d=Math.max(0,Math.min(this.n-2,this.m)),p=0;p=0;D--)if(this.s[D]!==0){for(var P=D+1;P=0;X--){if(function(it,dt){return it&&dt}(X0;){var ue=void 0,te=void 0;for(ue=I-2;ue>=-1&&ue!==-1;ue--)if(Math.abs(s[ue])<=ce+se*(Math.abs(this.s[ue])+Math.abs(this.s[ue+1]))){s[ue]=0;break}if(ue===I-2)te=4;else{var De=void 0;for(De=I-1;De>=ue&&De!==ue;De--){var oe=(De!==I?Math.abs(s[De]):0)+(De!==ue+1?Math.abs(s[De-1]):0);if(Math.abs(this.s[De])<=ce+se*oe){this.s[De]=0;break}}De===ue?te=3:De===I-1?te=1:(te=2,ue=De)}switch(ue++,te){case 1:{var ke=s[I-2];s[I-2]=0;for(var Ie=I-2;Ie>=ue;Ie--){var Se=n.hypot(this.s[Ie],ke),Ue=this.s[Ie]/Se,Pe=ke/Se;if(this.s[Ie]=Se,Ie!==ue&&(ke=-Pe*s[Ie-1],s[Ie-1]=Ue*s[Ie-1]),h)for(var _e=0;_e=this.s[ue+1]);){var je=this.s[ue];if(this.s[ue]=this.s[ue+1],this.s[ue+1]=je,h&&ueMath.abs(a)?(s=a/i,s=Math.abs(i)*Math.sqrt(1+s*s)):a!=0?(s=i/a,s=Math.abs(a)*Math.sqrt(1+s*s)):s=0,s},t.exports=n},function(t,e,r){"use strict";var n=function(){function s(l,u){for(var h=0;h2&&arguments[2]!==void 0?arguments[2]:1,f=arguments.length>3&&arguments[3]!==void 0?arguments[3]:-1,d=arguments.length>4&&arguments[4]!==void 0?arguments[4]:-1;i(this,s),this.sequence1=l,this.sequence2=u,this.match_score=h,this.mismatch_penalty=f,this.gap_penalty=d,this.iMax=l.length+1,this.jMax=u.length+1,this.grid=new Array(this.iMax);for(var p=0;p=0;l--){var u=this.listeners[l];u.event===a&&u.callback===s&&this.listeners.splice(l,1)}},i.emit=function(a,s){for(var l=0;l{"use strict";o(function(e,r){typeof vb=="object"&&typeof QB=="object"?QB.exports=r(KB()):typeof define=="function"&&define.amd?define(["layout-base"],r):typeof vb=="object"?vb.coseBase=r(KB()):e.coseBase=r(e.layoutBase)},"webpackUniversalModuleDefinition")(vb,function(t){return(()=>{"use strict";var e={45:(a,s,l)=>{var u={};u.layoutBase=l(551),u.CoSEConstants=l(806),u.CoSEEdge=l(767),u.CoSEGraph=l(880),u.CoSEGraphManager=l(578),u.CoSELayout=l(765),u.CoSENode=l(991),u.ConstraintHandler=l(902),a.exports=u},806:(a,s,l)=>{var u=l(551).FDLayoutConstants;function h(){}o(h,"CoSEConstants");for(var f in u)h[f]=u[f];h.DEFAULT_USE_MULTI_LEVEL_SCALING=!1,h.DEFAULT_RADIAL_SEPARATION=u.DEFAULT_EDGE_LENGTH,h.DEFAULT_COMPONENT_SEPERATION=60,h.TILE=!0,h.TILING_PADDING_VERTICAL=10,h.TILING_PADDING_HORIZONTAL=10,h.TRANSFORM_ON_CONSTRAINT_HANDLING=!0,h.ENFORCE_CONSTRAINTS=!0,h.APPLY_LAYOUT=!0,h.RELAX_MOVEMENT_ON_CONSTRAINTS=!0,h.TREE_REDUCTION_ON_INCREMENTAL=!0,h.PURE_INCREMENTAL=h.DEFAULT_INCREMENTAL,a.exports=h},767:(a,s,l)=>{var u=l(551).FDLayoutEdge;function h(d,p,m){u.call(this,d,p,m)}o(h,"CoSEEdge"),h.prototype=Object.create(u.prototype);for(var f in u)h[f]=u[f];a.exports=h},880:(a,s,l)=>{var u=l(551).LGraph;function h(d,p,m){u.call(this,d,p,m)}o(h,"CoSEGraph"),h.prototype=Object.create(u.prototype);for(var f in u)h[f]=u[f];a.exports=h},578:(a,s,l)=>{var u=l(551).LGraphManager;function h(d){u.call(this,d)}o(h,"CoSEGraphManager"),h.prototype=Object.create(u.prototype);for(var f in u)h[f]=u[f];a.exports=h},765:(a,s,l)=>{var u=l(551).FDLayout,h=l(578),f=l(880),d=l(991),p=l(767),m=l(806),g=l(902),y=l(551).FDLayoutConstants,v=l(551).LayoutConstants,x=l(551).Point,b=l(551).PointD,w=l(551).DimensionD,S=l(551).Layout,T=l(551).Integer,E=l(551).IGeometry,_=l(551).LGraph,A=l(551).Transform,L=l(551).LinkedList;function M(){u.call(this),this.toBeTiled={},this.constraints={}}o(M,"CoSELayout"),M.prototype=Object.create(u.prototype);for(var N in u)M[N]=u[N];M.prototype.newGraphManager=function(){var k=new h(this);return this.graphManager=k,k},M.prototype.newGraph=function(k){return new f(null,this.graphManager,k)},M.prototype.newNode=function(k){return new d(this.graphManager,k)},M.prototype.newEdge=function(k){return new p(null,null,k)},M.prototype.initParameters=function(){u.prototype.initParameters.call(this,arguments),this.isSubLayout||(m.DEFAULT_EDGE_LENGTH<10?this.idealEdgeLength=10:this.idealEdgeLength=m.DEFAULT_EDGE_LENGTH,this.useSmartIdealEdgeLengthCalculation=m.DEFAULT_USE_SMART_IDEAL_EDGE_LENGTH_CALCULATION,this.gravityConstant=y.DEFAULT_GRAVITY_STRENGTH,this.compoundGravityConstant=y.DEFAULT_COMPOUND_GRAVITY_STRENGTH,this.gravityRangeFactor=y.DEFAULT_GRAVITY_RANGE_FACTOR,this.compoundGravityRangeFactor=y.DEFAULT_COMPOUND_GRAVITY_RANGE_FACTOR,this.prunedNodesAll=[],this.growTreeIterations=0,this.afterGrowthIterations=0,this.isTreeGrowing=!1,this.isGrowthFinished=!1)},M.prototype.initSpringEmbedder=function(){u.prototype.initSpringEmbedder.call(this),this.coolingCycle=0,this.maxCoolingCycle=this.maxIterations/y.CONVERGENCE_CHECK_PERIOD,this.finalTemperature=.04,this.coolingAdjuster=1},M.prototype.layout=function(){var k=v.DEFAULT_CREATE_BENDS_AS_NEEDED;return k&&(this.createBendpoints(),this.graphManager.resetAllEdges()),this.level=0,this.classicLayout()},M.prototype.classicLayout=function(){if(this.nodesWithGravity=this.calculateNodesToApplyGravitationTo(),this.graphManager.setAllNodesToApplyGravitation(this.nodesWithGravity),this.calcNoOfChildrenForAllNodes(),this.graphManager.calcLowestCommonAncestors(),this.graphManager.calcInclusionTreeDepths(),this.graphManager.getRoot().calcEstimatedSize(),this.calcIdealEdgeLengths(),this.incremental){if(m.TREE_REDUCTION_ON_INCREMENTAL){this.reduceTrees(),this.graphManager.resetAllNodesToApplyGravitation();var I=new Set(this.getAllNodes()),C=this.nodesWithGravity.filter(function(P){return I.has(P)});this.graphManager.setAllNodesToApplyGravitation(C)}}else{var k=this.getFlatForest();if(k.length>0)this.positionNodesRadially(k);else{this.reduceTrees(),this.graphManager.resetAllNodesToApplyGravitation();var I=new Set(this.getAllNodes()),C=this.nodesWithGravity.filter(function(O){return I.has(O)});this.graphManager.setAllNodesToApplyGravitation(C),this.positionNodesRandomly()}}return Object.keys(this.constraints).length>0&&(g.handleConstraints(this),this.initConstraintVariables()),this.initSpringEmbedder(),m.APPLY_LAYOUT&&this.runSpringEmbedder(),!0},M.prototype.tick=function(){if(this.totalIterations++,this.totalIterations===this.maxIterations&&!this.isTreeGrowing&&!this.isGrowthFinished)if(this.prunedNodesAll.length>0)this.isTreeGrowing=!0;else return!0;if(this.totalIterations%y.CONVERGENCE_CHECK_PERIOD==0&&!this.isTreeGrowing&&!this.isGrowthFinished){if(this.isConverged())if(this.prunedNodesAll.length>0)this.isTreeGrowing=!0;else return!0;this.coolingCycle++,this.layoutQuality==0?this.coolingAdjuster=this.coolingCycle:this.layoutQuality==1&&(this.coolingAdjuster=this.coolingCycle/3),this.coolingFactor=Math.max(this.initialCoolingFactor-Math.pow(this.coolingCycle,Math.log(100*(this.initialCoolingFactor-this.finalTemperature))/Math.log(this.maxCoolingCycle))/100*this.coolingAdjuster,this.finalTemperature),this.animationPeriod=Math.ceil(this.initialAnimationPeriod*Math.sqrt(this.coolingFactor))}if(this.isTreeGrowing){if(this.growTreeIterations%10==0)if(this.prunedNodesAll.length>0){this.graphManager.updateBounds(),this.updateGrid(),this.growTree(this.prunedNodesAll),this.graphManager.resetAllNodesToApplyGravitation();var k=new Set(this.getAllNodes()),I=this.nodesWithGravity.filter(function(D){return k.has(D)});this.graphManager.setAllNodesToApplyGravitation(I),this.graphManager.updateBounds(),this.updateGrid(),m.PURE_INCREMENTAL?this.coolingFactor=y.DEFAULT_COOLING_FACTOR_INCREMENTAL/2:this.coolingFactor=y.DEFAULT_COOLING_FACTOR_INCREMENTAL}else this.isTreeGrowing=!1,this.isGrowthFinished=!0;this.growTreeIterations++}if(this.isGrowthFinished){if(this.isConverged())return!0;this.afterGrowthIterations%10==0&&(this.graphManager.updateBounds(),this.updateGrid()),m.PURE_INCREMENTAL?this.coolingFactor=y.DEFAULT_COOLING_FACTOR_INCREMENTAL/2*((100-this.afterGrowthIterations)/100):this.coolingFactor=y.DEFAULT_COOLING_FACTOR_INCREMENTAL*((100-this.afterGrowthIterations)/100),this.afterGrowthIterations++}var C=!this.isTreeGrowing&&!this.isGrowthFinished,O=this.growTreeIterations%10==1&&this.isTreeGrowing||this.afterGrowthIterations%10==1&&this.isGrowthFinished;return this.totalDisplacement=0,this.graphManager.updateBounds(),this.calcSpringForces(),this.calcRepulsionForces(C,O),this.calcGravitationalForces(),this.moveNodes(),this.animate(),!1},M.prototype.getPositionsData=function(){for(var k=this.graphManager.getAllNodes(),I={},C=0;C0&&this.updateDisplacements();for(var C=0;C0&&(O.fixedNodeWeight=P)}}if(this.constraints.relativePlacementConstraint){var F=new Map,B=new Map;if(this.dummyToNodeForVerticalAlignment=new Map,this.dummyToNodeForHorizontalAlignment=new Map,this.fixedNodesOnHorizontal=new Set,this.fixedNodesOnVertical=new Set,this.fixedNodeSet.forEach(function(J){k.fixedNodesOnHorizontal.add(J),k.fixedNodesOnVertical.add(J)}),this.constraints.alignmentConstraint){if(this.constraints.alignmentConstraint.vertical)for(var $=this.constraints.alignmentConstraint.vertical,C=0;C<$.length;C++)this.dummyToNodeForVerticalAlignment.set("dummy"+C,[]),$[C].forEach(function(Z){F.set(Z,"dummy"+C),k.dummyToNodeForVerticalAlignment.get("dummy"+C).push(Z),k.fixedNodeSet.has(Z)&&k.fixedNodesOnHorizontal.add("dummy"+C)});if(this.constraints.alignmentConstraint.horizontal)for(var z=this.constraints.alignmentConstraint.horizontal,C=0;C=2*J.length/3;q--)Z=Math.floor(Math.random()*(q+1)),H=J[q],J[q]=J[Z],J[Z]=H;return J},this.nodesInRelativeHorizontal=[],this.nodesInRelativeVertical=[],this.nodeToRelativeConstraintMapHorizontal=new Map,this.nodeToRelativeConstraintMapVertical=new Map,this.nodeToTempPositionMapHorizontal=new Map,this.nodeToTempPositionMapVertical=new Map,this.constraints.relativePlacementConstraint.forEach(function(J){if(J.left){var Z=F.has(J.left)?F.get(J.left):J.left,H=F.has(J.right)?F.get(J.right):J.right;k.nodesInRelativeHorizontal.includes(Z)||(k.nodesInRelativeHorizontal.push(Z),k.nodeToRelativeConstraintMapHorizontal.set(Z,[]),k.dummyToNodeForVerticalAlignment.has(Z)?k.nodeToTempPositionMapHorizontal.set(Z,k.idToNodeMap.get(k.dummyToNodeForVerticalAlignment.get(Z)[0]).getCenterX()):k.nodeToTempPositionMapHorizontal.set(Z,k.idToNodeMap.get(Z).getCenterX())),k.nodesInRelativeHorizontal.includes(H)||(k.nodesInRelativeHorizontal.push(H),k.nodeToRelativeConstraintMapHorizontal.set(H,[]),k.dummyToNodeForVerticalAlignment.has(H)?k.nodeToTempPositionMapHorizontal.set(H,k.idToNodeMap.get(k.dummyToNodeForVerticalAlignment.get(H)[0]).getCenterX()):k.nodeToTempPositionMapHorizontal.set(H,k.idToNodeMap.get(H).getCenterX())),k.nodeToRelativeConstraintMapHorizontal.get(Z).push({right:H,gap:J.gap}),k.nodeToRelativeConstraintMapHorizontal.get(H).push({left:Z,gap:J.gap})}else{var q=B.has(J.top)?B.get(J.top):J.top,K=B.has(J.bottom)?B.get(J.bottom):J.bottom;k.nodesInRelativeVertical.includes(q)||(k.nodesInRelativeVertical.push(q),k.nodeToRelativeConstraintMapVertical.set(q,[]),k.dummyToNodeForHorizontalAlignment.has(q)?k.nodeToTempPositionMapVertical.set(q,k.idToNodeMap.get(k.dummyToNodeForHorizontalAlignment.get(q)[0]).getCenterY()):k.nodeToTempPositionMapVertical.set(q,k.idToNodeMap.get(q).getCenterY())),k.nodesInRelativeVertical.includes(K)||(k.nodesInRelativeVertical.push(K),k.nodeToRelativeConstraintMapVertical.set(K,[]),k.dummyToNodeForHorizontalAlignment.has(K)?k.nodeToTempPositionMapVertical.set(K,k.idToNodeMap.get(k.dummyToNodeForHorizontalAlignment.get(K)[0]).getCenterY()):k.nodeToTempPositionMapVertical.set(K,k.idToNodeMap.get(K).getCenterY())),k.nodeToRelativeConstraintMapVertical.get(q).push({bottom:K,gap:J.gap}),k.nodeToRelativeConstraintMapVertical.get(K).push({top:q,gap:J.gap})}});else{var Y=new Map,Q=new Map;this.constraints.relativePlacementConstraint.forEach(function(J){if(J.left){var Z=F.has(J.left)?F.get(J.left):J.left,H=F.has(J.right)?F.get(J.right):J.right;Y.has(Z)?Y.get(Z).push(H):Y.set(Z,[H]),Y.has(H)?Y.get(H).push(Z):Y.set(H,[Z])}else{var q=B.has(J.top)?B.get(J.top):J.top,K=B.has(J.bottom)?B.get(J.bottom):J.bottom;Q.has(q)?Q.get(q).push(K):Q.set(q,[K]),Q.has(K)?Q.get(K).push(q):Q.set(K,[q])}});var X=o(function(Z,H){var q=[],K=[],se=new L,ce=new Set,ue=0;return Z.forEach(function(te,De){if(!ce.has(De)){q[ue]=[],K[ue]=!1;var oe=De;for(se.push(oe),ce.add(oe),q[ue].push(oe);se.length!=0;){oe=se.shift(),H.has(oe)&&(K[ue]=!0);var ke=Z.get(oe);ke.forEach(function(Ie){ce.has(Ie)||(se.push(Ie),ce.add(Ie),q[ue].push(Ie))})}ue++}}),{components:q,isFixed:K}},"constructComponents"),ie=X(Y,k.fixedNodesOnHorizontal);this.componentsOnHorizontal=ie.components,this.fixedComponentsOnHorizontal=ie.isFixed;var j=X(Q,k.fixedNodesOnVertical);this.componentsOnVertical=j.components,this.fixedComponentsOnVertical=j.isFixed}}},M.prototype.updateDisplacements=function(){var k=this;if(this.constraints.fixedNodeConstraint&&this.constraints.fixedNodeConstraint.forEach(function(j){var J=k.idToNodeMap.get(j.nodeId);J.displacementX=0,J.displacementY=0}),this.constraints.alignmentConstraint){if(this.constraints.alignmentConstraint.vertical)for(var I=this.constraints.alignmentConstraint.vertical,C=0;C1){var B;for(B=0;BO&&(O=Math.floor(F.y)),P=Math.floor(F.x+m.DEFAULT_COMPONENT_SEPERATION)}this.transform(new b(v.WORLD_CENTER_X-F.x/2,v.WORLD_CENTER_Y-F.y/2))},M.radialLayout=function(k,I,C){var O=Math.max(this.maxDiagonalInTree(k),m.DEFAULT_RADIAL_SEPARATION);M.branchRadialLayout(I,null,0,359,0,O);var D=_.calculateBounds(k),P=new A;P.setDeviceOrgX(D.getMinX()),P.setDeviceOrgY(D.getMinY()),P.setWorldOrgX(C.x),P.setWorldOrgY(C.y);for(var F=0;F1;){var q=H[0];H.splice(0,1);var K=X.indexOf(q);K>=0&&X.splice(K,1),J--,ie--}I!=null?Z=(X.indexOf(H[0])+1)%J:Z=0;for(var se=Math.abs(O-C)/ie,ce=Z;j!=ie;ce=++ce%J){var ue=X[ce].getOtherEnd(k);if(ue!=I){var te=(C+j*se)%360,De=(te+se)%360;M.branchRadialLayout(ue,k,te,De,D+P,P),j++}}},M.maxDiagonalInTree=function(k){for(var I=T.MIN_VALUE,C=0;CI&&(I=D)}return I},M.prototype.calcRepulsionRange=function(){return 2*(this.level+1)*this.idealEdgeLength},M.prototype.groupZeroDegreeMembers=function(){var k=this,I={};this.memberGroups={},this.idToDummyNode={};for(var C=[],O=this.graphManager.getAllNodes(),D=0;D"u"&&(I[B]=[]),I[B]=I[B].concat(P)}Object.keys(I).forEach(function($){if(I[$].length>1){var z="DummyCompound_"+$;k.memberGroups[z]=I[$];var Y=I[$][0].getParent(),Q=new d(k.graphManager);Q.id=z,Q.paddingLeft=Y.paddingLeft||0,Q.paddingRight=Y.paddingRight||0,Q.paddingBottom=Y.paddingBottom||0,Q.paddingTop=Y.paddingTop||0,k.idToDummyNode[z]=Q;var X=k.getGraphManager().add(k.newGraph(),Q),ie=Y.getChild();ie.add(Q);for(var j=0;jD?(O.rect.x-=(O.labelWidth-D)/2,O.setWidth(O.labelWidth),O.labelMarginLeft=(O.labelWidth-D)/2):O.labelPosHorizontal=="right"&&O.setWidth(D+O.labelWidth)),O.labelHeight&&(O.labelPosVertical=="top"?(O.rect.y-=O.labelHeight,O.setHeight(P+O.labelHeight),O.labelMarginTop=O.labelHeight):O.labelPosVertical=="center"&&O.labelHeight>P?(O.rect.y-=(O.labelHeight-P)/2,O.setHeight(O.labelHeight),O.labelMarginTop=(O.labelHeight-P)/2):O.labelPosVertical=="bottom"&&O.setHeight(P+O.labelHeight))}})},M.prototype.repopulateCompounds=function(){for(var k=this.compoundOrder.length-1;k>=0;k--){var I=this.compoundOrder[k],C=I.id,O=I.paddingLeft,D=I.paddingTop,P=I.labelMarginLeft,F=I.labelMarginTop;this.adjustLocations(this.tiledMemberPack[C],I.rect.x,I.rect.y,O,D,P,F)}},M.prototype.repopulateZeroDegreeMembers=function(){var k=this,I=this.tiledZeroDegreePack;Object.keys(I).forEach(function(C){var O=k.idToDummyNode[C],D=O.paddingLeft,P=O.paddingTop,F=O.labelMarginLeft,B=O.labelMarginTop;k.adjustLocations(I[C],O.rect.x,O.rect.y,D,P,F,B)})},M.prototype.getToBeTiled=function(k){var I=k.id;if(this.toBeTiled[I]!=null)return this.toBeTiled[I];var C=k.getChild();if(C==null)return this.toBeTiled[I]=!1,!1;for(var O=C.getNodes(),D=0;D0)return this.toBeTiled[I]=!1,!1;if(P.getChild()==null){this.toBeTiled[P.id]=!1;continue}if(!this.getToBeTiled(P))return this.toBeTiled[I]=!1,!1}return this.toBeTiled[I]=!0,!0},M.prototype.getNodeDegree=function(k){for(var I=k.id,C=k.getEdges(),O=0,D=0;DY&&(Y=X.rect.height)}C+=Y+k.verticalPadding}},M.prototype.tileCompoundMembers=function(k,I){var C=this;this.tiledMemberPack=[],Object.keys(k).forEach(function(O){var D=I[O];if(C.tiledMemberPack[O]=C.tileNodes(k[O],D.paddingLeft+D.paddingRight),D.rect.width=C.tiledMemberPack[O].width,D.rect.height=C.tiledMemberPack[O].height,D.setCenter(C.tiledMemberPack[O].centerX,C.tiledMemberPack[O].centerY),D.labelMarginLeft=0,D.labelMarginTop=0,m.NODE_DIMENSIONS_INCLUDE_LABELS){var P=D.rect.width,F=D.rect.height;D.labelWidth&&(D.labelPosHorizontal=="left"?(D.rect.x-=D.labelWidth,D.setWidth(P+D.labelWidth),D.labelMarginLeft=D.labelWidth):D.labelPosHorizontal=="center"&&D.labelWidth>P?(D.rect.x-=(D.labelWidth-P)/2,D.setWidth(D.labelWidth),D.labelMarginLeft=(D.labelWidth-P)/2):D.labelPosHorizontal=="right"&&D.setWidth(P+D.labelWidth)),D.labelHeight&&(D.labelPosVertical=="top"?(D.rect.y-=D.labelHeight,D.setHeight(F+D.labelHeight),D.labelMarginTop=D.labelHeight):D.labelPosVertical=="center"&&D.labelHeight>F?(D.rect.y-=(D.labelHeight-F)/2,D.setHeight(D.labelHeight),D.labelMarginTop=(D.labelHeight-F)/2):D.labelPosVertical=="bottom"&&D.setHeight(F+D.labelHeight))}})},M.prototype.tileNodes=function(k,I){var C=this.tileNodesByFavoringDim(k,I,!0),O=this.tileNodesByFavoringDim(k,I,!1),D=this.getOrgRatio(C),P=this.getOrgRatio(O),F;return PB&&(B=j.getWidth())});var $=P/D,z=F/D,Y=Math.pow(C-O,2)+4*($+O)*(z+C)*D,Q=(O-C+Math.sqrt(Y))/(2*($+O)),X;I?(X=Math.ceil(Q),X==Q&&X++):X=Math.floor(Q);var ie=X*($+O)-O;return B>ie&&(ie=B),ie+=O*2,ie},M.prototype.tileNodesByFavoringDim=function(k,I,C){var O=m.TILING_PADDING_VERTICAL,D=m.TILING_PADDING_HORIZONTAL,P=m.TILING_COMPARE_BY,F={rows:[],rowWidth:[],rowHeight:[],width:0,height:I,verticalPadding:O,horizontalPadding:D,centerX:0,centerY:0};P&&(F.idealRowWidth=this.calcIdealRowWidth(k,C));var B=o(function(J){return J.rect.width*J.rect.height},"getNodeArea"),$=o(function(J,Z){return B(Z)-B(J)},"areaCompareFcn");k.sort(function(j,J){var Z=$;return F.idealRowWidth?(Z=P,Z(j.id,J.id)):Z(j,J)});for(var z=0,Y=0,Q=0;Q0&&(F+=k.horizontalPadding),k.rowWidth[C]=F,k.width0&&(B+=k.verticalPadding);var $=0;B>k.rowHeight[C]&&($=k.rowHeight[C],k.rowHeight[C]=B,$=k.rowHeight[C]-$),k.height+=$,k.rows[C].push(I)},M.prototype.getShortestRowIndex=function(k){for(var I=-1,C=Number.MAX_VALUE,O=0;OC&&(I=O,C=k.rowWidth[O]);return 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u=l(551).FDLayoutNode,h=l(551).IMath;function f(p,m,g,y){u.call(this,p,m,g,y)}o(f,"CoSENode"),f.prototype=Object.create(u.prototype);for(var d in u)f[d]=u[d];f.prototype.calculateDisplacement=function(){var p=this.graphManager.getLayout();this.getChild()!=null&&this.fixedNodeWeight?(this.displacementX+=p.coolingFactor*(this.springForceX+this.repulsionForceX+this.gravitationForceX)/this.fixedNodeWeight,this.displacementY+=p.coolingFactor*(this.springForceY+this.repulsionForceY+this.gravitationForceY)/this.fixedNodeWeight):(this.displacementX+=p.coolingFactor*(this.springForceX+this.repulsionForceX+this.gravitationForceX)/this.noOfChildren,this.displacementY+=p.coolingFactor*(this.springForceY+this.repulsionForceY+this.gravitationForceY)/this.noOfChildren),Math.abs(this.displacementX)>p.coolingFactor*p.maxNodeDisplacement&&(this.displacementX=p.coolingFactor*p.maxNodeDisplacement*h.sign(this.displacementX)),Math.abs(this.displacementY)>p.coolingFactor*p.maxNodeDisplacement&&(this.displacementY=p.coolingFactor*p.maxNodeDisplacement*h.sign(this.displacementY)),this.child&&this.child.getNodes().length>0&&this.propogateDisplacementToChildren(this.displacementX,this.displacementY)},f.prototype.propogateDisplacementToChildren=function(p,m){for(var g=this.getChild().getNodes(),y,v=0;v{function u(g){if(Array.isArray(g)){for(var y=0,v=Array(g.length);y0){var Je=0;Ye.forEach(function(je){we=="horizontal"?(ye.set(je,x.has(je)?b[x.get(je)]:Ce.get(je)),Je+=ye.get(je)):(ye.set(je,x.has(je)?w[x.get(je)]:Ce.get(je)),Je+=ye.get(je))}),Je=Je/Ye.length,tt.forEach(function(je){Te.has(je)||ye.set(je,Je)})}else{var Ve=0;tt.forEach(function(je){we=="horizontal"?Ve+=x.has(je)?b[x.get(je)]:Ce.get(je):Ve+=x.has(je)?w[x.get(je)]:Ce.get(je)}),Ve=Ve/tt.length,tt.forEach(function(je){ye.set(je,Ve)})}});for(var Ze=o(function(){var Ye=ze.shift(),Je=ae.get(Ye);Je.forEach(function(Ve){if(ye.get(Ve.id)je&&(je=mt),Stkt&&(kt=St)}}catch(Qn){xt=!0,it=Qn}finally{try{!at&&dt.return&&dt.return()}finally{if(xt)throw it}}var gr=(Je+je)/2-(Ve+kt)/2,xn=!0,jt=!1,rn=void 0;try{for(var Er=tt[Symbol.iterator](),Kn;!(xn=(Kn=Er.next()).done);xn=!0){var hn=Kn.value;ye.set(hn,ye.get(hn)+gr)}}catch(Qn){jt=!0,rn=Qn}finally{try{!xn&&Er.return&&Er.return()}finally{if(jt)throw rn}}})}return ye},"findAppropriatePositionForRelativePlacement"),N=o(function(ae){var we=0,Te=0,Ce=0,Ae=0;if(ae.forEach(function(He){He.left?b[x.get(He.left)]-b[x.get(He.right)]>=0?we++:Te++:w[x.get(He.top)]-w[x.get(He.bottom)]>=0?Ce++:Ae++}),we>Te&&Ce>Ae)for(var Ge=0;GeTe)for(var Me=0;MeAe)for(var ye=0;ye1)y.fixedNodeConstraint.forEach(function(ne,ae){O[ae]=[ne.position.x,ne.position.y],D[ae]=[b[x.get(ne.nodeId)],w[x.get(ne.nodeId)]]}),P=!0;else if(y.alignmentConstraint)(function(){var ne=0;if(y.alignmentConstraint.vertical){for(var ae=y.alignmentConstraint.vertical,we=o(function(ye){var He=new Set;ae[ye].forEach(function(gt){He.add(gt)});var ze=new Set([].concat(u(He)).filter(function(gt){return B.has(gt)})),Ze=void 0;ze.size>0?Ze=b[x.get(ze.values().next().value)]:Ze=L(He).x,ae[ye].forEach(function(gt){O[ne]=[Ze,w[x.get(gt)]],D[ne]=[b[x.get(gt)],w[x.get(gt)]],ne++})},"_loop2"),Te=0;Te0?Ze=b[x.get(ze.values().next().value)]:Ze=L(He).y,Ce[ye].forEach(function(gt){O[ne]=[b[x.get(gt)],Ze],D[ne]=[b[x.get(gt)],w[x.get(gt)]],ne++})},"_loop3"),Ge=0;GeQ&&(Q=Y[ie].length,X=ie);if(Q0){var Ue={x:0,y:0};y.fixedNodeConstraint.forEach(function(ne,ae){var we={x:b[x.get(ne.nodeId)],y:w[x.get(ne.nodeId)]},Te=ne.position,Ce=A(Te,we);Ue.x+=Ce.x,Ue.y+=Ce.y}),Ue.x/=y.fixedNodeConstraint.length,Ue.y/=y.fixedNodeConstraint.length,b.forEach(function(ne,ae){b[ae]+=Ue.x}),w.forEach(function(ne,ae){w[ae]+=Ue.y}),y.fixedNodeConstraint.forEach(function(ne){b[x.get(ne.nodeId)]=ne.position.x,w[x.get(ne.nodeId)]=ne.position.y})}if(y.alignmentConstraint){if(y.alignmentConstraint.vertical)for(var Pe=y.alignmentConstraint.vertical,_e=o(function(ae){var we=new Set;Pe[ae].forEach(function(Ae){we.add(Ae)});var Te=new Set([].concat(u(we)).filter(function(Ae){return B.has(Ae)})),Ce=void 0;Te.size>0?Ce=b[x.get(Te.values().next().value)]:Ce=L(we).x,we.forEach(function(Ae){B.has(Ae)||(b[x.get(Ae)]=Ce)})},"_loop4"),me=0;me0?Ce=w[x.get(Te.values().next().value)]:Ce=L(we).y,we.forEach(function(Ae){B.has(Ae)||(w[x.get(Ae)]=Ce)})},"_loop5"),ge=0;ge{a.exports=t}},r={};function n(a){var s=r[a];if(s!==void 0)return s.exports;var l=r[a]={exports:{}};return e[a](l,l.exports,n),l.exports}o(n,"__webpack_require__");var i=n(45);return i})()})});var X1e=gi((xb,JB)=>{"use strict";o(function(e,r){typeof xb=="object"&&typeof JB=="object"?JB.exports=r(ZB()):typeof define=="function"&&define.amd?define(["cose-base"],r):typeof xb=="object"?xb.cytoscapeFcose=r(ZB()):e.cytoscapeFcose=r(e.coseBase)},"webpackUniversalModuleDefinition")(xb,function(t){return(()=>{"use strict";var e={658:a=>{a.exports=Object.assign!=null?Object.assign.bind(Object):function(s){for(var l=arguments.length,u=Array(l>1?l-1:0),h=1;h{var u=function(){function d(p,m){var g=[],y=!0,v=!1,x=void 0;try{for(var b=p[Symbol.iterator](),w;!(y=(w=b.next()).done)&&(g.push(w.value),!(m&&g.length===m));y=!0);}catch(S){v=!0,x=S}finally{try{!y&&b.return&&b.return()}finally{if(v)throw x}}return g}return o(d,"sliceIterator"),function(p,m){if(Array.isArray(p))return p;if(Symbol.iterator in Object(p))return d(p,m);throw new TypeError("Invalid attempt to destructure non-iterable instance")}}(),h=l(140).layoutBase.LinkedList,f={};f.getTopMostNodes=function(d){for(var p={},m=0;m0&&P.merge(z)});for(var F=0;F1){w=x[0],S=w.connectedEdges().length,x.forEach(function(D){D.connectedEdges().length0&&g.set("dummy"+(g.size+1),_),A},f.relocateComponent=function(d,p,m){if(!m.fixedNodeConstraint){var g=Number.POSITIVE_INFINITY,y=Number.NEGATIVE_INFINITY,v=Number.POSITIVE_INFINITY,x=Number.NEGATIVE_INFINITY;if(m.quality=="draft"){var b=!0,w=!1,S=void 0;try{for(var T=p.nodeIndexes[Symbol.iterator](),E;!(b=(E=T.next()).done);b=!0){var _=E.value,A=u(_,2),L=A[0],M=A[1],N=m.cy.getElementById(L);if(N){var k=N.boundingBox(),I=p.xCoords[M]-k.w/2,C=p.xCoords[M]+k.w/2,O=p.yCoords[M]-k.h/2,D=p.yCoords[M]+k.h/2;Iy&&(y=C),Ox&&(x=D)}}}catch(z){w=!0,S=z}finally{try{!b&&T.return&&T.return()}finally{if(w)throw S}}var P=d.x-(y+g)/2,F=d.y-(x+v)/2;p.xCoords=p.xCoords.map(function(z){return z+P}),p.yCoords=p.yCoords.map(function(z){return z+F})}else{Object.keys(p).forEach(function(z){var Y=p[z],Q=Y.getRect().x,X=Y.getRect().x+Y.getRect().width,ie=Y.getRect().y,j=Y.getRect().y+Y.getRect().height;Qy&&(y=X),iex&&(x=j)});var B=d.x-(y+g)/2,$=d.y-(x+v)/2;Object.keys(p).forEach(function(z){var Y=p[z];Y.setCenter(Y.getCenterX()+B,Y.getCenterY()+$)})}}},f.calcBoundingBox=function(d,p,m,g){for(var y=Number.MAX_SAFE_INTEGER,v=Number.MIN_SAFE_INTEGER,x=Number.MAX_SAFE_INTEGER,b=Number.MIN_SAFE_INTEGER,w=void 0,S=void 0,T=void 0,E=void 0,_=d.descendants().not(":parent"),A=_.length,L=0;Lw&&(y=w),vT&&(x=T),b{var u=l(548),h=l(140).CoSELayout,f=l(140).CoSENode,d=l(140).layoutBase.PointD,p=l(140).layoutBase.DimensionD,m=l(140).layoutBase.LayoutConstants,g=l(140).layoutBase.FDLayoutConstants,y=l(140).CoSEConstants,v=o(function(b,w){var S=b.cy,T=b.eles,E=T.nodes(),_=T.edges(),A=void 0,L=void 0,M=void 0,N={};b.randomize&&(A=w.nodeIndexes,L=w.xCoords,M=w.yCoords);var k=o(function(z){return typeof z=="function"},"isFn"),I=o(function(z,Y){return k(z)?z(Y):z},"optFn"),C=u.calcParentsWithoutChildren(S,T),O=o(function $(z,Y,Q,X){for(var ie=Y.length,j=0;j0){var se=void 0;se=Q.getGraphManager().add(Q.newGraph(),H),$(se,Z,Q,X)}}},"processChildrenList"),D=o(function(z,Y,Q){for(var X=0,ie=0,j=0;j0?y.DEFAULT_EDGE_LENGTH=g.DEFAULT_EDGE_LENGTH=X/ie:k(b.idealEdgeLength)?y.DEFAULT_EDGE_LENGTH=g.DEFAULT_EDGE_LENGTH=50:y.DEFAULT_EDGE_LENGTH=g.DEFAULT_EDGE_LENGTH=b.idealEdgeLength,y.MIN_REPULSION_DIST=g.MIN_REPULSION_DIST=g.DEFAULT_EDGE_LENGTH/10,y.DEFAULT_RADIAL_SEPARATION=g.DEFAULT_EDGE_LENGTH)},"processEdges"),P=o(function(z,Y){Y.fixedNodeConstraint&&(z.constraints.fixedNodeConstraint=Y.fixedNodeConstraint),Y.alignmentConstraint&&(z.constraints.alignmentConstraint=Y.alignmentConstraint),Y.relativePlacementConstraint&&(z.constraints.relativePlacementConstraint=Y.relativePlacementConstraint)},"processConstraints");b.nestingFactor!=null&&(y.PER_LEVEL_IDEAL_EDGE_LENGTH_FACTOR=g.PER_LEVEL_IDEAL_EDGE_LENGTH_FACTOR=b.nestingFactor),b.gravity!=null&&(y.DEFAULT_GRAVITY_STRENGTH=g.DEFAULT_GRAVITY_STRENGTH=b.gravity),b.numIter!=null&&(y.MAX_ITERATIONS=g.MAX_ITERATIONS=b.numIter),b.gravityRange!=null&&(y.DEFAULT_GRAVITY_RANGE_FACTOR=g.DEFAULT_GRAVITY_RANGE_FACTOR=b.gravityRange),b.gravityCompound!=null&&(y.DEFAULT_COMPOUND_GRAVITY_STRENGTH=g.DEFAULT_COMPOUND_GRAVITY_STRENGTH=b.gravityCompound),b.gravityRangeCompound!=null&&(y.DEFAULT_COMPOUND_GRAVITY_RANGE_FACTOR=g.DEFAULT_COMPOUND_GRAVITY_RANGE_FACTOR=b.gravityRangeCompound),b.initialEnergyOnIncremental!=null&&(y.DEFAULT_COOLING_FACTOR_INCREMENTAL=g.DEFAULT_COOLING_FACTOR_INCREMENTAL=b.initialEnergyOnIncremental),b.tilingCompareBy!=null&&(y.TILING_COMPARE_BY=b.tilingCompareBy),b.quality=="proof"?m.QUALITY=2:m.QUALITY=0,y.NODE_DIMENSIONS_INCLUDE_LABELS=g.NODE_DIMENSIONS_INCLUDE_LABELS=m.NODE_DIMENSIONS_INCLUDE_LABELS=b.nodeDimensionsIncludeLabels,y.DEFAULT_INCREMENTAL=g.DEFAULT_INCREMENTAL=m.DEFAULT_INCREMENTAL=!b.randomize,y.ANIMATE=g.ANIMATE=m.ANIMATE=b.animate,y.TILE=b.tile,y.TILING_PADDING_VERTICAL=typeof b.tilingPaddingVertical=="function"?b.tilingPaddingVertical.call():b.tilingPaddingVertical,y.TILING_PADDING_HORIZONTAL=typeof b.tilingPaddingHorizontal=="function"?b.tilingPaddingHorizontal.call():b.tilingPaddingHorizontal,y.DEFAULT_INCREMENTAL=g.DEFAULT_INCREMENTAL=m.DEFAULT_INCREMENTAL=!0,y.PURE_INCREMENTAL=!b.randomize,m.DEFAULT_UNIFORM_LEAF_NODE_SIZES=b.uniformNodeDimensions,b.step=="transformed"&&(y.TRANSFORM_ON_CONSTRAINT_HANDLING=!0,y.ENFORCE_CONSTRAINTS=!1,y.APPLY_LAYOUT=!1),b.step=="enforced"&&(y.TRANSFORM_ON_CONSTRAINT_HANDLING=!1,y.ENFORCE_CONSTRAINTS=!0,y.APPLY_LAYOUT=!1),b.step=="cose"&&(y.TRANSFORM_ON_CONSTRAINT_HANDLING=!1,y.ENFORCE_CONSTRAINTS=!1,y.APPLY_LAYOUT=!0),b.step=="all"&&(b.randomize?y.TRANSFORM_ON_CONSTRAINT_HANDLING=!0:y.TRANSFORM_ON_CONSTRAINT_HANDLING=!1,y.ENFORCE_CONSTRAINTS=!0,y.APPLY_LAYOUT=!0),b.fixedNodeConstraint||b.alignmentConstraint||b.relativePlacementConstraint?y.TREE_REDUCTION_ON_INCREMENTAL=!1:y.TREE_REDUCTION_ON_INCREMENTAL=!0;var F=new h,B=F.newGraphManager();return O(B.addRoot(),u.getTopMostNodes(E),F,b),D(F,B,_),P(F,b),F.runLayout(),N},"coseLayout");a.exports={coseLayout:v}},212:(a,s,l)=>{var u=function(){function b(w,S){for(var T=0;T0)if(D){var B=d.getTopMostNodes(T.eles.nodes());if(k=d.connectComponents(E,T.eles,B),k.forEach(function(oe){var ke=oe.boundingBox();I.push({x:ke.x1+ke.w/2,y:ke.y1+ke.h/2})}),T.randomize&&k.forEach(function(oe){T.eles=oe,A.push(m(T))}),T.quality=="default"||T.quality=="proof"){var $=E.collection();if(T.tile){var z=new Map,Y=[],Q=[],X=0,ie={nodeIndexes:z,xCoords:Y,yCoords:Q},j=[];if(k.forEach(function(oe,ke){oe.edges().length==0&&(oe.nodes().forEach(function(Ie,Se){$.merge(oe.nodes()[Se]),Ie.isParent()||(ie.nodeIndexes.set(oe.nodes()[Se].id(),X++),ie.xCoords.push(oe.nodes()[0].position().x),ie.yCoords.push(oe.nodes()[0].position().y))}),j.push(ke))}),$.length>1){var J=$.boundingBox();I.push({x:J.x1+J.w/2,y:J.y1+J.h/2}),k.push($),A.push(ie);for(var Z=j.length-1;Z>=0;Z--)k.splice(j[Z],1),A.splice(j[Z],1),I.splice(j[Z],1)}}k.forEach(function(oe,ke){T.eles=oe,N.push(y(T,A[ke])),d.relocateComponent(I[ke],N[ke],T)})}else k.forEach(function(oe,ke){d.relocateComponent(I[ke],A[ke],T)});var H=new Set;if(k.length>1){var q=[],K=_.filter(function(oe){return oe.css("display")=="none"});k.forEach(function(oe,ke){var Ie=void 0;if(T.quality=="draft"&&(Ie=A[ke].nodeIndexes),oe.nodes().not(K).length>0){var Se={};Se.edges=[],Se.nodes=[];var Ue=void 0;oe.nodes().not(K).forEach(function(Pe){if(T.quality=="draft")if(!Pe.isParent())Ue=Ie.get(Pe.id()),Se.nodes.push({x:A[ke].xCoords[Ue]-Pe.boundingbox().w/2,y:A[ke].yCoords[Ue]-Pe.boundingbox().h/2,width:Pe.boundingbox().w,height:Pe.boundingbox().h});else{var _e=d.calcBoundingBox(Pe,A[ke].xCoords,A[ke].yCoords,Ie);Se.nodes.push({x:_e.topLeftX,y:_e.topLeftY,width:_e.width,height:_e.height})}else N[ke][Pe.id()]&&Se.nodes.push({x:N[ke][Pe.id()].getLeft(),y:N[ke][Pe.id()].getTop(),width:N[ke][Pe.id()].getWidth(),height:N[ke][Pe.id()].getHeight()})}),oe.edges().forEach(function(Pe){var _e=Pe.source(),me=Pe.target();if(_e.css("display")!="none"&&me.css("display")!="none")if(T.quality=="draft"){var W=Ie.get(_e.id()),fe=Ie.get(me.id()),ge=[],re=[];if(_e.isParent()){var he=d.calcBoundingBox(_e,A[ke].xCoords,A[ke].yCoords,Ie);ge.push(he.topLeftX+he.width/2),ge.push(he.topLeftY+he.height/2)}else ge.push(A[ke].xCoords[W]),ge.push(A[ke].yCoords[W]);if(me.isParent()){var ne=d.calcBoundingBox(me,A[ke].xCoords,A[ke].yCoords,Ie);re.push(ne.topLeftX+ne.width/2),re.push(ne.topLeftY+ne.height/2)}else re.push(A[ke].xCoords[fe]),re.push(A[ke].yCoords[fe]);Se.edges.push({startX:ge[0],startY:ge[1],endX:re[0],endY:re[1]})}else 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r={name:t.filename,buffer:t.input.slice(0,-1),position:t.position,line:t.line,column:t.position-t.lineStart};return r.snippet=Met(r),new $s(e,r)}o(ave,"generateError");function Gt(t,e){throw ave(t,e)}o(Gt,"throwError");function TC(t,e){t.onWarning&&t.onWarning.call(null,ave(t,e))}o(TC,"throwWarning");var Pye={YAML:o(function(e,r,n){var i,a,s;e.version!==null&&Gt(e,"duplication of %YAML directive"),n.length!==1&&Gt(e,"YAML directive accepts exactly one argument"),i=/^([0-9]+)\.([0-9]+)$/.exec(n[0]),i===null&&Gt(e,"ill-formed argument of the YAML directive"),a=parseInt(i[1],10),s=parseInt(i[2],10),a!==1&&Gt(e,"unacceptable YAML version of the document"),e.version=n[0],e.checkLineBreaks=s<2,s!==1&&s!==2&&TC(e,"unsupported YAML version of the document")},"handleYamlDirective"),TAG:o(function(e,r,n){var i,a;n.length!==2&&Gt(e,"TAG directive accepts exactly two arguments"),i=n[0],a=n[1],tve.test(i)||Gt(e,"ill-formed tag handle (first argument) of the TAG 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s=t.input.charCodeAt(++t.position);while(s!==0&&!qc(s));break}if(qc(s))break;for(r=t.position;s!==0&&!Vs(s);)s=t.input.charCodeAt(++t.position);i.push(t.input.slice(r,t.position))}s!==0&&fF(t),Bf.call(Pye,n)?Pye[n](t,n,i):TC(t,'unknown document directive "'+n+'"')}if(Ai(t,!0,-1),t.lineIndent===0&&t.input.charCodeAt(t.position)===45&&t.input.charCodeAt(t.position+1)===45&&t.input.charCodeAt(t.position+2)===45?(t.position+=3,Ai(t,!0,-1)):a&&Gt(t,"directives end mark is expected"),N1(t,t.lineIndent-1,wC,!1,!0),Ai(t,!0,-1),t.checkLineBreaks&&ztt.test(t.input.slice(e,t.position))&&TC(t,"non-ASCII line breaks are interpreted as content"),t.documents.push(t.result),t.position===t.lineStart&&CC(t)){t.input.charCodeAt(t.position)===46&&(t.position+=3,Ai(t,!0,-1));return}if(t.position"u"&&(r=e,e=null);var n=sve(t,r);if(typeof e!="function")return n;for(var i=0,a=n.length;i=55296&&r<=56319&&e+1=56320&&n<=57343)?(r-55296)*1024+n-56320+65536:r}o(kb,"codePointAt");function mve(t){var e=/^\n* /;return e.test(t)}o(mve,"needIndentIndicator");var gve=1,cF=2,yve=3,vve=4,L1=5;function Rrt(t,e,r,n,i,a,s,l){var u,h=0,f=null,d=!1,p=!1,m=n!==-1,g=-1,y=Lrt(kb(t,0))&&Drt(kb(t,t.length-1));if(e||s)for(u=0;u=65536?u+=2:u++){if(h=kb(t,u),!Ab(h))return L1;y=y&&$ye(h,f,l),f=h}else{for(u=0;u=65536?u+=2:u++){if(h=kb(t,u),h===Cb)d=!0,m&&(p=p||u-g-1>n&&t[g+1]!==" ",g=u);else if(!Ab(h))return L1;y=y&&$ye(h,f,l),f=h}p=p||m&&u-g-1>n&&t[g+1]!==" "}return!d&&!p?y&&!s&&!i(t)?gve:a===Sb?L1:cF:r>9&&mve(t)?L1:s?a===Sb?L1:cF:p?vve:yve}o(Rrt,"chooseScalarStyle");function Nrt(t,e,r,n,i){t.dump=function(){if(e.length===0)return t.quotingType===Sb?'""':"''";if(!t.noCompatMode&&(Trt.indexOf(e)!==-1||krt.test(e)))return t.quotingType===Sb?'"'+e+'"':"'"+e+"'";var a=t.indent*Math.max(1,r),s=t.lineWidth===-1?-1:Math.max(Math.min(t.lineWidth,40),t.lineWidth-a),l=n||t.flowLevel>-1&&r>=t.flowLevel;function u(h){return _rt(t,h)}switch(o(u,"testAmbiguity"),Rrt(e,l,t.indent,s,u,t.quotingType,t.forceQuotes&&!n,i)){case gve:return e;case cF:return"'"+e.replace(/'/g,"''")+"'";case yve:return"|"+Vye(e,t.indent)+Uye(zye(e,a));case vve:return">"+Vye(e,t.indent)+Uye(zye(Mrt(e,s),a));case L1:return'"'+Irt(e)+'"';default:throw new $s("impossible error: invalid scalar style")}}()}o(Nrt,"writeScalar");function Vye(t,e){var r=mve(t)?String(e):"",n=t[t.length-1]===` -`,i=n&&(t[t.length-2]===` -`||t===` -`),a=i?"+":n?"":"-";return r+a+` -`}o(Vye,"blockHeader");function Uye(t){return t[t.length-1]===` -`?t.slice(0,-1):t}o(Uye,"dropEndingNewline");function Mrt(t,e){for(var r=/(\n+)([^\n]*)/g,n=function(){var h=t.indexOf(` -`);return h=h!==-1?h:t.length,r.lastIndex=h,Hye(t.slice(0,h),e)}(),i=t[0]===` -`||t[0]===" ",a,s;s=r.exec(t);){var l=s[1],u=s[2];a=u[0]===" ",n+=l+(!i&&!a&&u!==""?` -`:"")+Hye(u,e),i=a}return n}o(Mrt,"foldString");function Hye(t,e){if(t===""||t[0]===" ")return t;for(var r=/ [^ ]/g,n,i=0,a,s=0,l=0,u="";n=r.exec(t);)l=n.index,l-i>e&&(a=s>i?s:l,u+=` -`+t.slice(i,a),i=a+1),s=l;return u+=` 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All rights reserved. diff --git a/docs/overrides/home.html b/docs/overrides/home.html new file mode 100644 index 00000000..a75656f5 --- /dev/null +++ b/docs/overrides/home.html @@ -0,0 +1,767 @@ +{% extends "main.html" %} + +{% block tabs %} + {{ super() }} + + + + +
    +
    +
    + + {% if config.theme.language == "zh" %} +

    分布式 AI 动态性能分析器

    + {% else %} +

    Dynamic Performance Profiler for Distributed AI

    + {% endif %} +
    +     +
    +
    +
    + + +
    +
    + {% if config.theme.language == "zh" %} +

    核心特性

    +

    零侵入分析 AI 工作负载，SQL 驱动的分析能力

    + {% else %} +

    Core Features

    +

    Zero-intrusion profiling for AI workloads with SQL-powered analytics

    + {% endif %} +
    +
    + 🔍 + {% if config.theme.language == "zh" %} +

    零侵入

    +

    无需修改代码即可附加到运行中的进程。无需插桩、无需重启、不中断工作流。

    + {% else %} +

    Zero Intrusion

    +

    Attach to running processes without code changes. No instrumentation, no restarts, no workflow disruption.

    + {% endif %} +
    +
    + 📊 + {% if config.theme.language == "zh" %} +

    SQL 分析

    +

    使用标准 SQL 查询性能数据。用熟悉的语法分析 torch 追踪、内存使用等。

    + {% else %} +

    SQL Analytics

    +

    Query performance data with standard SQL. Use familiar syntax to analyze torch traces, memory usage, and more.

    + {% endif %} +
    +
    + 🎯 + {% if config.theme.language == "zh" %} +

    实时执行

    +

    直接在目标进程中运行 Python 代码。检查变量、修改状态、实时调试。

    + {% else %} +

    Live Execution

    +

    Run Python code directly in target processes. Inspect variables, modify state, and debug in real-time.

    + {% endif %} +
    +
    + 📈 + {% if config.theme.language == "zh" %} +

    堆栈分析

    +

    捕获带有变量值的执行堆栈。精确了解代码在任何时刻的执行状态。

    + {% else %} +

    Stack Analysis

    +

    Capture execution stacks with variable values. Understand exactly what your code is doing at any moment.

    + {% endif %} +
    +
    + 🌐 + {% if config.theme.language == "zh" %} +

    分布式支持

    +

    监控跨多节点的进程。通过跨节点关联调试分布式训练问题。

    + {% else %} +

    Distributed Ready

    +

    Monitor processes across multiple nodes. Debug distributed training issues with cross-node correlation.

    + {% endif %} +
    +
    + ⚡ + {% if config.theme.language == "zh" %} +

    极低开销

    +

    典型工作负载下性能影响小于 5%。生产级效率，适用于真实部署。

    + {% else %} +

    Minimal Overhead

    +

    Less than 5% performance impact in typical workloads. Production-grade efficiency for real deployments.

    + {% endif %} +
    +
    +
    +
    + + +
    +
    +

    {% if config.theme.language == "zh" %}快速开始{% else %}Quick Start{% endif %}

    +
    +
    +
    1
    +
    +

    {% if config.theme.language == "zh" %}安装 Probing{% else %}Install Probing{% endif %}

    +
    + 
    pip install probing
    +
    +
    +
    +
    +
    2
    +
    +

    {% if config.theme.language == "zh" %}注入到运行中的进程{% else %}Inject into Running Process{% endif %}

    +
    + 
    # {% if config.theme.language == "zh" %}查找进程{% else %}Find your process{% endif %}
+pgrep -f "python.*train"
+
+# {% if config.theme.language == "zh" %}注入探针{% else %}Inject probes{% endif %}
+probing -t <pid> inject
    +
    +
    +
    +
    +
    3
    +
    +

    {% if config.theme.language == "zh" %}开始分析{% else %}Start Analyzing{% endif %}

    +
    + 
    # {% if config.theme.language == "zh" %}查询性能数据{% else %}Query performance data{% endif %}
+probing -t <pid> query "SELECT * FROM python.torch_trace LIMIT 10"
+
+# {% if config.theme.language == "zh" %}在进程中执行代码{% else %}Execute code in process{% endif %}
+probing -t <pid> eval "print(torch.cuda.memory_allocated())"
+
+# {% if config.theme.language == "zh" %}捕获堆栈跟踪{% else %}Capture stack trace{% endif %}
+probing -t <pid> backtrace
    +
    +
    +
    +
    +
    +
    + + +
    +
    +
    +
    + {% if config.theme.language == "zh" %} +

    SQL 驱动的分析

    +

    + 使用熟悉的 SQL 语法查询您的 AI 工作负载数据。使用标准数据库操作 + 分析 PyTorch 追踪、内存使用和自定义指标。 +

    +

    + 基于 Apache DataFusion 构建的高性能分析引擎，支持 + 聚合、窗口函数和复杂连接。 +

    + 了解更多 → + {% else %} +

    SQL-Powered Analytics

    +

    + Query your AI workload data with familiar SQL syntax. Analyze PyTorch traces, + memory usage, and custom metrics using standard database operations. +

    +

    + Built on Apache DataFusion for high-performance analytics with support for + aggregations, window functions, and complex joins. +

    + Learn More → + {% endif %} +
    +
    +
    + 
    -- {% if config.theme.language == "zh" %}查找最慢操作{% else %}Find slowest operations{% endif %}
+SELECT
+    module,
+    AVG(duration) as avg_time,
+    MAX(allocated) as peak_memory
+FROM python.torch_trace
+WHERE step > 100
+GROUP BY module
+ORDER BY avg_time DESC
+LIMIT 10;
    +
    +
    +
    +
    +
    + + +
    +
    +
    +
    + {% if config.theme.language == "zh" %} +

    实时进程调试

    +

    + 在运行中的进程中执行任意 Python 代码而无需停止它们。 + 检查变量、查看 GPU 内存、实时修改行为。 +

    +

    + 交互式 REPL 模式用于探索性调试。Tab 补全和 + 完全访问进程上下文。 +

    + 了解更多 → + {% else %} +

    Live Process Debugging

    +

    + Execute arbitrary Python code in running processes without stopping them. + Inspect variables, check GPU memory, and modify behavior on the fly. +

    +

    + Interactive REPL mode for exploratory debugging. Tab completion and + full access to the process context. +

    + Learn More → + {% endif %} +
    +
    +
    + 
    # {% if config.theme.language == "zh" %}检查 GPU 内存{% else %}Check GPU memory{% endif %}
+probing -t <pid> eval "
+import torch
+print(f'Allocated: {torch.cuda.memory_allocated()/1024**3:.2f} GB')
+print(f'Cached: {torch.cuda.memory_reserved()/1024**3:.2f} GB')"
+
+# {% if config.theme.language == "zh" %}交互式 REPL{% else %}Interactive REPL{% endif %}
+probing -t <pid> repl
    +
    +
    +
    +
    +
    + + +
    +
    +

    {% if config.theme.language == "zh" %}使用场景{% else %}Use Cases{% endif %}

    +
    +
    +
    🎓
    + {% if config.theme.language == "zh" %} +

    训练调试

    +

    调试训练不稳定、卡住和性能下降问题。实时了解训练为何发散或停止。

    + {% else %} +

    Training Debugging

    +

    Debug training instabilities, hangs, and performance regressions. Real-time insight into why training diverges or stops.

    + {% endif %} +
    +
    +
    💾
    + {% if config.theme.language == "zh" %} +

    内存分析

    +

    追踪训练步骤中的 GPU/CPU 内存使用。检测内存泄漏并优化内存效率。

    + {% else %} +

    Memory Analysis

    +

    Track GPU/CPU memory usage across training steps. Detect memory leaks and optimize memory efficiency.

    + {% endif %} +
    +
    +
    🚀
    + {% if config.theme.language == "zh" %} +

    性能分析

    +

    识别前向/反向传播中的瓶颈。找到慢操作并优化模型性能。

    + {% else %} +

    Performance Profiling

    +

    Identify bottlenecks in forward/backward passes. Find slow operations and optimize model performance.

    + {% endif %} +
    +
    +
    🔧
    + {% if config.theme.language == "zh" %} +

    生产监控

    +

    无需重启即可监控 AI 服务。收集自定义指标并实时调试生产问题。

    + {% else %} +

    Production Monitoring

    +

    Monitor AI services without restarts. Collect custom metrics and debug production issues live.

    + {% endif %} +
    +
    +
    +
    + + +
    +
    + {% if config.theme.language == "zh" %} +

    加入社区

    +

    Probing 是开源的，欢迎参与！

    + {% else %} +

    Join the Community

    +

    Probing is open source. Get involved!

    + {% endif %} +
    + + + + + GitHub + + + + + + {% if config.theme.language == "zh" %}问题反馈{% else %}Issues{% endif %} + + + + + + PyPI + +
    +
    +
    + +{% endblock %} + +{% block content %}{% endblock %} +{% block footer %} + {{ super() }} +{% endblock %} diff --git a/docs/pyproject.toml b/docs/pyproject.toml new file mode 100644 index 00000000..e7a4460a --- /dev/null +++ b/docs/pyproject.toml @@ -0,0 +1,21 @@ +[project] +name = "probing-docs" +version = "0.1.0" +description = "Documentation for Probing - Dynamic Performance Profiler for Distributed AI" +readme = "README.md" +requires-python = ">=3.10" +dependencies = [ + "mkdocs>=1.5.0", + "mkdocs-material>=9.4.0", + "mkdocs-material-extensions>=1.3.0", + "mkdocs-static-i18n>=1.0.0", + "mkdocstrings>=0.24.0", + "mkdocstrings-python>=1.7.0", + "jieba>=0.42.1", +] + +[tool.uv] +dev-dependencies = [ + "mkdocs-minify-plugin>=0.7.0", + "mkdocs-git-revision-date-localized-plugin>=1.2.0", +] diff --git a/docs/requirements.txt b/docs/requirements.txt new file mode 100644 index 00000000..6d4373e2 --- /dev/null +++ b/docs/requirements.txt @@ -0,0 +1,10 @@ +# Documentation dependencies for Probing +# Auto-generated from pyproject.toml + +mkdocs>=1.5.0 +mkdocs-material>=9.4.0 +mkdocs-material-extensions>=1.3.0 +mkdocs-static-i18n>=1.0.0 +mkdocstrings>=0.24.0 +mkdocstrings-python>=1.7.0 +jieba>=0.42.1 diff --git a/docs/requirements_doc.txt b/docs/requirements_doc.txt deleted file mode 100644 index 8713345f..00000000 --- a/docs/requirements_doc.txt +++ /dev/null @@ -1,7 +0,0 @@ -sphinx>=7.0.0 -myst-parser>=2.0.0 -sphinx-rtd-theme>=1.3.0 -sphinx-copybutton>=0.5.0 -sphinx-design>=0.5.0 -sphinx-autobuild>=2021.3.14 -linkify-it-py>=2.0.0 diff --git a/docs/serve.sh b/docs/serve.sh deleted file mode 100755 index 4d23d798..00000000 --- a/docs/serve.sh +++ /dev/null @@ -1,32 +0,0 @@ -#!/bin/bash -# Live preview documentation server - -set -e - -echo "🚀 Starting Probing documentation live preview server..." -echo "" - -# Check if sphinx-autobuild is installed -if ! python3 -c "import sphinx_autobuild" 2>/dev/null; then - echo "⚠️ sphinx-autobuild not detected, installing..." - pip install -q sphinx-autobuild -fi - -# Start server -echo "📖 Documentation will open automatically in browser" -echo "🌐 Access URL: http://127.0.0.1:8000" -echo "💡 Documentation will auto-rebuild and refresh browser when files are modified" -echo "🛑 Press Ctrl+C to stop server" -echo "" - -sphinx-autobuild \ - -b html \ - . \ - _build/html \ - --host 0.0.0.0 \ - --port 8000 \ - --open-browser \ - --watch . \ - --ignore "_build/*" \ - --ignore "*.pyc" \ - --ignore ".git/*" diff --git a/docs/src/README.md b/docs/src/README.md deleted file mode 100644 index a6d2a449..00000000 --- a/docs/src/README.md +++ /dev/null @@ -1,54 +0,0 @@ -# Probing Documentation - -Welcome to the Probing documentation! Probing is a performance and stability diagnostic tool for Python and AI applications, designed to solve debugging and optimization challenges in large-scale, distributed, long-cycle AI heterogeneous computing tasks. - -## Quick Navigation - -### 🚀 **Getting Started** -- [Introduction](getting-started/introduction.md) - Learn about Probing's core principles -- [Installation](getting-started/installation.md) - Install and setup guide -- [Quick Start](getting-started/quick-start.md) - Get running in minutes -- [First Steps](getting-started/first-steps.md) - Your first profiling session - -### 📖 **User Guide** -- [Basic Usage](user-guide/basic-usage.md) - Core functionality and workflows -- [SQL Analytics](user-guide/sql-analytics.md) - Powerful data analysis interface -- [Configuration](user-guide/configuration.md) - Customize your setup -- [Troubleshooting](user-guide/troubleshooting.md) - Solve common issues - -### 🔧 **Advanced Features** -- [Architecture Overview](advanced/architecture.md) - System design and internals -- [Performance Analysis](advanced/performance.md) - Deep dive into performance optimization -- [API Reference](advanced/api-reference.md) - Complete API documentation - -### 💡 **Examples & Use Cases** -- [PyTorch Training](examples/pytorch-training.md) - Monitor training processes -- [Distributed Training](examples/distributed-training.md) - Multi-node analysis -- [Memory Debugging](examples/memory-debugging.md) - Find memory leaks -- [Production Monitoring](examples/production-monitoring.md) - Real-world deployment - -### 🛠️ **Development** -- [Building from Source](development/building.md) - Compile Probing yourself -- [Contributing](development/contributing.md) - Help improve Probing -- [Testing](development/testing.md) - Run the test suite - -### 📚 **Reference** -- [CLI Commands](reference/cli-commands.md) - Complete command reference -- [SQL Schema](reference/sql-schema.md) - Database structure -- [Environment Variables](reference/environment-vars.md) - Configuration options -- [Roadmap](reference/roadmap.md) - Future plans - ---- - -## Key Features - -- **Zero Intrusion**: No code changes, environment adaptation, or process modifications required -- **Zero Learning Curve**: Standard SQL interface transforms complex performance analysis into intuitive database queries -- **Zero Deployment Burden**: Rust-based minimal static compilation enables single-file deployment and elastic scaling - -## Support - -- 📖 Browse the full documentation using the navigation above -- 💬 Join our community discussions -- 🐛 Report issues on GitHub -- 📧 Contact the team for enterprise support diff --git a/docs/src/SUMMARY.md b/docs/src/SUMMARY.md deleted file mode 100644 index 6152e1cb..00000000 --- a/docs/src/SUMMARY.md +++ /dev/null @@ -1,21 +0,0 @@ -# Summary - -- [Introduction](getting-started/introduction.md) -- [Installation](getting-started/installation.md) - ---- - -# System Design -- [Design Overview](design/README.md) -- [System Architecture](design/architecture.md) -- [Debugging Mechanisms](design/debugging.md) -- [Profiling Implementation](design/profiling.md) -- [Distributed Architecture](design/distributed.md) -- [Extensibility Framework](design/extensibility.md) - ---- - -# Advanced (Optional) -- [SQL Analytics](user-guide/sql-analytics.md) -- [Memory Analysis](user-guide/memory-analysis.md) -- [Building from Source](development/building.md) diff --git a/docs/src/advanced/architecture.md b/docs/src/advanced/architecture.md deleted file mode 100644 index 66af07d5..00000000 --- a/docs/src/advanced/architecture.md +++ /dev/null @@ -1,259 +0,0 @@ -# 大规模分布式异构训练面临的挑战 - -当前顶级的LLM训练已经进入万卡时代，即便最为节约算力成本的DeepSeek也采用2000张H100，等效FP16算力达5000+ A100。然而，性能诊断体系严重滞后于算力规模扩张，主要体现在以下三个方面： - -* **GPU故障为常态**：在Meta的LLaMA3训练中[^1]，16, 000张H100在54天内共出现466次故障，其中58.2%源自GPU硬件故障。这意味着每天都要面临8次GPU故障，严重影响训练的连续性和稳定性; - -* **性能不及预期也是故障**：由于使用大规模的集合通信语义(比如All Reduce)进行同步训练，慢节点会引发整个系统效率下降。在字节的MegaScale的一项实测中[^2]，移除慢节可带来0.7%的MFU提升。LLaMA-3训练中，也提到这种慢节点问题定位极为困难； - -* **硬件、系统与模型优化割裂**：芯片层面的诊断数据未能被系统与模型有效利用。目前，系统与模型仅关注算力利用率MFU，而内存带宽利用率MBU、C2C互联带宽与网络带宽利用率缺乏关注，导致硬件、系统与模型之间的优化无法形成合力; - -由上述挑战可知，现有工具在应对大规模分布式环境时存在固有局限，难以同时满足实时性能监控和精细化数据采集的需求。 - -## 现有工具与方法难以应付这些挑战 - -现有诊断工具多聚焦于单一维度，难以在硬件故障、系统通信与模型表现等层面实现综合分析，且缺乏对分布式环境的可视化与实时监测能力。这大大增加了研发团队的诊断成本和时间。具体缺陷包括： - -* **事前工具限制** 现有工具需要在问题发生之前预先部署与插桩，否则无法捕获事中或事后发生的故障信息。大量故障和性能问题通常是在训练过程中或之后才暴露，导致无法有效进行回溯分析; -* **高性能开销** 这些工具在捕获和诊断过程中消耗大量GPU资源及通信带宽，对生产任务性能影响巨大，难以在真实生产环境中常驻部署; -* **专注单机，缺乏分布式能力** 目前多数诊断工具仅关注单节点或单GPU数据，缺少针对跨节点通信、负载均衡等分布式训练场景的综合分析能力，难以定位整个系统的性能瓶颈; - -> 为了突破上述局限，我们需要一款既具备低侵入性和实时监控能力，又能在分布式场景下实现全链路数据采集与关联分析的诊断工具。 - -## 理想的诊断工具应该具备哪些特性 - -* **全天候** - 能够在任何时刻动态启用，无需预先部署或插桩，在生产任务中以极低性能开销持续运行，实现实时监控与故障追溯，帮助工程团队及时响应系统异常和硬件故障； - -* **全场景** - 不仅支持单机诊断，还能无缝覆盖分布式训练环境，无论集群规模如何，都能确保数据采集与故障分析的一致性，满足从小规模研发到大规模生产的需求； - -* **全链路** - 实现从硬件层面的诊断数据、芯片互联状态，到框架、系统和模型各层数据的全面采集，构建完整的闭环监控系统，从而为硬件芯片公司提供关键的反馈数据，以便优化产品设计和性能。 - -# 一种分布式探针系统——Probing - -Probing 针对上述问题提供以下技术方案： - -+ **探针机制** - 采用 ptrace 或基于 LD_LIBRARY_PATH 的动态库注入方式，将探针注入目标进程，从而获得与目标进程相同的内存地址空间与权限。这种方案允许在进程内部直接观测和调试，超越传统进程外工具的监控能力。值得指出的是，在没有发出监控指令的情况下，探针处于完全静默状态，不会引入额外的性能开销。 - -+ **Query 引擎** - 借助标准 SQL 引擎实现数据管理与分析。针对数值序列和字符串序列，Query 引擎分别采用不同的压缩编码算法，利用数据的局部性进行压缩。数据压缩比视具体数据而定，可达到5倍到上百倍的压缩效果，从而显著降低存储开销并保障时序数据精度。 - -+ **分布式数据处理** - 依托 Query 引擎的分布式处理能力，能够自动将数据计算任务拆分为各个节点内部的局部汇总计算与跨节点的数据聚合分析，实现大规模分布式环境下统一的高效监控与诊断。 - -针对分布式与异构计算两个关键问题，Probing进行特殊优化： - -- **分布式** - - 分布式训练状态感知：实时监控各节点计算和通信状态，支持自动发现性能异常节点 - - 集合通信性能优化：针对常见分布式训练模式(DP/TP/PP)提供专门的性能分析工具 - - 负载均衡分析：跟踪记录各节点负载分布情况，及时发现训练过程中的性能瓶颈 - - -- **异构计算** - - 全链路数据采集：统一采集从硬件到框架各层面的性能指标，支持关联分析 - - 硬件性能优化：通过分析训练负载特征，为硬件性能调优提供数据支持 - -## 整体架构 - -```mermaid ---- -title: 整体架构 ---- -graph TB - subgraph "控制平面" - UI[Web UI] - CLI[命令行] - API[SQL查询+HTTP协议] - UI & CLI --> API - end - - subgraph "探针平面" - direction LR - P0[探针核心机制 + 查询引擎] - EX[探针扩展机制] - P1[Python探针] - P2[PyTorch探针] - P3[硬件探针] - API --> P0 --> EX-.-> P1 & P2 & P3 - end - - style UI fill:#ddd,stroke:#333 - style CLI fill:#ddd,stroke:#333 - style API fill:#ddd,stroke:#333 - style P0 fill:#999,stroke:#333 - style P1 fill:#999,stroke:#333 - style P2 fill:#999,stroke:#333 - style P3 fill:#999,stroke:#333 -``` - -Probing在整体设计采用简单可靠的两层设计： -1. 探针平面，实现为一个纯静态链接的so，会被注入到训练进程内，提供： - 1. 核心机制：实现数据采集与代码注入； - 2. 查询引擎：提供数据存储与分析能力； - 3. 扩展机制：提供基于Python与Rust扩展探针能力的机制，同时提供Python、Torch和硬件等探针功能； -2. 控制平面，提供用户交互界面，包括: - 1. Web界面：可视化操作与数据展示 - 2. 命令行工具：灵活的探针控制 - 3. 统一API：标准化的接口规范 - -在分布式场景中，每个节点的每个进程都拥有独立探针，探针之间互为平等，无Master节点，避免单点故障。控制平面可单独管理任意进程，也可通过该进程探针再控制其他节点。 - -```mermaid ---- -title: 分布式部署视图 ---- -graph TB - subgraph "控制平面" - UI[Web UI] - CLI[命令行] - API[统一API] - UI & CLI --> API - end - - subgraph "Node 1" - P1[训练进程] - PR1[探针] - P1 -.- PR1 - end - - subgraph "Node 2" - P2[训练进程] - PR2[探针] - P2 -.- PR2 - end - - subgraph "Node 3" - P3[训练进程] - PR3[探针] - P3 -.- PR3 - end - - PR1 <--自动发现--> PR2 - PR2 <--自动发现--> PR3 - PR3 <--自动发现--> PR1 - - API --控制/查询--> PR1 - - - style UI fill:#ddd,stroke:#333 - style CLI fill:#ddd,stroke:#333 - style API fill:#ddd,stroke:#333 - style P1 fill:#f9f,stroke:#333 - style P2 fill:#f9f,stroke:#333 - style P3 fill:#f9f,stroke:#333 - style PR1 fill:#bfb,stroke:#333 - style PR2 fill:#bfb,stroke:#333 - style PR3 fill:#bfb,stroke:#333 - ``` - -## 查询引擎 - -引入查询引擎可以极大简化探针内部的数据存储与处理模块的设计，查询引擎内部对于查询的处理过程如下图所示： - -```mermaid -graph LR - sql["Query Language"] - --> lp["Logical Plan"] - --> pp["Physical Plan"] - --> ds["Data Sources"] -``` - -其中： -1. Query Language是用户书写的查询语言，一般是SQL或者DataFrame API； -2. Logical Plan描述查询的高层结构与所需的逻辑操作，并不关心底层执行细节； -3. Physical Plan: 基于逻辑计划生成的具体执行方案，包含数据分布、算子实现等底层执行细节； -4. Data Sources： 是数据源，任何Query都将被转换为数据源上的操作； - -整个处理流程复用了开源查询引擎DataFusion提供的标准实现。Probing主要将探针能够获取的各种数据作为DataFusion的数据源扩展。数据源按照三级来组织： -``` -Catalog (命名空间) - | - +-- Schema1 (逻辑分组) - | | - | +-- Table1 - | +-- Table2 - | - +-- Schema2 - | - +-- Table3 -``` - -## 扩展机制 - -Probing支持两类数据源插件，分别用于静态和动态数据场景： - -##### TablePlugin -静态表格数据源插件，用于提供结构固定的单一数据表。适用于: -- 硬件性能指标(CPU/GPU利用率、温度等) -- 进程状态监控(内存占用、线程数等) -- 固定格式的性能计数器数据 - -##### SchemaPlugin -动态表格数据源插件，支持按需创建多个数据表。适用于: -- 文件系统监控(每个文件映射为表) -- Python模块跟踪(每个模块映射为表) -- 动态生成的性能分析数据 - -除了上述两种扩展机制外，Probing还提供使用Python扩展的查询引擎的机制： - -##### 即时查询 - -任何Python包的函数均可视作查询引擎的扩展，只需要在SQL语句中按如下方式书写： -```SQL --- 直接执行Python表达式 -SELECT * -FROM python.`` -``` -比如 -```SQL -SELECT * -FROM python.`time.time()` -``` -将会在节点上调用`time.time()`这个语句，并将结果作为表格返回； - -##### 表格注册 - -用户也可以在代码中主动向Probing写入数据 -```python -import probing - -# 创建并注册表格 -tbl = probing.ExternalTable("test", ["col1", "col2"]) - -# 写入数据 -tbl.append([1, 0.1]) -tbl.append([2, 0.2]) -tbl.append([3, 0.3]) -``` -Python写入的数据也会自动被查询引擎进行压缩和优化。 -```SQL --- 查询已注册表格 -SELECT * -FROM python.test -``` - -## 与其他工具的比较 - -| 特性维度 | NVIDIA DCGM | Nsight Systems | Torch Profiler | **Probing** | -|-----------------|--------------|----------------------|----------------------|--------------------------------| -| **侵入性** | 无（驱动级） | 低(需事先注入) | 高(修改代码) | **极低(动态注入)** | -| **性能开销** | 无 | 动态（随采样频率变化） | 高 | **动态(随采样频率变化)** | -| **分布式关联分析** | ❌单节点 | ❌单进程 | ❌单进程 | **分布式查询** | -| **数据采集维度** | 硬件指标 | 系统+应用层 | 框架层 | **全链路(硬件/框架/模型)** | -| **动态启停能力** | ❌常驻采集 | ❌需重启 | ❌代码修改 | **运行时注入🚀** | -| **故障诊断能力** | 硬件故障告警 | 性能瓶颈定位 | 算子性能分析 | **硬件→模型关联分析🔍** | -| **慢节点检测** | ❌ | ❌ | ❌ | **自动标记🏷️** | -| **通信性能分析** | 带宽利用率 | NCCL事件追踪 | 无 | **通信+模型分析偶📊** | -| **内存诊断** | 显存占用量 | 显存访问模式 | 显存分配统计 | 待开发 | -| **数据回溯能力** | 滚动窗口存储 | 需导出文件（文件较大） | 需导出文件（文件较大） | **动态存储📦** | -| **硬件反馈价值** | 基础健康监测 | 微架构优化 | 无 | **结合workload指导芯片设计💎** | - - -# 参考文献 - -[^1]: [The Llama 3 Herd of Models](https://arxiv.org/pdf/2407.21783) - -[^2]: [MegaScale: Scaling Large Language Model Training to More Than 10, 000 GPUs](https://arxiv.org/pdf/2402.15627) diff --git a/docs/src/advanced/performance.md b/docs/src/advanced/performance.md deleted file mode 100644 index c8265e5a..00000000 --- a/docs/src/advanced/performance.md +++ /dev/null @@ -1,393 +0,0 @@ - - -本文详细介绍Probing的性能分析实现。关于Probing的整体架构设计，请参考[整体架构](./arch.md)。 - -在进行系统优化时，Benchmark与Profiling是非常重要的两种手段。前者是指测试整个系统端到端性能，或者系统某个子模块的端到端性能的方法，可以非常快速的形成：代码优化 --> Benchmark --> 代码优化 这样一个带有反馈的循环。也是大多数进行性能优化的工程师最主要的工作流。然而单纯的Benckmark工作流非常容易陷入拼命投入优化代码，但性能始终不见提升的情况。此时所投入的优化已经不再是整个系统的核心瓶颈了，因此无法为整个系统的性能带来太多提升。如何定位系统的性能问题，找到需要优化的瓶颈就需要进行Profiling。 - -## Profiling方法论 - -日常工作中，大家能够到很多profiling工具，比如Linux内核自带的perf、intel的vtune以及torch的torch.profiler等。这些工具根据底层原理的不同，可分为三类：插桩（Instrumentation）、采样（Sampling）和性能计数器（Peformance Counter）。 - -### Instrumentation方法 - -在测试代码性能时，经常用到的方法就是在代码里直接插入计时代码： - -```python -def foo(): - start = time.time() - do something - end = time.time() - - print("duration = ", end - start) -``` - -除了记录时间以外，这种手动插入代码还能记录分支和循环的情况、内存分配的情况等分析性能时感兴趣的信息。如果能够将这种过程自动化，为所执行的每一段代码都插入相关计时，我们就能从全局视角对整个应用进行Profiling了，这也是自动Instrumentation技术。对于C/C++代码，这种插桩需要编译器配合，比如GCC有profiling相关选项： -```bash -gcc a.c -g -pg -``` -借助Instrumentation，编译器还能实现代码覆盖率、AddressSanitizer、StackProtector等功能。除了直接在编译阶段影响二进制代码的生成，还可以在linker/loader中做手脚，比如大多数memory profiler都是通过替换libc中的malloc/free函数来实现对内存分配行为的追踪。 - -#### Python代码的插桩 - -对于Python来说，最常见的插桩方法是使用Decorator： - -```python -def my_decorator(func): - def wrapper(*args, **kwargs): - start = time.time() - ret = func(*args, **kwargs) - end = time.time() - print("duration of {}: {}", func, end - start) - return ret - return wrapper -``` -使用`my_decorator`时： -```python -@my_decorator -def foo(): - pass -``` -会自动被展开为： -```python -def foo(): - pass -foo = my_decorator(foo) -``` -Python还支持带参数的decorator，这里不进一步展开。decorator语法极大简化了Python中插桩的开发成本，并且易于在代码中进行控制。与decorator语法相配合的是`with`语法，该语法定义了一个语义scope，可以在借助这个scope控制插桩代码的逻辑： - -```python -def my_decorator(func): - def wrapper(*args, **kwargs): - enabled = get_enable() - if enabled: - start = time.time() - ret = func(*args, **kwargs) - if enabled: - end = time.time() - print("duration of {}: {}", func, end - start) - return ret - return wrapper - -class MyContext: - def __enter__(self): - set_enable() - def __exit__(self): - set_disable() -``` - -此时可以借助`MyContext`控制插桩代码的行为： -```python -foo() # 不会执行插桩代码 -with MyContext(): - foo() # 会打印插桩计时 - -foo() # 不会执行插桩代码 -``` - -#### Python解释器插桩 - -除了在代码中进行插桩操作以外，Python还支持在解释器层面进行插桩操作。Python解释器支持用户自定义trace函数： - -```C -struct _frame { - PyObject_HEAD - PyFrameObject *f_back; /* previous frame, or NULL */ - struct _PyInterpreterFrame *f_frame; /* points to the frame data */ -==> PyObject *f_trace; /* Trace function */ - int f_lineno; /* Current line number. Only valid if non-zero */ - char f_trace_lines; /* Emit per-line trace events? */ - char f_trace_opcodes; /* Emit per-opcode trace events? */ - PyObject *f_extra_locals; /* Dict for locals set by users using f_locals, could be NULL */ - ... -}; -``` - -在执行Python代码时，可以借助这个`f_trace`函数来trace解释器内部的执行，共有5种事件会被转发给trace函数： -- call：执行函数调用； -- line：执行一行代码； -- return：函数返回； -- exception：发生一场； -- opcode：执行一条字节码； -借助`trace`函数可以实现Python的调试器，profiler等。如果我们需要实现一个分布式调试器，可以开发一个受RPC控制的trace函数。此外，在Python代码可通过自省的方式获取每一个函数的字节码，并对这些字节码进行反编译和插桩。关于字节码的反编译与插桩方法在此处不进行展开。 - -#### Torch框架插桩 - -Torch框架的dispatch机制中预留了一些hook接口，可以通过`torch::addGlobalCallback`接口来捕获算子调用。这些hook会被传入一个`torch::RecordFunction`结构体，通过这个结构体可以获取调用上下文，包括name，inputs等。但是记录这些调用信息的开销很高，需要做好控制。同时Torch也提供`c10::ThreadLocalDebugInfo`接口，用于在整个forward和backward过程中追踪一些信息。 - -PyTorch中使用`addGlobalCallback`进行trace的主要功能有： -1. Kineto Profiler，目前Torch Profiler的主要实现: torch/csrc/autograd/profiler_kineto.cpp: ; -2. Execution Trace Observer，用于导出计算图供底层设施分析和仿真：torch/csrc/profiler/standalone/execution_trace_observer.cpp； - -### Sampling方法 - -插桩方法能够获得准确的执行时间线，输出timeline。但是插桩需要侵入目标代码，并且会带来额外的运行时开销，难以直接用于线上生产任务。 - -Sampling方法可以很好的避免对目标代码的侵入，同时也能把性能开销控制在一个合理的水平上。常见的Sampling方法有两种： - -#### Pprof方法 - -Pprof方法最先见于google的gperftools，其实现原理在于基于`setitimer`方法[^setitimer]设置一个定时器，`setitimer`方法的原型如下： - -```C -int setitimer(int which, const struct itimerval *new_value, - struct itimerval *old_value); -``` -其中which有三种取值： - -- **ITIMER_REAL** 计时完成时，触发**SIGALRM**信号; -- **ITIMER_VIRTUAL** 计时完成时，触发**SIGALRM**信号，但只在进程活跃时计时; -- **ITIMER_PROF** 计时完成时，触发**SIGPROF**信号； - -**SIGPROF**信号会随机选中一个线程，中断其执行，并在其执行堆栈上运行该信号的handler。一般来说，占用CPU越多的线程被选中的概率越高。在该信号的handler中，可以通过backtrace获取被中断线程的调用堆栈，通过libunwind对堆栈进行分析后，可以完成一次采样。综合多次采样结果，可以绘制当前进程的火焰图。 - -#### PMU方法PMU - -Linux提供perf相关接口[^9]，向开发者暴露硬件PMU（Performance Monitoring Unit）的采样能力： - -```C -#include /* Definition of PERF_* constants */ -#include /* Definition of HW_* constants */ -#include /* Definition of SYS_* constants */ -#include - -int syscall(SYS_perf_event_open, struct perf_event_attr *attr, - pid_t pid, int cpu, int group_fd, unsigned long flags); -``` -由于glibc未提供封装，需要直接通过syscall直接进行系统调用。`perf_event_attr`用来控制PMU采样： -```C -struct perf_event_attr { - __u32 type; /* Type of event */ - __u32 size; /* Size of attribute structure */ - __u64 config; /* Type-specific configuration */ - - union { - __u64 sample_period; /* Period of sampling */ - __u64 sample_freq; /* Frequency of sampling */ - }; - - __u64 sample_type; /* Specifies values included in sample */ - __u64 read_format; /* Specifies values returned in read */ - - ... - union { - __u32 wakeup_events; /* wakeup every n events */ - __u32 wakeup_watermark; /* bytes before wakeup */ - }; - ... -}; -``` -关键字段说明： -- type：用来定义事件类型 - - `PERF_TYPE_HARDWARE` 硬件采样 - - `PERF_TYPE_SOFTWARE` 软件采样 -- config： 用来配置事件 - - `PERF_COUNT_HW_CPU_CYCLES` 按时钟周期采样 - - `PERF_COUNT_HW_INSTRUCTIONS` 按指令数采样 -- sample_type：用来制定采样事件包含的信息 - - `PERF_SAMPLE_IP` 记录IP指针 - - `PERF_SAMPLE_TIME` 记录时间戳 - - `PERF_SAMPLE_STACK_USER` 记录用户态调用栈 - - `PERF_SAMPLE_CALLCHAIN` 记录调用栈 - -相比`setitimer`方法，PMU方法能够提供更高的计时精度，并且采样完全由硬件完成，开销更低。但是PMU方法依赖具体硬件实现，并且需确保进程具备CAP_PERFMON权限（Linux 5.8+）或CAP_SYS_ADMIN权限。而且PMU无法像SIGPROF那样可以通过handler获取更丰富的信息。 - -#### GPU 上的采样技术 - -CUDA支持对设备上的PC指针（program counter）进行采样，每个SM（Streaming Multiprocessor）以固定的时间间隔，随机选择一个wrap记录其调度状态与PC指针。同时CUDA也提供关联PC指针与SASS代码的方法，共用户获取函数ID。 - -自CUDA 12.6版本开始，CUPTI（CUDA Profiling Toolkit Interface）引入了全新的performance monitors (PM)接口[^cupti_pm]，基于该接口可以获取： -- GR Active: The percentage of cycles the compute engine is active； -- Tensor Active / FP16 Active: The ratio of cycles the SM tensor pipes or FP16x2 pipes were active issuing tensor instructions to the number of cycles in the sample period as a percentage. -- DRAM Read Bandwidth: The ratio of cycles the DRAM interface was active reading data to the elapsed cycles in the same period as a percentage. -- PCIe Read Throughput: The ratio of bytes received on the PCIe interface to the maximum number of bytes receivable in the sample period as a percentage. -- NVLink bytes received: The ratio of bytes received on the NVLink interface to the maximum number of bytes receivable in the sample period as a percentage. - -完整的列表请参考Nvidia官方文档。 - -### Performance Counter方法 - -从Instrumentation和Sampling方法的介绍中，我们可以看到一个趋势，基于采样的方法因为其侵入性低、适用范围广，得到了越来越多的发展与硬件支持。除了常规的IP指针采样以外，内存操作计数器，PCIE操作计数器等也为性能问题的诊断提供了大量有用信息。但性能计数器方法的实现也不仅仅限于硬件实现，软件层面也能通过性能计数器提供诊断信息。此处简单列举了常见的软硬件计数器： - -#### 硬件计数器 - -在一个异构训练的硬件系统中，不同的硬件组件有各自不同的计数器，以下列举常见的硬件计数器： - -- CPU[^1][^2] - - 指令计数器：`instructions`指令计数器与`cycles`时钟计数器等； - - 缓存：`cache-misses`L1、L2、L3 缓存未命中的次数与`cache-references`缓存访问次数等; - - 分支：`branch-instructions`分支指令次数与`branch-misses`分支预测失败次数等； -- RDMA[^6] - - 基础统计：`port_rcv_packets`接收包数和`port_xmit_packets`（发送包数等； - - 数据：`port_rcv_data`接收数据量和`port_xmit_data`发送数据量； - - 拥塞控制：`np_cnp_sent`等； - -#### 软件计数器 -在整个软件栈上的各个层面上，也同样存在大大小小的计数器： - -- OS层面： - - /proc//stat， CPU、上下文切换、系统调用等统计信息； - - /proc//statm，进程的内存使用情况； -- 框架层面： - - NCCL Counter[^7] - - PyTorch Flight Recorder[^8] - -#### GPU上的性能计数器 - -GPU上的PMU分为两部分Core PMU和Uncore PMU，分别用于监控Core上的性能指标与Core外的性能指标： - -- Core上性能计数器[^nsyscomp]： - - 计算：`sm_inst_executed`执行的指令数, `sm_inst_executed_atomics`执行的原子指令数； - - 访存：`sm_inst_executed_generic_loads`访存load指令数和`sm_inst_executed_generic_stores`访存save指令数； -- Uncore性能计数器[^uncore] - - SCF PMU: 监控系统级缓存事件、CPU 流量以及到本地/远程内存的强排序 (SO) PCIE 写入流量; - - NVLink-C2C0 PMU: 监控来自通过 NVLink-C2C (芯片到芯片) 互连连接的 GPU/CPU 的传入流量; - - NVLink-C2C1 PMU: 监控来自通过 NVLink-C2C (芯片到芯片) 互连连接的 GPU 的传入流量; - - CNVLink PMU: 监控从远程插槽上的 GPU 和 PCIE 设备到本地内存的流量; - - PCIE PMU: 监控从 PCIE 根端口到本地/远程内存的所有读取/写入流量; - -#### 对GPU上性能计数器进行采样 - -性能计数器常见的用法是在一个span开始和结束的时候分别抓取性能计数器的数值，并通过两者的diff来判断span内的代码产生了多少网络吞吐或者计算量。但是对于GPU这种异构计算场景，性能计数器的采样和统计往往需要考虑到GPU Stream执行与CPU的同步关系。即必须在GPU Stream上异步采样，才能获得一段时间的真实数值。如下图所示，虽然计算是由CPU在时刻`2-3`发起，但是获取硬件性能计数器需要在时刻`5-9`。 - -```mermaid -block-beta - columns 11 - TL["Time:"] - 1 2 3 4 5 6 7 8 9 10 - C["CPU:"] space CS["launch"]:2 space:7 - G["GPU:"] space:4 S["span"]:5 space:1 - - style TL fill:#00000000, stroke:#00000000; - style G fill:#00000000, stroke:#00000000; - style C fill:#00000000, stroke:#00000000; -``` - -因此读取GPU硬件寄存器的工作必须在GPU设备上完成才能比较好的满足Stream异步语义。如何在GPU上读取性能计数器，并同步数据到CPU端将在另外文章里边讨论。 - -## 分布式训练Profiling的挑战与应对策略 - -现有的 Profiling 工具能够较好地解决单机性能分析问题，但在分布式训练场景下，它们面临全新的挑战： - -1. **性能特征的剧烈变化** - - 当集群规模扩大时，训练任务的性能特性会显著转变。例如： - - 3D 并行切分策略受集群规模及并行参数的影响，单机Profiling难以捕捉到所有参与角色的关键指标； - - 大规模集群中，集合通信可能引发延迟激增和带宽利用率下降的问题，单机Profiling工具难以准确反映这些现象。 - -2. **数据统计与解析的复杂性** - - 单机Profiling通常依靠单一样本得出较为确定的结论，而分布式环境更多依赖海量统计数据，这大大增加了结果的准确性要求和数据解析的复杂度。 - -3. **跨节点协同调试需求** - - 分布式环境中，各节点之间必须实现精准的协调，对不同角色间的协同行为进行实时且深入的分析，这对Profiling工具提出了更高要求。 - - timeline 对于单机系统性能分析具有极大的帮助，但是对于分布式训练问题，timeline方法会面临两方面的问题： - - 1. 数据量与分析难度：在1000节点集群中，Timeline数据量可能超过TB级别，导致存储与可视化不可行，并且1000条timeline也难以分析； - 2. 时间精度问题：分布式节点时间同步存在精度问题，且毫秒级时间同步误差会掩盖真实通信问题。 - -**应对这些挑战的技术路线：** - -1. **以Sampling为主的低侵入Profiling** - - 通过灵活调整采样率，可以在保证系统整体性能的前提下，适应从单机调试到大规模分布式生产任务的Profiling需求。 - -2. **Instrumentation与Sampling的混合方案** - - 针对执行时间较长、低频任务，在采样的基础上混入Instrumentation手段，从而既保证关键数据的准确性，又提供足够的上下文信息以辅助精细调优。 - -3. **采样与性能建模的结合** - - 由于纯Sampling方法不足以展示完整的执行timeline，有必要引入性能建模： - - 对训练任务的理论FLOPS（Floating-Point Operations Per Second）、内存吞吐和通信量进行建模； - - 利用建模结果与采样数据相结合，量化各模型层的算力利用率和带宽利用率，从而全面评估计算、通信及访存之间的潜在掩盖现象。 - -## Probing的性能Profiling方案 - -### 方案介绍 - -1. 将模型的执行按照其Layer结构，拆分成不同的span；这个过程可以借助Torch与Python的反射特性自动完成； -2. 对于每个span，以采样的方式对其进行计时，同时记录span相关元数据，比如span内的计算内容、本次计算的输入等； -3. 对于每个span，在采样执行时间的同时，对底层硬件计数器，比如NCCL通信和访存等进行采样； -4. 结合性能建模结果，评估每个span内部的实际硬件吞吐是否合理，并计算span内的算力利用率、内存带宽利用率和互联带宽利用率； - -probing给出的profiling方案本质上是通过纵向分层解耦分析代替横向时间轴关联分析： - -| | timeline方法 | probing | -| -------- | -------------------------- | ------------------------------ | -| 分析维度 | 横向时间轴关联分析 | 纵向分层解耦分析 | -| 关注焦点 | 跨节点事件的时间顺序关系 | 单节点各抽象层的资源利用效率 | -| 数据依赖 | 需要精确的全局时钟同步 | 仅需相对时间戳或逻辑因果关系 | -| 典型输出 | Gantt图、通信时序图 | 蜂窝热力图、分层利用率雷达图 | -| 适用场景 | 死锁诊断、精细化的通信优化 | 常态化性能监控、架构级瓶颈定位 | - -### 如何解决问题 - -#### 单机环境下基础性能问题 - -- 定位低效算子： - - 传统难点：可以观测到每个算子的时间，但是无法确定合理时间时多少，有多少优化空间； - - 解决问题： - - 通过Probing自定向下分析每个span的MFU； - - 自顶层span递降定位MFU较低的细粒度span； -- 任务流水线掩盖不足，计算与数据拷贝未充分重叠； - - 传统难点：Timeline工具可观测事件重叠，但无法量化掩盖效率。 - - 解决问题： - - 定义流水线阶段理想执行时间（如T_pipeline = max(T_load, T_comp, T_save) - - 计算实际效率（效率 = T_pipeline理想值 / T_pipeline实际值），识别拖尾阶段。 - -#### 小规模集群下分布式调优 - -- 通信-计算掩盖失效 - - 传统难点：通信耗时受网络拓扑、消息大小、协议类型等多因素影响，难以定位根因 - - 解决问题： - - 通过Probing获取某个span的执行时间； - - 通过性能建模计算理想计算时间与理想通信时间； - - 计算通信时间暴露比 = (T_span - max(T_comp, T_c2c))/T_c2c; - - 负载不均衡引发的集体操作延迟 - - 传统难点：无法确定各个节点进入集合通信语义的时间； - - 解决问题： - - 追踪每次集合通信开始到结束的时间； - - 根据节点角色，对节点分组，分组内比较集合通信的时间开销； - -#### 超大规模训练的独有难题 - -- 训练hang住 - - 传统难点：难以在几千个进程中定位到哪个进程导致hang； - - 解决问题： - - 通过probing查看每个进程的调用堆栈； - - 通过probing查看每个进程内集合通信库的内部计数器； - -- 慢节点问题 - - 传统难点：缺乏手段定位哪个进程速度慢； - - 解决稳定： - - 借助probing绘制performance heatmap，在heatmap上定位慢节点； - - 通过通信库的性能计数器，定位经常被其他节点等待的节点； - -## 参考文献 - -[^1]: https://github.com/torvalds/linux/blob/master/tools/perf/Documentation/perf-intel-pt.txt -[^2]: perf list 可查看完整列表 -[^3]: [Nvidia显卡的硬件性能计数器](https://docs.nvidia.com/gameworks/index.html#developertools/desktop/linux_graphics_debugger/lgd_perf_counters.htm) -[^nsyscomp]: [NSight Compute中的硬件Metric](https://docs.nvidia.com/nsight-compute/ProfilingGuide/index.html#metrics-reference) -[^5]: [PTX中的PMU寄存器](https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#special-registers-pm0-pm7) -[^6]: [RDMA网卡的硬件计数器](https://enterprise-support.nvidia.com/s/article/understanding-mlx5-linux-counters-and-status-parameters); -[^7]: https://github.com/NVIDIA/nccl/blob/master/src/include/nvtx3/nvtxDetail/nvtxExtImplCounters_v1.h -[^8]: https://pytorch.org/tutorials/prototype/flight_recorder_tutorial.html#enabling-flight-recorder -[^9]:https://man7.org/linux/man-pages/man2/perf_event_open.2.html -[^setitimer]: https://linux.die.net/man/2/setitimer -[^cupti_pm]: https://docs.nvidia.com/cupti/main/main.html#cupti-pm-sampling-api -[^nvpmu]: https://docs.nvidia.com/grace-perf-tuning-guide/measuring-performance.html#grace-coresight-system-pmu-units -[^uncore]: https://github.com/torvalds/linux/blob/master/Documentation/admin-guide/perf/nvidia-pmu.rst diff --git a/docs/src/api-reference.md b/docs/src/api-reference.md new file mode 100644 index 00000000..8e4554ed --- /dev/null +++ b/docs/src/api-reference.md @@ -0,0 +1,274 @@ +# API Reference + +Complete reference for Probing's CLI commands and Python API. + +## CLI Commands + +### probing inject + +Inject probes into a running process. + +```bash +probing -t inject +``` + +**Options:** + +- `-t, --target ` - Target process ID (required) + +**Platform:** Linux only + +--- + +### probing query + +Execute SQL queries against collected data. + +```bash +probing -t query "" +``` + +**Examples:** + +```bash +# Query torch traces +probing -t 12345 query "SELECT * FROM python.torch_trace LIMIT 10" + +# Aggregate query +probing -t host:8080 query "SELECT module, AVG(duration) FROM python.torch_trace GROUP BY module" +``` + +--- + +### probing eval + +Execute Python code in target process. + +```bash +probing -t eval "" +``` + +**Examples:** + +```bash +# Simple evaluation +probing -t 12345 eval "print('hello')" + +# Multi-statement +probing -t 12345 eval "import torch; print(torch.cuda.is_available())" +``` + +--- + +### probing backtrace + +Capture current stack trace. + +```bash +probing -t backtrace +``` + +**Output:** Stack frames with function names, files, and line numbers. + +--- + +### probing repl + +Start interactive Python REPL. + +```bash +probing -t repl +``` + +**Features:** + +- Tab completion +- Multi-line input +- Command history + +--- + +### probing list + +List processes with probing enabled. + +```bash +probing list +``` + +**Output:** Process IDs and their probing status. + +--- + +### probing config + +View or modify configuration. + +```bash +# View all config +probing -t config + +# View specific key +probing -t config probing.sample_rate + +# Set value +probing -t config probing.sample_rate=0.1 +``` + +--- + +### probing memory + +Quick memory overview. + +```bash +probing -t memory +``` + +--- + +### probing rdma + +RDMA flow analysis. + +```bash +probing -t rdma +``` + +## Python API + +### probing.connect + +Connect to a probing endpoint. + +```python +from probing import connect + +# Connect by PID +probe = connect(pid=12345) + +# Connect by address +probe = connect(address="host:8080") +``` + +--- + +### probe.eval + +Execute code in target process. + +```python +result = probe.eval("print('hello')") +``` + +--- + +### probe.query + +Execute SQL query. + +```python +df = probe.query("SELECT * FROM python.torch_trace") +``` + +--- + +### @probing.table + +Register custom data table. + +```python +from probing import table + +@table("my_data") +def get_my_data(): + return [{"key": "value"}] +``` + +--- + +### @probing.metric + +Register custom metric. + +```python +from probing import metric + +@metric("custom_metric") +def get_metric(): + return 42.0 +``` + +## SQL Tables + +### python.backtrace + +Stack trace information. + +| Column | Type | Description | +|--------|------|-------------| +| func | string | Function name | +| file | string | Source file | +| lineno | int | Line number | +| depth | int | Stack depth | +| frame_type | string | Python/Native | + +--- + +### python.torch_trace + +PyTorch execution traces. + +| Column | Type | Description | +|--------|------|-------------| +| step | int | Training step | +| seq | int | Sequence number | +| module | string | Module name | +| stage | string | forward/backward/step | +| allocated | float | GPU memory (MB) | +| max_allocated | float | Peak GPU memory (MB) | +| cached | float | Cached memory (MB) | +| duration | float | Execution time (sec) | + +--- + +### python.variables + +Variable tracking. + +| Column | Type | Description | +|--------|------|-------------| +| step | int | Training step | +| func | string | Function name | +| name | string | Variable name | +| value | string | String representation | + +--- + +### information_schema.df_settings + +Configuration settings. + +| Column | Type | Description | +|--------|------|-------------| +| name | string | Setting name | +| value | string | Setting value | + +## Configuration Options + +| Key | Default | Description | +|-----|---------|-------------| +| `probing.sample_rate` | 1.0 | Sampling rate (0.0-1.0) | +| `probing.buffer_size` | 10000 | Ring buffer size | +| `probing.server.port` | 0 | TCP port (0=Unix socket only) | +| `probing.torch.enabled` | true | Enable PyTorch tracing | + +## Environment Variables + +| Variable | Description | +|----------|-------------| +| `PROBING` | Enable probing (1=on) | +| `PROBING_PORT` | TCP server port | +| `PROBING_TORCH_PROFILING` | PyTorch profiling (on/off) | +| `PROBING_SAMPLE_RATE` | Default sample rate | +| `PROBING_AUTH_TOKEN` | Authentication token | diff --git a/docs/src/api-reference.zh.md b/docs/src/api-reference.zh.md new file mode 100644 index 00000000..89219538 --- /dev/null +++ b/docs/src/api-reference.zh.md @@ -0,0 +1,194 @@ +# API 参考 + +Probing CLI 命令和 Python API 的完整参考。 + +## CLI 命令 + +### probing inject + +将探针注入到运行中的进程。 + +```bash +probing -t inject +``` + +**选项：** + +- `-t, --target ` - 目标进程 ID（必需） + +**平台：** 仅 Linux + +--- + +### probing query + +对收集的数据执行 SQL 查询。 + +```bash +probing -t query "" +``` + +**示例：** + +```bash +# 查询 torch 追踪 +probing -t 12345 query "SELECT * FROM python.torch_trace LIMIT 10" + +# 聚合查询 +probing -t host:8080 query "SELECT module, AVG(duration) FROM python.torch_trace GROUP BY module" +``` + +--- + +### probing eval + +在目标进程中执行 Python 代码。 + +```bash +probing -t eval "" +``` + +**示例：** + +```bash +# 简单执行 +probing -t 12345 eval "print('hello')" + +# 多语句 +probing -t 12345 eval "import torch; print(torch.cuda.is_available())" +``` + +--- + +### probing backtrace + +捕获当前堆栈跟踪。 + +```bash +probing -t backtrace +``` + +**输出：** 包含函数名、文件和行号的堆栈帧。 + +--- + +### probing repl + +启动交互式 Python REPL。 + +```bash +probing -t repl +``` + +**功能：** + +- Tab 补全 +- 多行输入 +- 命令历史 + +--- + +### probing list + +列出启用了 probing 的进程。 + +```bash +probing list +``` + +**输出：** 进程 ID 及其 probing 状态。 + +--- + +### probing config + +查看或修改配置。 + +```bash +# 查看所有配置 +probing -t config + +# 查看特定键 +probing -t config probing.sample_rate + +# 设置值 +probing -t config probing.sample_rate=0.1 +``` + +## Python API + +### probing.connect + +连接到 probing 端点。 + +```python +from probing import connect + +# 通过 PID 连接 +probe = connect(pid=12345) + +# 通过地址连接 +probe = connect(address="host:8080") +``` + +--- + +### @probing.table + +注册自定义数据表。 + +```python +from probing import table + +@table("my_data") +def get_my_data(): + return [{"key": "value"}] +``` + +## SQL 表 + +### python.backtrace + +堆栈跟踪信息。 + +| 列 | 类型 | 描述 | +|----|------|------| +| func | string | 函数名 | +| file | string | 源文件 | +| lineno | int | 行号 | +| depth | int | 堆栈深度 | +| frame_type | string | Python/Native | + +--- + +### python.torch_trace + +PyTorch 执行跟踪。 + +| 列 | 类型 | 描述 | +|----|------|------| +| step | int | 训练步骤 | +| seq | int | 序列号 | +| module | string | 模块名 | +| stage | string | forward/backward/step | +| allocated | float | GPU 内存 (MB) | +| duration | float | 执行时间 (秒) | + +## 配置选项 + +| 键 | 默认值 | 描述 | +|----|--------|------| +| `probing.sample_rate` | 1.0 | 采样率 (0.0-1.0) | +| `probing.buffer_size` | 10000 | 环形缓冲区大小 | +| `probing.server.port` | 0 | TCP 端口 (0=仅 Unix socket) | +| `probing.torch.enabled` | true | 启用 PyTorch 追踪 | + +## 环境变量 + +| 变量 | 描述 | +|------|------| +| `PROBING` | 启用 probing (1=开启) | +| `PROBING_PORT` | TCP 服务器端口 | +| `PROBING_TORCH_PROFILING` | PyTorch 分析 (on/off) | +| `PROBING_SAMPLE_RATE` | 默认采样率 | +| `PROBING_AUTH_TOKEN` | 认证令牌 | diff --git a/docs/src/assets/stylesheets/home.css b/docs/src/assets/stylesheets/home.css new file mode 100644 index 00000000..6a802c24 --- /dev/null +++ b/docs/src/assets/stylesheets/home.css @@ -0,0 +1,223 @@ +/* Probing Homepage Styles - Emerald Green Theme */ + +/* Custom color palette - Emerald Green */ +:root { + --md-primary-fg-color: #10B981; + --md-primary-fg-color--light: #34D399; + --md-primary-fg-color--dark: #059669; + --md-accent-fg-color: #10B981; + --md-accent-fg-color--transparent: rgba(16, 185, 129, 0.1); +} + +/* ======================================== + HOMEPAGE ONLY STYLES + All styles below are scoped to .mdx-container + which only exists on the homepage + ======================================== */ + +/* Homepage container */ +.mdx-container .container { + width: 100%; + max-width: 1140px; + margin-right: auto; + margin-left: auto; + padding-right: 15px; + padding-left: 15px; +} + +/* Hero Section - Fullscreen with Gradient Background */ +.mdx-container { + text-align: center; + color: #f8f8f8; + background: linear-gradient(135deg, #064e3b 0%, #065f46 25%, #047857 50%, #10B981 75%, #34D399 100%); + min-height: 100vh; + height: 100vh; + display: flex; + align-items: center; + justify-content: center; + position: relative; + overflow: hidden; +} + +/* Animated background particles */ +.mdx-container::before { + content: ''; + position: absolute; + width: 200%; + height: 200%; + background: radial-gradient(circle, rgba(255,255,255,0.1) 1px, transparent 1px); + background-size: 50px 50px; + animation: pulse 20s linear infinite; + opacity: 0.3; +} + +@keyframes pulse { + 0% { transform: translate(-50%, -50%) rotate(0deg); } + 100% { transform: translate(-50%, -50%) rotate(360deg); } +} + +.mdx-container .intro-message { + position: relative; + padding: 40px 20px; + font-family: "Inter", -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif; + max-width: 1000px; + margin: 0 auto; + z-index: 1; +} + +.mdx-container .hero-logo { + display: inline-flex; + align-items: center; + margin-bottom: 16px; +} + +.mdx-container .hero-icon { + font-size: 80px; + margin-right: 20px; + filter: drop-shadow(3px 3px 8px rgba(0, 0, 0, 0.5)); +} + +.mdx-container .intro-message h1 { + font-weight: 700; + margin: 0; + display: inline-block; + text-shadow: 3px 3px 8px rgba(0, 0, 0, 0.5); + font-size: 6em; + line-height: 1.2; + color: #ffffff; + vertical-align: middle; + letter-spacing: -0.02em; +} + +.mdx-container .intro-message h3 { + font-size: 1.3rem; + text-shadow: 2px 2px 6px rgba(0, 0, 0, 0.5); + font-weight: 400; + margin-bottom: 32px; + color: rgba(255, 255, 255, 0.9); +} + +.mdx-container .intro-divider { + width: 400px; + max-width: 80%; + border-top: 1px solid rgba(255, 255, 255, 0.4); + border-bottom: none; + margin: 24px auto; +} + +.mdx-container .list-inline { + padding-left: 0; + margin-left: -5px; + list-style: none; + margin-bottom: 0; +} + +.mdx-container .list-inline li { + display: inline-block; + padding-right: 5px; + padding-left: 5px; +} + +.mdx-container .intro-message .md-button { + margin: 8px; + padding: 14px 36px; + font-size: 1rem; + font-weight: 600; + border-radius: 8px; + transition: all 0.3s ease; +} + +.mdx-container .intro-message .md-button--primary { + background: #ffffff; + color: #059669; + border: none; +} + +.mdx-container .intro-message .md-button:not(.md-button--primary) { + background: transparent; + color: #ffffff; + border: 2px solid rgba(255, 255, 255, 0.6); +} + +.mdx-container .intro-message .md-button:hover { + transform: translateY(-2px); + box-shadow: 0 8px 20px rgba(0, 0, 0, 0.3); +} + +.mdx-container .intro-message .md-button--primary:hover { + background: #f8f8f8; + box-shadow: 0 8px 25px rgba(255, 255, 255, 0.3); +} + +.mdx-container .intro-message .md-button:not(.md-button--primary):hover { + background: rgba(255, 255, 255, 0.1); + border-color: #ffffff; +} + +/* Feature section link styles */ +.probing-feature-section .md-button { + background-color: transparent; + color: #10B981; + text-decoration: underline; + border: none; + box-shadow: none; + padding: 0; +} + +.probing-feature-section .md-button:hover { + background-color: transparent; + color: #34D399; + text-decoration: underline; + transform: translateX(4px); +} + +/* Responsive Design */ +@media (max-width: 768px) { + .mdx-container .container { + padding-right: 16px; + padding-left: 16px; + } + + .mdx-container .intro-message h1 { + font-size: 3rem; + } + + .mdx-container .hero-icon { + font-size: 50px; + } + + .mdx-container .intro-message h3 { + font-size: 1rem; + } +} + +@media (max-width: 960px) { + .mdx-container .intro-message .list-inline li { + display: block; + padding: 0; + margin-bottom: 12px; + } + + .mdx-container .intro-message .md-button { + display: block; + margin: 0 auto; + width: 90%; + max-width: 350px; + } +} + +@media (max-width: 480px) { + .mdx-container .intro-message h1 { + font-size: 2.5rem; + } + + .mdx-container .hero-icon { + font-size: 40px; + margin-right: 10px; + } + + .mdx-container .intro-message .md-button { + max-width: 300px; + padding: 12px 24px; + } +} diff --git a/docs/src/contributing.md b/docs/src/contributing.md new file mode 100644 index 00000000..7ad996f0 --- /dev/null +++ b/docs/src/contributing.md @@ -0,0 +1,197 @@ +# Contributing Guide + +Thank you for your interest in contributing to Probing! This guide will help you get started. + +## Getting Started + +### Prerequisites + +- Python 3.9+ +- Rust (latest stable) +- maturin (for building Python extensions) + +### Development Setup + +1. Clone the repository: + +```bash +git clone https://github.com/DeepLink-org/probing.git +cd probing +``` + +2. Create a virtual environment: + +```bash +python -m venv .venv +source .venv/bin/activate # Linux/macOS +# or +.venv\Scripts\activate # Windows +``` + +3. Install development dependencies: + +```bash +pip install -e ".[dev]" +``` + +4. Build the Rust extension: + +```bash +maturin develop +``` + +## Development Workflow + +### Running Tests + +```bash +# Run all tests +make test + +# Run specific test +pytest tests/test_specific.py -v + +# Run with coverage +make coverage +``` + +### Code Style + +We use the following tools for code quality: + +**Python:** + +- `ruff` for linting and formatting +- `mypy` for type checking + +```bash +# Format code +ruff format . + +# Check linting +ruff check . + +# Type check +mypy python/probing +``` + +**Rust:** + +- `rustfmt` for formatting +- `clippy` for linting + +```bash +# Format +cargo fmt + +# Lint +cargo clippy --all --tests --benches -- -D warnings +``` + +### Building Documentation + +```bash +cd docs +make install # Install dependencies +make serve # Preview at http://127.0.0.1:8000 +``` + +## Submitting Changes + +### Pull Request Process + +1. Fork the repository +2. Create a feature branch: + +```bash +git checkout -b feature/your-feature-name +``` + +3. Make your changes +4. Run tests and linting: + +```bash +make test +make lint +``` + +5. Commit with a descriptive message: + +```bash +git commit -m "feat: add new feature description" +``` + +6. Push and create a pull request + +### Commit Message Format + +We follow [Conventional Commits](https://www.conventionalcommits.org/): + +- `feat:` - New feature +- `fix:` - Bug fix +- `docs:` - Documentation changes +- `style:` - Code style changes (formatting) +- `refactor:` - Code refactoring +- `test:` - Test changes +- `chore:` - Build/tooling changes + +### Code Review + +All submissions require code review. Please: + +- Keep PRs focused on a single change +- Add tests for new functionality +- Update documentation as needed +- Respond to review feedback promptly + +## Project Structure + +``` +probing/ +├── python/ # Python source code +│ └── probing/ # Main Python package +├── probing/ # Rust crates +│ ├── core/ # Core functionality +│ ├── server/ # HTTP server +│ ├── extensions/ # Python/PyTorch extensions +│ └── cli/ # Command-line interface +├── tests/ # Python tests +├── docs/ # Documentation +└── examples/ # Usage examples +``` + +## Areas for Contribution + +### Good First Issues + +Look for issues labeled `good-first-issue` on GitHub. These are suitable for newcomers. + +### Documentation + +- Improve existing documentation +- Add more examples +- Translate documentation + +### Testing + +- Add test coverage +- Write integration tests +- Performance benchmarks + +### Features + +- Check the roadmap and open issues +- Discuss large changes before implementing + +## Getting Help + +- **GitHub Issues**: For bugs and feature requests +- **Discussions**: For questions and ideas + +## Code of Conduct + +Please be respectful and constructive in all interactions. We are committed to providing a welcoming environment for everyone. + +## License + +By contributing, you agree that your contributions will be licensed under the project's Apache 2.0 license. diff --git a/docs/src/contributing.zh.md b/docs/src/contributing.zh.md new file mode 100644 index 00000000..c233d592 --- /dev/null +++ b/docs/src/contributing.zh.md @@ -0,0 +1,197 @@ +# 贡献指南 + +感谢您对贡献 Probing 的兴趣！本指南将帮助您开始。 + +## 入门 + +### 前提条件 + +- Python 3.9+ +- Rust（最新稳定版） +- maturin（用于构建 Python 扩展） + +### 开发环境设置 + +1. 克隆仓库： + +```bash +git clone https://github.com/DeepLink-org/probing.git +cd probing +``` + +2. 创建虚拟环境： + +```bash +python -m venv .venv +source .venv/bin/activate # Linux/macOS +# 或 +.venv\Scripts\activate # Windows +``` + +3. 安装开发依赖： + +```bash +pip install -e ".[dev]" +``` + +4. 构建 Rust 扩展： + +```bash +maturin develop +``` + +## 开发流程 + +### 运行测试 + +```bash +# 运行所有测试 +make test + +# 运行特定测试 +pytest tests/test_specific.py -v + +# 运行带覆盖率的测试 +make coverage +``` + +### 代码风格 + +我们使用以下工具保证代码质量： + +**Python：** + +- `ruff` 用于代码检查和格式化 +- `mypy` 用于类型检查 + +```bash +# 格式化代码 +ruff format . + +# 检查代码规范 +ruff check . + +# 类型检查 +mypy python/probing +``` + +**Rust：** + +- `rustfmt` 用于格式化 +- `clippy` 用于代码检查 + +```bash +# 格式化 +cargo fmt + +# 检查 +cargo clippy --all --tests --benches -- -D warnings +``` + +### 构建文档 + +```bash +cd docs +make install # 安装依赖 +make serve # 在 http://127.0.0.1:8000 预览 +``` + +## 提交变更 + +### Pull Request 流程 + +1. Fork 仓库 +2. 创建功能分支： + +```bash +git checkout -b feature/your-feature-name +``` + +3. 进行修改 +4. 运行测试和检查： + +```bash +make test +make lint +``` + +5. 使用描述性消息提交： + +```bash +git commit -m "feat: 添加新功能描述" +``` + +6. 推送并创建 Pull Request + +### 提交消息格式 + +我们遵循 [Conventional Commits](https://www.conventionalcommits.org/)： + +- `feat:` - 新功能 +- `fix:` - Bug 修复 +- `docs:` - 文档变更 +- `style:` - 代码风格变更（格式化） +- `refactor:` - 代码重构 +- `test:` - 测试变更 +- `chore:` - 构建/工具变更 + +### 代码审查 + +所有提交都需要代码审查。请： + +- 保持 PR 专注于单一变更 +- 为新功能添加测试 +- 根据需要更新文档 +- 及时响应审查反馈 + +## 项目结构 + +``` +probing/ +├── python/ # Python 源代码 +│ └── probing/ # 主 Python 包 +├── probing/ # Rust crates +│ ├── core/ # 核心功能 +│ ├── server/ # HTTP 服务器 +│ ├── extensions/ # Python/PyTorch 扩展 +│ └── cli/ # 命令行界面 +├── tests/ # Python 测试 +├── docs/ # 文档 +└── examples/ # 使用示例 +``` + +## 贡献领域 + +### 适合新手的 Issues + +在 GitHub 上查找标记为 `good-first-issue` 的问题。这些适合新贡献者。 + +### 文档 + +- 改进现有文档 +- 添加更多示例 +- 翻译文档 + +### 测试 + +- 增加测试覆盖率 +- 编写集成测试 +- 性能基准测试 + +### 功能 + +- 查看路线图和开放的 issues +- 大型变更前请先讨论 + +## 获取帮助 + +- **GitHub Issues**：用于 Bug 和功能请求 +- **Discussions**：用于问题和想法讨论 + +## 行为准则 + +请在所有互动中保持尊重和建设性。我们致力于为每个人提供友好的环境。 + +## 许可证 + +通过贡献，您同意您的贡献将根据项目的 Apache 2.0 许可证进行许可。 diff --git a/docs/src/design/README.md b/docs/src/design/README.md deleted file mode 100644 index b241c374..00000000 --- a/docs/src/design/README.md +++ /dev/null @@ -1,15 +0,0 @@ -# Design Overview - -## Why Python Dominates AI: The Pythonic Advantage - -Python's dominance in AI stems from one core principle: **everything feels like Python**. Whether you're using pandas, PyTorch, or NumPy, you can **talk to them pythonic**—the same `print()`, iteration, and attribute access patterns work everywhere. Interactive environments like REPL and Jupyter notebooks further reinforce this seamless development experience. - - -## How Distributed Systems Break the Pythonic Experience - -As AI models scale to distributed clusters, something fundamental breaks: **distributed systems aren't Pythonic**. Single-machine debugging feels natural—`print(model.parameters())`, `loss.item()`, `torch.cuda.memory_allocated()`—but distributed debugging forces you into system administration tools: `kubectl get nodes`, SSH sessions, log file parsing, monitoring dashboards. You lose the interactive, consistent, composable experience that makes Python powerful. - - -## Probing: Restoring the Pythonic Experience for Distributed Systems - -Probing's core mission is simple: **make distributed systems feel Pythonic again**. Your cluster, nodes, and distributed processes become Python objects with familiar interfaces—`len(cluster.nodes)`, `node.gpu_usage`, `cluster.training.status`. Interactive exploration, consistent patterns, and natural composition return to distributed debugging. Instead of context-switching between tools, you stay in Python and **talk to your distributed system pythonic**. diff --git a/docs/src/design/architecture.md b/docs/src/design/architecture.md index e1004b16..c565bf33 100644 --- a/docs/src/design/architecture.md +++ b/docs/src/design/architecture.md @@ -1,12 +1,173 @@ # System Architecture -Probing 整体被设计为简洁的两层结构： -1. probe探针，被注入到目标进程，进而获得目标进程全部资源的访问权限，包括Python解释器、文件、内存等； -2. cli命令行，用于控制进程内探针，读取数据或者执行代码。与probe探针通过RESTful API通信，用户也可以自行通过API控制probe探针； +Probing is designed as a simple two-layer structure to minimize complexity and deployment difficulty. -整体设计上尽可能避免引入不必要的组件，来降低整体整体复杂度与部署和使用难度。 +## High-Level Architecture -在probe探针内部，主要包含三部分组件： -1. engine：包含核心的数据存储与处理能力，并提供配置管理与扩展机制等基础设施； -2. server：负责与cli命令行的交互； -3. extensions：为探针提供性能数据，以及调试功能等； +```mermaid +graph TB + subgraph "User Interface" + CLI[CLI Client] + HTTP[HTTP API] + WEB[Web UI] + end + + subgraph "Target Process" + PROBE[Probe] + subgraph "Probe Components" + ENGINE[Engine] + SERVER[Server] + EXT[Extensions] + end + end + + CLI --> |Unix Socket / TCP| PROBE + HTTP --> |HTTP/REST| PROBE + WEB --> |WebSocket| PROBE + + PROBE --> ENGINE + PROBE --> SERVER + PROBE --> EXT +``` + +## Components + +### 1. Probe + +Injected into target processes to gain full access to all resources: + +- Python interpreter access +- File system access +- Memory inspection +- Network capabilities + +The probe runs an embedded HTTP server that listens on: + +- **Unix domain socket** - For local connections (default) +- **TCP port** - For remote connections + +### 2. CLI + +Command-line interface for controlling probes: + +- Process discovery and listing +- Probe injection and management +- Query execution +- Code evaluation + +Communication happens via HTTP protocol over Unix domain sockets (local) or TCP (remote). + +### 3. HTTP API + +RESTful API for programmatic access: + +- All CLI commands available as endpoints +- WebSocket support for real-time data +- Integration with monitoring tools + +## Probe Internal Architecture + +```mermaid +graph LR + subgraph "Probe" + SERVER[HTTP Server] + ENGINE[Query Engine] + CONFIG[Config Manager] + + subgraph "Extensions" + PYTHON[Python Extension] + TORCH[PyTorch Extension] + CUSTOM[Custom Tables] + end + end + + SERVER --> ENGINE + SERVER --> CONFIG + ENGINE --> PYTHON + ENGINE --> TORCH + ENGINE --> CUSTOM +``` + +### Engine + +Core data storage and processing: + +- **DataFusion** - SQL query engine +- **Arrow** - Columnar data format +- Time-series data storage +- Configuration management + +### Server + +HTTP server handling: + +- Request routing +- Authentication (optional) +- WebSocket connections +- Response formatting + +### Extensions + +Pluggable data providers: + +- **Python Extension** - Backtrace, variables +- **PyTorch Extension** - Torch traces, memory +- **Custom Tables** - User-defined data sources + +## Data Flow + +```mermaid +sequenceDiagram + participant CLI + participant Probe + participant Engine + participant Extension + + CLI->>Probe: query "SELECT * FROM python.torch_trace" + Probe->>Engine: Parse & Plan Query + Engine->>Extension: Request Data + Extension-->>Engine: Arrow RecordBatch + Engine-->>Probe: Query Results + Probe-->>CLI: JSON Response +``` + +## Communication Protocol + +### Local Connection + +``` +probing -t query "..." + | + v + Unix Socket: /tmp/probing-.sock + | + v + HTTP Request: POST /query +``` + +### Remote Connection + +``` +probing -t host:port query "..." + | + v + TCP Connection: host:port + | + v + HTTP Request: POST /query +``` + +## Security Considerations + +- **Local mode**: Unix socket permissions (process owner only) +- **Remote mode**: Optional authentication +- **Network**: Support for TLS encryption + +## Performance Characteristics + +| Aspect | Target | +|--------|--------| +| Overhead | < 5% in typical workloads | +| Memory | < 50MB additional | +| Latency | < 10ms for queries | +| Throughput | 1000+ queries/sec | diff --git a/docs/src/design/architecture.zh.md b/docs/src/design/architecture.zh.md new file mode 100644 index 00000000..40eb15c4 --- /dev/null +++ b/docs/src/design/architecture.zh.md @@ -0,0 +1,173 @@ +# 系统架构 + +Probing 采用简洁的两层架构设计，以最小化复杂性和部署难度。 + +## 高层架构 + +```mermaid +graph TB + subgraph "用户界面" + CLI[CLI 客户端] + HTTP[HTTP API] + WEB[Web UI] + end + + subgraph "目标进程" + PROBE[探针] + subgraph "探针组件" + ENGINE[引擎] + SERVER[服务器] + EXT[扩展] + end + end + + CLI --> |Unix Socket / TCP| PROBE + HTTP --> |HTTP/REST| PROBE + WEB --> |WebSocket| PROBE + + PROBE --> ENGINE + PROBE --> SERVER + PROBE --> EXT +``` + +## 组件 + +### 1. 探针 (Probe) + +注入到目标进程中，获得对所有资源的完全访问权限： + +- Python 解释器访问 +- 文件系统访问 +- 内存检查 +- 网络能力 + +探针运行一个嵌入式 HTTP 服务器，监听在： + +- **Unix 域套接字** - 用于本地连接（默认） +- **TCP 端口** - 用于远程连接 + +### 2. CLI + +用于控制探针的命令行界面： + +- 进程发现和列表 +- 探针注入和管理 +- 查询执行 +- 代码执行 + +通过 HTTP 协议在 Unix 域套接字（本地）或 TCP（远程）上通信。 + +### 3. HTTP API + +用于编程访问的 RESTful API： + +- 所有 CLI 命令都可作为端点使用 +- WebSocket 支持实时数据 +- 与监控工具集成 + +## 探针内部架构 + +```mermaid +graph LR + subgraph "探针" + SERVER[HTTP 服务器] + ENGINE[查询引擎] + CONFIG[配置管理] + + subgraph "扩展" + PYTHON[Python 扩展] + TORCH[PyTorch 扩展] + CUSTOM[自定义表] + end + end + + SERVER --> ENGINE + SERVER --> CONFIG + ENGINE --> PYTHON + ENGINE --> TORCH + ENGINE --> CUSTOM +``` + +### 引擎 + +核心数据存储和处理： + +- **DataFusion** - SQL 查询引擎 +- **Arrow** - 列式数据格式 +- 时间序列数据存储 +- 配置管理 + +### 服务器 + +HTTP 服务器处理： + +- 请求路由 +- 认证（可选） +- WebSocket 连接 +- 响应格式化 + +### 扩展 + +可插拔的数据提供者： + +- **Python 扩展** - 堆栈跟踪、变量 +- **PyTorch 扩展** - Torch 跟踪、内存 +- **自定义表** - 用户定义的数据源 + +## 数据流 + +```mermaid +sequenceDiagram + participant CLI + participant 探针 + participant 引擎 + participant 扩展 + + CLI->>探针: query "SELECT * FROM python.torch_trace" + 探针->>引擎: 解析 & 计划查询 + 引擎->>扩展: 请求数据 + 扩展-->>引擎: Arrow RecordBatch + 引擎-->>探针: 查询结果 + 探针-->>CLI: JSON 响应 +``` + +## 通信协议 + +### 本地连接 + +``` +probing -t query "..." + | + v + Unix Socket: /tmp/probing-.sock + | + v + HTTP 请求: POST /query +``` + +### 远程连接 + +``` +probing -t host:port query "..." + | + v + TCP 连接: host:port + | + v + HTTP 请求: POST /query +``` + +## 安全考虑 + +- **本地模式**: Unix 套接字权限（仅进程所有者） +- **远程模式**: 可选认证 +- **网络**: 支持 TLS 加密 + +## 性能特征 + +| 方面 | 目标 | +|------|------| +| 开销 | 典型工作负载 < 5% | +| 内存 | 额外 < 50MB | +| 延迟 | 查询 < 10ms | +| 吞吐量 | 1000+ 查询/秒 | diff --git a/docs/src/design/debugging.md b/docs/src/design/debugging.md new file mode 100644 index 00000000..5847fae2 --- /dev/null +++ b/docs/src/design/debugging.md @@ -0,0 +1,208 @@ +# Debugging Architecture + +Probing provides powerful debugging capabilities through code injection and stack analysis. + +## Overview + +The debugging subsystem enables: + +- Live code execution in target processes +- Stack trace capture with variable inspection +- Interactive REPL sessions +- Remote debugging support + +## Code Execution + +### Eval Command + +Execute arbitrary Python code in the target process context: + +```bash +probing $ENDPOINT eval "print(model.state_dict().keys())" +``` + +### Execution Flow + +```mermaid +sequenceDiagram + participant CLI + participant Server + participant Python + + CLI->>Server: POST /eval {"code": "..."} + Server->>Python: PyRun_String(code) + Python->>Python: Execute in __main__ + Python-->>Server: Result/Exception + Server-->>CLI: JSON Response +``` + +### Execution Context + +Code executes in the main module's global namespace: + +- Access to all imported modules +- Access to global variables +- Can modify state directly + +### Safety Considerations + +- Code runs with full privileges +- No sandboxing (by design) +- Use with appropriate access controls + +## Stack Analysis + +### Backtrace Capture + +Captures current execution stack: + +```bash +probing $ENDPOINT backtrace +``` + +### Frame Information + +Each stack frame includes: + +| Field | Description | +|-------|-------------| +| func | Function name | +| file | Source file path | +| lineno | Line number | +| depth | Stack depth (0 = innermost) | +| frame_type | Python or Native | +| locals | Local variables (optional) | + +### Query Stack + +```sql +SELECT func, file, lineno, depth +FROM python.backtrace +ORDER BY depth; +``` + +## Interactive REPL + +### Starting REPL + +```bash +probing -t repl +``` + +### REPL Features + +- Tab completion +- Multi-line input +- History support +- Exception display + +### Example Session + +```python +>>> import torch +>>> model = get_model() +>>> model.training +True +>>> torch.cuda.memory_allocated() / 1024**3 +2.5 +``` + +## Remote Debugging + +### Setup + +```bash +# On target machine +PROBING_PORT=8080 python train.py + +# From remote machine +probing -t host:8080 eval "..." +``` + +### Security + +- Authentication via tokens +- TLS encryption support +- IP-based access control + +## Debugging Patterns + +### Finding Hangs + +```bash +# Capture stack +probing $ENDPOINT backtrace + +# Check where execution is blocked +probing $ENDPOINT query " +SELECT func, file, lineno +FROM python.backtrace +WHERE depth < 5" +``` + +### Inspecting State + +```bash +# Check model state +probing $ENDPOINT eval " +for name, param in model.named_parameters(): + print(f'{name}: {param.shape}')" +``` + +### Modifying Behavior + +```bash +# Change learning rate +probing $ENDPOINT eval " +optimizer.param_groups[0]['lr'] = 0.0001" + +# Force checkpoint +probing $ENDPOINT eval " +trainer.save_checkpoint('debug_checkpoint.pt')" +``` + +## Thread Debugging + +### List Threads + +```bash +probing $ENDPOINT eval " +import threading +for t in threading.enumerate(): + print(f'{t.name}: alive={t.is_alive()}')" +``` + +### Main Thread Focus + +Backtrace captures the main thread by default. For other threads: + +```bash +probing $ENDPOINT eval " +import sys, traceback +for thread_id, frame in sys._current_frames().items(): + print(f'Thread {thread_id}:') + traceback.print_stack(frame)" +``` + +## Integration with IDEs + +### VS Code + +The HTTP API can be used for IDE integration: + +```json +{ + "type": "probing", + "request": "attach", + "endpoint": "localhost:8080" +} +``` + +### Jupyter + +```python +from probing import connect + +probe = connect(pid=12345) +probe.eval("print(globals().keys())") +``` diff --git a/docs/src/design/debugging.zh.md b/docs/src/design/debugging.zh.md new file mode 100644 index 00000000..f8149ab8 --- /dev/null +++ b/docs/src/design/debugging.zh.md @@ -0,0 +1,185 @@ +# 调试架构 + +Probing 通过代码注入和堆栈分析提供强大的调试能力。 + +## 概览 + +调试子系统支持： + +- 在目标进程中实时执行代码 +- 带变量检查的堆栈跟踪捕获 +- 交互式 REPL 会话 +- 远程调试支持 + +## 代码执行 + +### Eval 命令 + +在目标进程上下文中执行任意 Python 代码： + +```bash +probing $ENDPOINT eval "print(model.state_dict().keys())" +``` + +### 执行流程 + +```mermaid +sequenceDiagram + participant CLI + participant 服务器 + participant Python + + CLI->>服务器: POST /eval {"code": "..."} + 服务器->>Python: PyRun_String(code) + Python->>Python: 在 __main__ 中执行 + Python-->>服务器: 结果/异常 + 服务器-->>CLI: JSON 响应 +``` + +### 执行上下文 + +代码在主模块的全局命名空间中执行： + +- 访问所有已导入的模块 +- 访问全局变量 +- 可以直接修改状态 + +### 安全考虑 + +- 代码以完全权限运行 +- 无沙箱（设计如此） +- 使用适当的访问控制 + +## 堆栈分析 + +### 堆栈跟踪捕获 + +捕获当前执行堆栈： + +```bash +probing $ENDPOINT backtrace +``` + +### 帧信息 + +每个堆栈帧包括： + +| 字段 | 描述 | +|------|------| +| func | 函数名 | +| file | 源文件路径 | +| lineno | 行号 | +| depth | 堆栈深度（0 = 最内层）| +| frame_type | Python 或 Native | +| locals | 局部变量（可选）| + +### 查询堆栈 + +```sql +SELECT func, file, lineno, depth +FROM python.backtrace +ORDER BY depth; +``` + +## 交互式 REPL + +### 启动 REPL + +```bash +probing -t repl +``` + +### REPL 功能 + +- Tab 补全 +- 多行输入 +- 历史记录支持 +- 异常显示 + +### 示例会话 + +```python +>>> import torch +>>> model = get_model() +>>> model.training +True +>>> torch.cuda.memory_allocated() / 1024**3 +2.5 +``` + +## 远程调试 + +### 设置 + +```bash +# 在目标机器上 +PROBING_PORT=8080 python train.py + +# 从远程机器 +probing -t host:8080 eval "..." +``` + +### 安全 + +- 通过令牌认证 +- 支持 TLS 加密 +- 基于 IP 的访问控制 + +## 调试模式 + +### 查找卡住位置 + +```bash +# 捕获堆栈 +probing $ENDPOINT backtrace + +# 检查执行阻塞在哪里 +probing $ENDPOINT query " +SELECT func, file, lineno +FROM python.backtrace +WHERE depth < 5" +``` + +### 检查状态 + +```bash +# 检查模型状态 +probing $ENDPOINT eval " +for name, param in model.named_parameters(): + print(f'{name}: {param.shape}')" +``` + +### 修改行为 + +```bash +# 修改学习率 +probing $ENDPOINT eval " +optimizer.param_groups[0]['lr'] = 0.0001" + +# 强制保存检查点 +probing $ENDPOINT eval " +trainer.save_checkpoint('debug_checkpoint.pt')" +``` + +## 线程调试 + +### 列出线程 + +```bash +probing $ENDPOINT eval " +import threading +for t in threading.enumerate(): + print(f'{t.name}: alive={t.is_alive()}')" +``` + +### 主线程焦点 + +默认情况下，backtrace 捕获主线程。对于其他线程： + +```bash +probing $ENDPOINT eval " +import sys, traceback +for thread_id, frame in sys._current_frames().items(): + print(f'线程 {thread_id}:') + traceback.print_stack(frame)" +``` diff --git a/docs/src/design/distributed.md b/docs/src/design/distributed.md new file mode 100644 index 00000000..871f2dcc --- /dev/null +++ b/docs/src/design/distributed.md @@ -0,0 +1,217 @@ +# Distributed Architecture + +Probing supports monitoring and debugging distributed AI workloads across multiple nodes. + +## Overview + +Distributed training introduces challenges: + +- Multiple processes across nodes +- Communication between ranks +- Synchronized debugging needs +- Cross-node data correlation + +Probing addresses these through a distributed architecture. + +## Architecture + +```mermaid +graph TB + subgraph "Node 1" + P1[Process Rank 0] + PROBE1[Probe] + end + + subgraph "Node 2" + P2[Process Rank 1] + PROBE2[Probe] + end + + subgraph "Control Plane" + CLI[CLI Client] + AGG[Aggregator] + end + + PROBE1 --> AGG + PROBE2 --> AGG + CLI --> AGG + CLI --> PROBE1 + CLI --> PROBE2 +``` + +## Process Discovery + +### Local Discovery + +```bash +# List all probing-enabled processes on local machine +probing list +``` + +### Remote Discovery + +```bash +# Connect to remote node +probing -t node1:8080 list +probing -t node2:8080 list +``` + +### Cluster View + +```bash +# Aggregate view across cluster +probing cluster list +``` + +## Cross-Node Queries + +### Query Single Node + +```bash +probing -t node1:8080 query " +SELECT * FROM python.torch_trace +WHERE step = (SELECT MAX(step) FROM python.torch_trace)" +``` + +### Query All Nodes + +```bash +# Federated query (future feature) +probing cluster query " +SELECT node, rank, step, loss +FROM python.training_metrics +ORDER BY step" +``` + +## Synchronized Debugging + +### Capture All Stacks + +```bash +# Capture stack traces from all ranks +for node in node1 node2 node3; do + echo "=== $node ===" + probing -t $node:8080 backtrace +done +``` + +### Check Distributed State + +```bash +probing -t $ENDPOINT eval " +import torch.distributed as dist + +if dist.is_initialized(): + print(f'Rank: {dist.get_rank()}') + print(f'World Size: {dist.get_world_size()}') + print(f'Backend: {dist.get_backend()}')" +``` + +## Communication Analysis + +### NCCL Operations + +```sql +-- Analyze communication patterns +SELECT + operation, + src_rank, + dst_rank, + bytes_transferred, + duration_ms +FROM python.nccl_trace +WHERE step = (SELECT MAX(step) FROM python.nccl_trace) +ORDER BY duration_ms DESC; +``` + +### RDMA Flow Analysis + +```bash +# RDMA-specific analysis +probing -t $ENDPOINT rdma +``` + +## Troubleshooting Distributed Issues + +### Rank Synchronization + +```bash +# Check if all ranks are at same step +for node in node1 node2 node3; do + probing -t $node:8080 eval "print(f'Step: {trainer.current_step}')" +done +``` + +### Deadlock Detection + +```bash +# Check for hanging collective operations +probing -t $ENDPOINT query " +SELECT func, file, lineno +FROM python.backtrace +WHERE func LIKE '%collective%' OR func LIKE '%allreduce%'" +``` + +### Memory Imbalance + +```sql +-- Compare memory across ranks +SELECT + rank, + AVG(allocated) as avg_memory, + MAX(allocated) as peak_memory +FROM python.torch_trace +GROUP BY rank; +``` + +## Configuration + +### Enable Remote Access + +```bash +# Start with TCP server +PROBING_PORT=8080 python train.py + +# Or configure dynamically +probing $ENDPOINT config probing.server.port=8080 +``` + +### Security + +```bash +# Enable authentication +PROBING_AUTH_TOKEN=secret python train.py + +# Connect with token +probing -t host:8080 --token secret query "..." +``` + +## Best Practices + +### 1. Consistent Configuration + +Use same configuration across all nodes: + +```bash +export PROBING_PORT=8080 +export PROBING_TORCH_PROFILING=on +``` + +### 2. Centralized Collection + +For large clusters, consider aggregation: + +```bash +# Export data to central location +probing -t $node query "SELECT * FROM python.torch_trace" >> /shared/traces.json +``` + +### 3. Timestamp Synchronization + +Ensure NTP is configured for accurate cross-node timing. + +### 4. Network Considerations + +- Use dedicated network for probing traffic if possible +- Consider firewall rules for probing ports +- Monitor probing overhead on training network diff --git a/docs/src/design/distributed.zh.md b/docs/src/design/distributed.zh.md new file mode 100644 index 00000000..aa3aa6e7 --- /dev/null +++ b/docs/src/design/distributed.zh.md @@ -0,0 +1,217 @@ +# 分布式架构 + +Probing 支持监控和调试跨多节点的分布式 AI 工作负载。 + +## 概览 + +分布式训练带来了挑战： + +- 跨节点的多进程 +- Rank 之间的通信 +- 同步调试需求 +- 跨节点数据关联 + +Probing 通过分布式架构解决这些问题。 + +## 架构 + +```mermaid +graph TB + subgraph "节点 1" + P1[进程 Rank 0] + PROBE1[探针] + end + + subgraph "节点 2" + P2[进程 Rank 1] + PROBE2[探针] + end + + subgraph "控制平面" + CLI[CLI 客户端] + AGG[聚合器] + end + + PROBE1 --> AGG + PROBE2 --> AGG + CLI --> AGG + CLI --> PROBE1 + CLI --> PROBE2 +``` + +## 进程发现 + +### 本地发现 + +```bash +# 列出本地机器上所有启用 probing 的进程 +probing list +``` + +### 远程发现 + +```bash +# 连接到远程节点 +probing -t node1:8080 list +probing -t node2:8080 list +``` + +### 集群视图 + +```bash +# 跨集群的聚合视图 +probing cluster list +``` + +## 跨节点查询 + +### 查询单个节点 + +```bash +probing -t node1:8080 query " +SELECT * FROM python.torch_trace +WHERE step = (SELECT MAX(step) FROM python.torch_trace)" +``` + +### 查询所有节点 + +```bash +# 联合查询（未来功能） +probing cluster query " +SELECT node, rank, step, loss +FROM python.training_metrics +ORDER BY step" +``` + +## 同步调试 + +### 捕获所有堆栈 + +```bash +# 从所有 rank 捕获堆栈跟踪 +for node in node1 node2 node3; do + echo "=== $node ===" + probing -t $node:8080 backtrace +done +``` + +### 检查分布式状态 + +```bash +probing -t $ENDPOINT eval " +import torch.distributed as dist + +if dist.is_initialized(): + print(f'Rank: {dist.get_rank()}') + print(f'World Size: {dist.get_world_size()}') + print(f'Backend: {dist.get_backend()}')" +``` + +## 通信分析 + +### NCCL 操作 + +```sql +-- 分析通信模式 +SELECT + operation, + src_rank, + dst_rank, + bytes_transferred, + duration_ms +FROM python.nccl_trace +WHERE step = (SELECT MAX(step) FROM python.nccl_trace) +ORDER BY duration_ms DESC; +``` + +### RDMA 流分析 + +```bash +# RDMA 特定分析 +probing -t $ENDPOINT rdma +``` + +## 分布式问题排查 + +### Rank 同步 + +```bash +# 检查所有 rank 是否在相同步骤 +for node in node1 node2 node3; do + probing -t $node:8080 eval "print(f'Step: {trainer.current_step}')" +done +``` + +### 死锁检测 + +```bash +# 检查挂起的集合操作 +probing -t $ENDPOINT query " +SELECT func, file, lineno +FROM python.backtrace +WHERE func LIKE '%collective%' OR func LIKE '%allreduce%'" +``` + +### 内存不均衡 + +```sql +-- 比较各 rank 的内存 +SELECT + rank, + AVG(allocated) as avg_memory, + MAX(allocated) as peak_memory +FROM python.torch_trace +GROUP BY rank; +``` + +## 配置 + +### 启用远程访问 + +```bash +# 以 TCP 服务器启动 +PROBING_PORT=8080 python train.py + +# 或动态配置 +probing $ENDPOINT config probing.server.port=8080 +``` + +### 安全 + +```bash +# 启用认证 +PROBING_AUTH_TOKEN=secret python train.py + +# 带令牌连接 +probing -t host:8080 --token secret query "..." +``` + +## 最佳实践 + +### 1. 一致的配置 + +在所有节点上使用相同配置： + +```bash +export PROBING_PORT=8080 +export PROBING_TORCH_PROFILING=on +``` + +### 2. 集中收集 + +对于大型集群，考虑聚合： + +```bash +# 导出数据到中心位置 +probing -t $node query "SELECT * FROM python.torch_trace" >> /shared/traces.json +``` + +### 3. 时间戳同步 + +确保配置 NTP 以获得准确的跨节点时间。 + +### 4. 网络考虑 + +- 尽可能使用专用网络进行 probing 流量 +- 考虑 probing 端口的防火墙规则 +- 监控 probing 对训练网络的开销 diff --git a/docs/src/design/extensibility.md b/docs/src/design/extensibility.md index ec554ff9..907f7c1c 100644 --- a/docs/src/design/extensibility.md +++ b/docs/src/design/extensibility.md @@ -1,195 +1,277 @@ -# Extensibility Framework +# Extensibility -Probing的核心能力在于，它既能深入目标进程获取关键的性能与故障数据，也能灵活地向目标进程植入诊断逻辑与定制代码。为了满足多样化的监控与分析需求，用户可以通过其强大的扩展系统来定制和增强Probing的功能，从而更有效地解决特定场景下的问题。 +Probing provides mechanisms for extending its capabilities with custom data sources and metrics. -Probing提供了两种主要的扩展路径：基于Python的轻量级扩展与基于Rust的高性能扩展。Python扩展以其无需重新构建Probing的便捷性，在实际生产环境中展现出色的灵活性；而Rust扩展则为开发者提供了更接近底层的控制能力和更高的执行效率，适用于对性能有极致要求或需要深度集成系统的场景。 +## Overview -## 基于Python扩展Probing +The extension system allows: -### SQL直接调用python +- Custom data tables +- User-defined metrics +- Integration with external tools +- Plugin architecture -Probing支持在SQL查询中直接调用Python函数或者变量，作为一个快速的外部表： +## Custom Tables -```bash -# 调用Python标准库函数 -probing $ENDPOINT query "SELECT * from python.`time.time()`" +### Python API +Create custom tables using the `@table` decorator: -# 调用自定义包函数 -probing $ENDPOINT query "SELECT * FROM python.`pkg.some_func()`" +```python +from probing import table + +@table("my_metrics") +def get_metrics(): + """Return data as list of dicts or pandas DataFrame.""" + return [ + {"name": "loss", "value": current_loss}, + {"name": "accuracy", "value": current_acc}, + ] ``` -在`python`这个namespace下，通过反引号"`"来引用一段python代码，Probing会将该代码执行，并将结果作为表返回给接下来的SQL来执行。 +### Query Custom Tables + +```sql +SELECT * FROM python.my_metrics; +``` -### Python写入Probing +### Table Schema -Probing支持使用`dataclass`自定义数据表： +Tables are dynamically typed based on returned data: ```python -from dataclasses import dataclass -from probing.core import table +@table("training_state") +def get_training_state(): + return { + "step": trainer.current_step, # int + "loss": trainer.last_loss, # float + "lr": optimizer.param_groups[0]["lr"], # float + "epoch": trainer.current_epoch, # int + } +``` -@table -@dataclass -class MetricData: - timestamp: int - cpu_usage: float - memory_mb: int - process_name: str +## External Table Integration -# 写入数据 -MetricData.append(MetricData(1234567890, 85.2, 1024, "python")) +### Pandas DataFrames -# 或者更加直接 -MetricData(1234567890, 85.2, 1024, "python").save() -``` -写入的数据可以通过SQL来查询 -```bash -probing $ENDPOINT query "SELECT * FROM python.metric_data" +```python +import pandas as pd +from probing import register_table + +df = pd.DataFrame({ + "timestamp": timestamps, + "metric": values +}) + +register_table("external_metrics", df) ``` -## Rust扩展系统 +### Arrow Tables -使用Rust语言，开发者可以更直接、更深入地扩展Probing的数据查询与处理能力。 +```python +import pyarrow as pa +from probing import register_table -### 核心接口 +table = pa.table({ + "id": [1, 2, 3], + "value": [10.0, 20.0, 30.0] +}) -```rust -pub trait EngineExtension: Debug + Send + Sync + EngineCall + EngineDatasource { - fn name(&self) -> String; // 扩展名称 - fn set(&mut self, key: &str, value: &str) -> Result<...>; // 设置配置 - fn get(&self, key: &str) -> Result; // 获取配置 - fn options(&self) -> Vec; // 列出所有配置项 -} +register_table("arrow_data", table) ``` -`EngineDatasource`接口负责向Probing的数据处理核心`DataFusion`输送数据，而`EngineCall`接口则赋予Probing服务器额外的远程API调用能力。这套设计精巧的接口共同构成了Probing扩展系统的基石。 +## Custom Metrics -### 数据源核心接口 - EngineDatasource +### Defining Metrics -Probing的数据处理能力建立在一个灵活的数据源抽象之上。当我们谈论性能诊断和监控时，数据收集是一切分析的基础。`EngineDatasource`接口正是这个数据桥梁的核心所在，它负责将各式各样的数据源接入Probing的查询引擎。 - -```rust -pub trait EngineDatasource { - fn datasrc(&self, namespace: &str, name: Option<&str>) - -> Option>; -} +```python +from probing import metric + +@metric("gpu_utilization") +def gpu_util(): + """Return current GPU utilization.""" + import pynvml + pynvml.nvmlInit() + handle = pynvml.nvmlDeviceGetHandleByIndex(0) + util = pynvml.nvmlDeviceGetUtilizationRates(handle) + return util.gpu ``` -`EngineExtension`通过实现`EngineDatasource`接口向Probing提供数据源插件。在Probing中，所有数据都以`namespace.table_name`的形式进行组织和访问。基于此，数据源插件主要分为两种类型： +### Querying Metrics -- `CustomTable` (静态表格插件): 此类插件提供具有固定结构和内容的数据表，非常适合用于展示系统固有信息、配置项或不经常变动的数据集。 -- `CustomNamespace` (动态命名空间插件): 此类插件能够根据查询请求动态地生成表格列表和内容，适用于封装那些数据结构或内容频繁变化的复杂数据源，或是与外部系统交互获取实时数据。 +```sql +SELECT * FROM python.metrics WHERE name = 'gpu_utilization'; +``` -这两种插件类型共同构成了Probing数据访问的骨架。可以将静态表格想象成数据仓库中预先定义好的、结构固定的视图，而动态命名空间则更像一个智能的、按需服务的数据代理，能够灵活连接并转换来自各种外部系统的数据。这种双层设计既保证了在处理简单、静态数据时的易用性与高效性，也为集成复杂、动态的数据源提供了充足的灵活性和强大的功能。 +## Hook System -通过这种统一的接口设计，用户可以将各种原本零散、异构的数据源整合到Probing强大的SQL查询体系之中。这意味着开发者不再需要为每一种新的数据格式或数据源编写专门的解析和处理代码。无论是监控CPU使用率这样的系统指标，还是分析网络流量、检查应用日志，甚至是查询自定义的业务数据，都可以通过标准、一致的SQL接口来完成，极大地简化了数据获取与分析的复杂度。 +### Module Hooks + +```python +from probing import register_hook + +@register_hook("torch.nn.Linear", "forward") +def linear_hook(module, input, output): + """Called on every Linear forward pass.""" + record_custom_data({ + "module": str(module), + "input_shape": list(input[0].shape), + "output_shape": list(output.shape), + }) +``` -这个看似简单的`EngineDatasource`接口，其背后蕴含着强大的扩展能力。它允许Probing以一种统一和标准化的方式，访问种类繁多的异构数据源——从操作系统内部的性能计数器、应用程序暴露的内部状态信息，到外部数据库的查询结果，甚至是远程微服务的API响应。当一个扩展实现了这个接口时，它实际上是在为Probing庞大的数据宇宙贡献一个新的、可被探索和分析的维度。具体来说，该接口的设计带来了以下关键优势： +### Function Hooks -- **清晰的命名空间管理**：通过`namespace`参数，Probing能够将来自不同来源、不同类型的数据进行逻辑上的分组与隔离，从而形成层次清晰、易于管理的数据视图。 -- **灵活的动态数据发现**：`name`参数的可选性设计，使得`EngineDatasource`接口既能支持对特定数据表的精确查询，也能够支持对整个命名空间下所有可用数据源的动态发现和枚举，这对于探索未知或动态变化的数据环境尤为重要。 -- **强大的多态插件系统**：接口的返回值采用Rust语言的特征对象（trait object）设计，这使得不同类型、不同实现的数据源插件可以被无缝地集成到Probing统一的查询框架之中，并被一致地处理。 +```python +from probing import hook_function + +@hook_function("torch.optim.Adam.step") +def optimizer_hook(optimizer): + """Called on every optimizer step.""" + record_custom_data({ + "lr": optimizer.param_groups[0]["lr"], + "step_count": optimizer.state_dict()["state"][0]["step"], + }) +``` -一个典型的`EngineDatasource`实现，可能会连接到操作系统的性能计数器以收集系统负载信息，或者查询应用程序的内部状态以监控其健康度，也可以是从一个专门的数据库中拉取业务数据，甚至是调用一个远程服务API来获取第三方信息。通过`EngineDatasource`接口的适配与转换，Probing将这些来源各异的数据统一转化为结构化的、可被SQL查询的表格形式，从而使开发者能够运用他们所熟悉的SQL语法，进行复杂的、跨数据源的深度数据分析与洞察。 +## Plugin Architecture -### 数据源插件 +### Creating a Plugin -当我们深入了解Probing的数据访问机制，会发现其真正的力量来自于多样化的数据源插件实现。Probing提供了两种核心插件类型，各自承担不同的数据访问职责。 +```python +# my_plugin.py +from probing import Plugin -**静态表格插件（TablePlugin）：** +class MyPlugin(Plugin): + name = "my_plugin" -静态表格插件是Probing扩展系统中最直观的数据提供者。它们就像是预先定义好的数据视图，具有固定的结构和内容，非常适合展示系统配置、静态信息或缓存数据。 + def on_load(self): + """Called when plugin is loaded.""" + self.register_table("plugin_data", self.get_data) -```rust -pub trait CustomTable { - fn name() -> &'static str; // 表名 - fn schema() -> SchemaRef; // 表结构 - fn data() -> Vec; // 数据批次 -} + def on_unload(self): + """Called when plugin is unloaded.""" + pass -// 注册插件 -let plugin = TablePluginHelper::::create("namespace", "table_name"); + def get_data(self): + return [{"key": "value"}] ``` -实现这个特性的插件能够以最小的代码量提供数据访问能力。开发者只需定义表名、数据结构和内容，Probing就会自动处理SQL查询解析、数据过滤和结果返回等操作。这种简洁性使得快速开发特定用途的数据表变得非常容易。 +### Loading Plugins -在性能诊断场景中，静态表格可用于展示系统基础信息，如CPU核心数、内存配置、运行时环境变量或编译选项等。这些信息虽然简单，但对于理解系统整体状态至关重要。 +```bash +# Environment variable +PROBING_PLUGINS=my_plugin python train.py -**动态表格插件（SchemaPlugin）：** -```rust -#[async_trait] -pub trait CustomNamespace { - async fn table_names(&self) -> Result>; - async fn table(&self, name: &str) -> Result>; -} +# Or programmatically +import probing +probing.load_plugin("my_plugin") ``` -与静态表格不同，动态命名空间插件提供了更强大的灵活性。它可以根据查询请求动态生成表格列表和内容，实现与外部数据源的实时交互。这种设计非常适合处理变化的数据集，例如实时监控指标、日志流或远程API响应。 +## Configuration Extension -动态命名空间的核心优势在于其自适应能力。`table_names`方法允许插件根据当前环境动态发现可用数据表，而`table`方法则负责按需创建特定表格的查询接口。这种延迟加载策略不仅提高了资源利用效率，还使得插件能够适应复杂多变的数据源环境。 +### Custom Config Options -在实际应用中，一个动态命名空间插件可能会连接到操作系统的进程列表，并为每个进程动态创建独立的数据表，展示其资源使用情况、打开的文件句柄或内存映射等信息。这种动态生成的表格集合为开发者提供了丰富而精准的系统视图。 +```python +from probing import config + +# Register custom config +config.register("my_plugin.sample_rate", default=0.1, type=float) + +# Use in plugin +rate = config.get("my_plugin.sample_rate") +``` + +### Query Configuration -### API接口插件 +```sql +SELECT * FROM information_schema.df_settings +WHERE name LIKE 'my_plugin.%'; +``` + +## Integration Examples -除了数据源插件外，Probing还提供了另一个强大的扩展点：API接口。通过`EngineCall`特性，扩展可以向Probing服务器添加自定义的HTTP端点，实现更复杂的交互逻辑和命令执行能力。 +### Weights & Biases -```rust -#[async_trait] -pub trait EngineCall { - async fn call( - &self, - path: &str, // API路径 - params: &HashMap, // 查询参数 - body: &[u8], // 请求体 - ) -> Result, EngineError>; // 响应数据 -} +```python +from probing import table +import wandb + +@table("wandb_metrics") +def get_wandb_metrics(): + run = wandb.run + if run: + return { + "run_id": run.id, + "step": run.step, + "summary": dict(run.summary), + } + return {} ``` -这个接口设计简洁而灵活，类似于一个微型的Web服务框架。扩展可以通过实现这个特性来处理来自客户端的各种请求： +### TensorBoard + +```python +from probing import table +from torch.utils.tensorboard import SummaryWriter -- 执行动态生成的代码片段 -- 注入诊断逻辑到目标进程 -- 提供交互式调试控制 -- 实时修改监控参数 -- 导出分析结果到外部格式 +writer = SummaryWriter() -API接口和数据源的结合形成了Probing的完整能力闭环：数据源提供了观测系统状态的"眼睛"，而API接口则是干预系统行为的"手"。通过这两种能力的协同，扩展可以实现从问题发现到故障诊断再到动态修复的完整工作流。 +@table("tensorboard_scalars") +def get_tb_scalars(): + # Access TensorBoard data + return logged_scalars +``` -**现有Rust扩展示例：** -- **C/C++扩展**：原生代码调试、内存分析、系统调用追踪 -- **Python扩展**：REPL、火焰图、调用栈追踪、对象检查 -- **Torch扩展**：PyTorch模型监控和性能分析 +### Prometheus -### 配置管理 +```python +from probing import metric +from prometheus_client import Gauge -```rust -// 扩展配置项定义 -pub struct EngineExtensionOption { - pub key: String, // 配置键 - pub value: Option, // 当前值 - pub help: &'static str, // 帮助信息 -} +gpu_memory = Gauge("gpu_memory_bytes", "GPU memory usage") -// 运行时配置 -manager.set_option("my_option", "new_value")?; -let value = manager.get_option("my_option")?; +@metric("prometheus_gpu_memory") +def update_prometheus(): + mem = torch.cuda.memory_allocated() + gpu_memory.set(mem) + return mem ``` -## 扩展开发指南 +## Best Practices + +### 1. Lightweight Data Collection + +```python +# Good: Return only necessary data +@table("efficient") +def get_efficient(): + return {"step": step, "loss": loss} + +# Avoid: Expensive operations +@table("expensive") +def get_expensive(): + return serialize_entire_model() # Too heavy +``` -### Python扩展开发 +### 2. Error Handling -1. **轻度扩展**：直接编写Python函数，在SQL中调用 -2. **重度扩展**：使用`@table`装饰器创建数据表，支持持久化 -3. **测试集成**：确保数据类型转换正确，SQL查询正常 +```python +@table("safe_data") +def get_safe_data(): + try: + return {"value": compute_value()} + except Exception as e: + return {"error": str(e)} +``` -### Rust扩展开发 +### 3. Caching -1. **实现核心trait**：根据需求实现`EngineCall`和/或`EngineDatasource` -2. **定义配置项**：在`options()`方法中列出所有可配置参数 -3. **注册插件**：通过`EngineExtensionManager`注册到系统 -4. **性能优化**：利用Arrow列式存储和DataFusion查询优化 +```python +from functools import lru_cache -扩展系统与DataFusion查询引擎深度集成，支持标准SQL语法访问所有扩展提供的数据源。 +@table("cached_data") +@lru_cache(maxsize=1) +def get_cached_data(): + return expensive_computation() +``` diff --git a/docs/src/design/extensibility.zh.md b/docs/src/design/extensibility.zh.md new file mode 100644 index 00000000..25ee3819 --- /dev/null +++ b/docs/src/design/extensibility.zh.md @@ -0,0 +1,277 @@ +# 扩展机制 + +Probing 提供机制来扩展其功能，支持自定义数据源和指标。 + +## 概览 + +扩展系统允许： + +- 自定义数据表 +- 用户定义的指标 +- 与外部工具集成 +- 插件架构 + +## 自定义表 + +### Python API + +使用 `@table` 装饰器创建自定义表： + +```python +from probing import table + +@table("my_metrics") +def get_metrics(): + """返回字典列表或 pandas DataFrame。""" + return [ + {"name": "loss", "value": current_loss}, + {"name": "accuracy", "value": current_acc}, + ] +``` + +### 查询自定义表 + +```sql +SELECT * FROM python.my_metrics; +``` + +### 表 Schema + +表根据返回数据动态类型化： + +```python +@table("training_state") +def get_training_state(): + return { + "step": trainer.current_step, # int + "loss": trainer.last_loss, # float + "lr": optimizer.param_groups[0]["lr"], # float + "epoch": trainer.current_epoch, # int + } +``` + +## 外部表集成 + +### Pandas DataFrames + +```python +import pandas as pd +from probing import register_table + +df = pd.DataFrame({ + "timestamp": timestamps, + "metric": values +}) + +register_table("external_metrics", df) +``` + +### Arrow 表 + +```python +import pyarrow as pa +from probing import register_table + +table = pa.table({ + "id": [1, 2, 3], + "value": [10.0, 20.0, 30.0] +}) + +register_table("arrow_data", table) +``` + +## 自定义指标 + +### 定义指标 + +```python +from probing import metric + +@metric("gpu_utilization") +def gpu_util(): + """返回当前 GPU 利用率。""" + import pynvml + pynvml.nvmlInit() + handle = pynvml.nvmlDeviceGetHandleByIndex(0) + util = pynvml.nvmlDeviceGetUtilizationRates(handle) + return util.gpu +``` + +### 查询指标 + +```sql +SELECT * FROM python.metrics WHERE name = 'gpu_utilization'; +``` + +## 钩子系统 + +### 模块钩子 + +```python +from probing import register_hook + +@register_hook("torch.nn.Linear", "forward") +def linear_hook(module, input, output): + """每次 Linear 前向传播时调用。""" + record_custom_data({ + "module": str(module), + "input_shape": list(input[0].shape), + "output_shape": list(output.shape), + }) +``` + +### 函数钩子 + +```python +from probing import hook_function + +@hook_function("torch.optim.Adam.step") +def optimizer_hook(optimizer): + """每次优化器步骤时调用。""" + record_custom_data({ + "lr": optimizer.param_groups[0]["lr"], + "step_count": optimizer.state_dict()["state"][0]["step"], + }) +``` + +## 插件架构 + +### 创建插件 + +```python +# my_plugin.py +from probing import Plugin + +class MyPlugin(Plugin): + name = "my_plugin" + + def on_load(self): + """插件加载时调用。""" + self.register_table("plugin_data", self.get_data) + + def on_unload(self): + """插件卸载时调用。""" + pass + + def get_data(self): + return [{"key": "value"}] +``` + +### 加载插件 + +```bash +# 环境变量 +PROBING_PLUGINS=my_plugin python train.py + +# 或编程方式 +import probing +probing.load_plugin("my_plugin") +``` + +## 配置扩展 + +### 自定义配置选项 + +```python +from probing import config + +# 注册自定义配置 +config.register("my_plugin.sample_rate", default=0.1, type=float) + +# 在插件中使用 +rate = config.get("my_plugin.sample_rate") +``` + +### 查询配置 + +```sql +SELECT * FROM information_schema.df_settings +WHERE name LIKE 'my_plugin.%'; +``` + +## 集成示例 + +### Weights & Biases + +```python +from probing import table +import wandb + +@table("wandb_metrics") +def get_wandb_metrics(): + run = wandb.run + if run: + return { + "run_id": run.id, + "step": run.step, + "summary": dict(run.summary), + } + return {} +``` + +### TensorBoard + +```python +from probing import table +from torch.utils.tensorboard import SummaryWriter + +writer = SummaryWriter() + +@table("tensorboard_scalars") +def get_tb_scalars(): + # 访问 TensorBoard 数据 + return logged_scalars +``` + +### Prometheus + +```python +from probing import metric +from prometheus_client import Gauge + +gpu_memory = Gauge("gpu_memory_bytes", "GPU 内存使用") + +@metric("prometheus_gpu_memory") +def update_prometheus(): + mem = torch.cuda.memory_allocated() + gpu_memory.set(mem) + return mem +``` + +## 最佳实践 + +### 1. 轻量级数据收集 + +```python +# 好：只返回必要数据 +@table("efficient") +def get_efficient(): + return {"step": step, "loss": loss} + +# 避免：昂贵的操作 +@table("expensive") +def get_expensive(): + return serialize_entire_model() # 太重 +``` + +### 2. 错误处理 + +```python +@table("safe_data") +def get_safe_data(): + try: + return {"value": compute_value()} + except Exception as e: + return {"error": str(e)} +``` + +### 3. 缓存 + +```python +from functools import lru_cache + +@table("cached_data") +@lru_cache(maxsize=1) +def get_cached_data(): + return expensive_computation() +``` diff --git a/docs/src/design/index.md b/docs/src/design/index.md new file mode 100644 index 00000000..645e6702 --- /dev/null +++ b/docs/src/design/index.md @@ -0,0 +1,48 @@ +# Design Overview + +## Why Probing? + +### The Pythonic Advantage + +Python's dominance in AI stems from one core principle: **everything feels like Python**. Whether you're using pandas, PyTorch, or NumPy, you can **talk to them pythonically**—the same `print()`, iteration, and attribute access patterns work everywhere. + +### How Distributed Systems Break This + +As AI models scale to distributed clusters, something fundamental breaks: **distributed systems aren't Pythonic**. Single-machine debugging feels natural—`print(model.parameters())`, `loss.item()`, `torch.cuda.memory_allocated()`—but distributed debugging forces you into system administration tools: `kubectl get nodes`, SSH sessions, log file parsing, monitoring dashboards. + +### Probing's Mission + +Probing's core mission is simple: **make distributed systems feel Pythonic again**. Your cluster, nodes, and distributed processes become accessible through familiar interfaces. Instead of context-switching between tools, you stay in Python and **talk to your distributed system pythonically**. + +## Design Principles + +### 🔍 Zero Intrusion + +- No code modifications required +- No environment setup changes needed +- No workflow disruptions +- Dynamic probe injection into running processes + +### 🎯 Zero Learning Curve + +- Standard SQL interface for data analysis +- Familiar database query patterns +- Intuitive command-line tools +- Web-based dashboard for visualization + +### 📦 Zero Deployment Burden + +- Single binary deployment (Rust-based) +- Static compilation with minimal dependencies +- Linux-first design with query/eval support on other platforms +- Elastic scaling capabilities + +## Design Documents + +| Document | Description | +|----------|-------------| +| [Architecture](architecture.md) | System structure and components | +| [Profiling](profiling.md) | Performance data collection | +| [Debugging](debugging.md) | Debugging capabilities | +| [Distributed](distributed.md) | Multi-node support | +| [Extensibility](extensibility.md) | Custom tables and metrics | diff --git a/docs/src/design/index.zh.md b/docs/src/design/index.zh.md new file mode 100644 index 00000000..10c4fcf0 --- /dev/null +++ b/docs/src/design/index.zh.md @@ -0,0 +1,48 @@ +# 设计概览 + +## 为什么选择 Probing？ + +### Pythonic 的优势 + +Python 在 AI 领域的主导地位源于一个核心原则：**一切都像 Python**。无论您使用 pandas、PyTorch 还是 NumPy，都可以用 **Pythonic 的方式与它们交互**——相同的 `print()`、迭代和属性访问模式随处可用。 + +### 分布式系统如何破坏了这一点 + +当 AI 模型扩展到分布式集群时，一些根本性的东西被打破了：**分布式系统不是 Pythonic 的**。单机调试感觉很自然——`print(model.parameters())`、`loss.item()`、`torch.cuda.memory_allocated()`——但分布式调试迫使您使用系统管理工具：`kubectl get nodes`、SSH 会话、日志文件解析、监控仪表板。 + +### Probing 的使命 + +Probing 的核心使命很简单：**让分布式系统重新变得 Pythonic**。您的集群、节点和分布式进程都可以通过熟悉的接口访问。不再需要在工具之间切换，您可以留在 Python 中，**用 Pythonic 的方式与您的分布式系统对话**。 + +## 设计原则 + +### 🔍 零侵入 + +- 无需修改代码 +- 无需更改环境设置 +- 无需中断工作流 +- 动态探针注入到运行中的进程 + +### 🎯 零学习曲线 + +- 标准 SQL 接口用于数据分析 +- 熟悉的数据库查询模式 +- 直观的命令行工具 +- 基于 Web 的可视化仪表板 + +### 📦 零部署负担 + +- 单一二进制部署（基于 Rust） +- 静态编译，最小依赖 +- Linux 优先设计，其他平台支持查询/执行 +- 弹性扩展能力 + +## 设计文档 + +| 文档 | 描述 | +|------|------| +| [系统架构](architecture.md) | 系统结构和组件 | +| [性能分析](profiling.md) | 性能数据收集 | +| [调试](debugging.md) | 调试能力 | +| [分布式](distributed.md) | 多节点支持 | +| [扩展机制](extensibility.md) | 自定义表和指标 | diff --git a/docs/src/design/profiling.md b/docs/src/design/profiling.md new file mode 100644 index 00000000..4576a87c --- /dev/null +++ b/docs/src/design/profiling.md @@ -0,0 +1,191 @@ +# Profiling Implementation + +Probing provides comprehensive profiling capabilities for AI workloads. + +## Overview + +The profiling system collects performance data with minimal overhead through: + +- Event-based collection +- Efficient sampling strategies +- Columnar data storage +- SQL query interface + +## Data Collection Architecture + +```mermaid +graph TB + subgraph "Data Sources" + TORCH[PyTorch Hooks] + PYTHON[Python Frames] + SYSTEM[System Metrics] + end + + subgraph "Collection Layer" + BUFFER[Ring Buffer] + SAMPLER[Sampler] + end + + subgraph "Storage Layer" + ARROW[Arrow Tables] + QUERY[Query Engine] + end + + TORCH --> SAMPLER + PYTHON --> SAMPLER + SYSTEM --> SAMPLER + + SAMPLER --> BUFFER + BUFFER --> ARROW + ARROW --> QUERY +``` + +## PyTorch Profiling + +### Hook Integration + +Probing integrates with PyTorch's module hooks: + +```python +# Forward hook +def forward_hook(module, input, output): + record_trace(module, "forward", memory_stats()) + +# Backward hook +def backward_hook(module, grad_input, grad_output): + record_trace(module, "backward", memory_stats()) +``` + +### Collected Data + +| Field | Type | Description | +|-------|------|-------------| +| step | int | Training step number | +| seq | int | Sequence within step | +| module | string | Module name | +| stage | string | forward/backward/step | +| allocated | float | GPU memory allocated (MB) | +| max_allocated | float | Peak GPU memory (MB) | +| cached | float | GPU memory cached (MB) | +| duration | float | Execution time (seconds) | + +### Enable PyTorch Profiling + +```bash +# Environment variable +PROBING_TORCH_PROFILING=on python train.py + +# Or programmatically +import probing +probing.enable_torch_profiling() +``` + +## Python Stack Profiling + +### Backtrace Collection + +Captures Python call stack with: + +- Function names +- File paths +- Line numbers +- Local variables (optional) + +```python +# Stack frame data +{ + "func": "forward", + "file": "/app/model.py", + "lineno": 123, + "depth": 5, + "frame_type": "Python" +} +``` + +### Sampling Strategy + +- **Periodic sampling**: Configurable interval (default: 100ms) +- **Event-triggered**: On specific operations +- **On-demand**: Via backtrace command + +## System Metrics + +### Collected Metrics + +- CPU utilization +- Memory usage (RSS, VMS) +- GPU utilization +- GPU memory +- I/O statistics +- Network statistics + +### Collection Interval + +```bash +# Configure sampling interval +probing $ENDPOINT config probing.sample_rate=0.1 # 100ms +``` + +## Data Storage + +### Ring Buffer + +Efficient fixed-size buffer for recent data: + +- Configurable size (default: 10000 records) +- Automatic eviction of old data +- Lock-free concurrent access + +### Arrow Tables + +Columnar format for efficient queries: + +- Vectorized operations +- Memory-mapped I/O +- Compression support + +## Query Interface + +### Available Tables + +```sql +-- List available tables +SELECT table_name FROM information_schema.tables; + +-- Query torch traces +SELECT * FROM python.torch_trace WHERE step > 100; + +-- Query backtraces +SELECT * FROM python.backtrace; +``` + +### Custom Aggregations + +```sql +-- Per-module statistics +SELECT + module, + COUNT(*) as count, + AVG(duration) as avg_duration, + MAX(allocated) as peak_memory +FROM python.torch_trace +GROUP BY module; +``` + +## Performance Overhead + +### Measurement Results + +| Scenario | Overhead | +|----------|----------| +| Idle (no profiling) | < 0.1% | +| Basic profiling | < 1% | +| Full PyTorch profiling | < 5% | +| With variable capture | < 10% | + +### Optimization Techniques + +1. **Lazy evaluation** - Only compute metrics when queried +2. **Batched writes** - Buffer multiple records before storage +3. **Sampling** - Configurable sample rates +4. **Selective hooks** - Enable only needed data sources diff --git a/docs/src/design/profiling.zh.md b/docs/src/design/profiling.zh.md new file mode 100644 index 00000000..b08efb55 --- /dev/null +++ b/docs/src/design/profiling.zh.md @@ -0,0 +1,163 @@ +# 性能分析实现 + +Probing 为 AI 工作负载提供全面的性能分析能力。 + +## 概览 + +性能分析系统通过以下方式以最小开销收集性能数据： + +- 基于事件的收集 +- 高效的采样策略 +- 列式数据存储 +- SQL 查询接口 + +## 数据收集架构 + +```mermaid +graph TB + subgraph "数据源" + TORCH[PyTorch 钩子] + PYTHON[Python 帧] + SYSTEM[系统指标] + end + + subgraph "收集层" + BUFFER[环形缓冲区] + SAMPLER[采样器] + end + + subgraph "存储层" + ARROW[Arrow 表] + QUERY[查询引擎] + end + + TORCH --> SAMPLER + PYTHON --> SAMPLER + SYSTEM --> SAMPLER + + SAMPLER --> BUFFER + BUFFER --> ARROW + ARROW --> QUERY +``` + +## PyTorch 分析 + +### 钩子集成 + +Probing 与 PyTorch 的模块钩子集成： + +```python +# 前向钩子 +def forward_hook(module, input, output): + record_trace(module, "forward", memory_stats()) + +# 反向钩子 +def backward_hook(module, grad_input, grad_output): + record_trace(module, "backward", memory_stats()) +``` + +### 收集的数据 + +| 字段 | 类型 | 描述 | +|------|------|------| +| step | int | 训练步数 | +| seq | int | 步内序号 | +| module | string | 模块名 | +| stage | string | forward/backward/step | +| allocated | float | GPU 已分配内存 (MB) | +| max_allocated | float | GPU 峰值内存 (MB) | +| cached | float | GPU 缓存内存 (MB) | +| duration | float | 执行时间（秒）| + +### 启用 PyTorch 分析 + +```bash +# 环境变量 +PROBING_TORCH_PROFILING=on python train.py + +# 或编程方式 +import probing +probing.enable_torch_profiling() +``` + +## Python 堆栈分析 + +### 堆栈跟踪收集 + +捕获 Python 调用栈包括： + +- 函数名 +- 文件路径 +- 行号 +- 局部变量（可选） + +```python +# 堆栈帧数据 +{ + "func": "forward", + "file": "/app/model.py", + "lineno": 123, + "depth": 5, + "frame_type": "Python" +} +``` + +### 采样策略 + +- **周期采样**: 可配置间隔（默认：100ms） +- **事件触发**: 特定操作时 +- **按需**: 通过 backtrace 命令 + +## 系统指标 + +### 收集的指标 + +- CPU 利用率 +- 内存使用（RSS、VMS） +- GPU 利用率 +- GPU 内存 +- I/O 统计 +- 网络统计 + +### 收集间隔 + +```bash +# 配置采样间隔 +probing $ENDPOINT config probing.sample_rate=0.1 # 100ms +``` + +## 数据存储 + +### 环形缓冲区 + +高效的固定大小缓冲区用于存储最新数据： + +- 可配置大小（默认：10000 条记录） +- 自动淘汰旧数据 +- 无锁并发访问 + +### Arrow 表 + +用于高效查询的列式格式： + +- 向量化操作 +- 内存映射 I/O +- 压缩支持 + +## 性能开销 + +### 测量结果 + +| 场景 | 开销 | +|------|------| +| 空闲（无分析）| < 0.1% | +| 基础分析 | < 1% | +| 完整 PyTorch 分析 | < 5% | +| 带变量捕获 | < 10% | + +### 优化技术 + +1. **延迟计算** - 仅在查询时计算指标 +2. **批量写入** - 缓冲多条记录后再存储 +3. **采样** - 可配置采样率 +4. **选择性钩子** - 仅启用需要的数据源 diff --git a/docs/src/development/building.md b/docs/src/development/building.md deleted file mode 100644 index f5a314a3..00000000 --- a/docs/src/development/building.md +++ /dev/null @@ -1,453 +0,0 @@ -# Building from Source - -This guide covers building Probing from source code, including all dependencies and components. - -## Prerequisites - -### Required Tools - -**Rust Toolchain:** -```bash -# Install Rust via rustup -curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -source ~/.cargo/env - -# Verify installation -rustc --version -cargo --version -``` - -**Python Development:** -```bash -# Python 3.7+ required -python3 --version - -# Development dependencies -pip install setuptools wheel build -``` - -**System Dependencies:** -```bash -# Ubuntu/Debian -sudo apt update -sudo apt install build-essential pkg-config libssl-dev - -# CentOS/RHEL/Fedora -sudo yum groupinstall "Development Tools" -sudo yum install openssl-devel - -# macOS (with Homebrew) -brew install pkg-config openssl -``` - -### Optional Tools - -**For cross-platform builds:** -```bash -# Install Zig for better compatibility -cargo install cargo-zigbuild -``` - -**For web UI development:** -```bash -# Install Dioxus CLI for WebAssembly builds -cargo install dioxus-cli -``` - -## Quick Build - -### Standard Build - -Clone and build the project: -```bash -# Clone repository -git clone https://github.com/reiase/probing.git -cd probing - -# Build all components -make - -# Verify build -./target/release/probing --version -``` - -### Development Build - -For faster development cycles: -```bash -# Development build (debug mode) -cargo build - -# Build specific components -cargo build -p probing-cli # CLI only -cargo build -p probing-core # Core library -cargo build -p probing-server # HTTP server -``` - -## Detailed Build Process - -### Core Components - -**1. Probing Core Library:** -```bash -cd probing/core -cargo build --release -``` - -**2. Command Line Interface:** -```bash -cd probing/cli -cargo build --release -``` - -**3. Python Extension:** -```bash -cd probing/extensions/python -cargo build --release -``` - -**4. Server Component:** -```bash -cd probing/server -cargo build --release -``` - -### Python Wheel Package - -**Build Python wheel:** -```bash -# Standard build -python make_wheel.py - -# Cross-platform build with Zig -make ZIG=1 - -# Install locally -pip install dist/probing-*.whl --force-reinstall -``` - -### Web UI (Optional) - -**Build web interface:** -```bash -cd web -dx build --release -``` - -The web UI is built as WebAssembly using Dioxus and provides a graphical interface for Probing. The build output is in `web/dist/` for packaging. - -## Build Configurations - -### Release Build - -For production deployment: -```bash -# Optimized release build -cargo build --release - -# Full production build with all optimizations -make ZIG=1 -``` - -### Debug Build - -For development and debugging: -```bash -# Debug build with symbols -cargo build - -# With additional debug info -RUSTFLAGS="-C debug-assertions=on" cargo build -``` - -### Feature Flags - -Enable specific features during build: -```bash -# Build with all features -cargo build --all-features - -# Build with specific features -cargo build --features "python-ext,server" - -# Build minimal version -cargo build --no-default-features -``` - -## Cross-Platform Building - -### Linux Distributions - -**For older glibc compatibility:** -```bash -# Use Zig for better compatibility -make ZIG=1 - -# Manual Zig build -cargo zigbuild --release --target x86_64-unknown-linux-gnu -``` - -**Static linking:** -```bash -# Static binary (self-contained) -RUSTFLAGS="-C target-feature=+crt-static" cargo build --release --target x86_64-unknown-linux-musl -``` - -### macOS - -```bash -# Standard macOS build -cargo build --release - -# Universal binary (Intel + Apple Silicon) -cargo build --release --target x86_64-apple-darwin -cargo build --release --target aarch64-apple-darwin -``` - -### Windows - -```bash -# Windows build (from Linux using cross-compilation) -cargo build --release --target x86_64-pc-windows-gnu -``` - -## Testing the Build - -### Basic Functionality - -```bash -# Run test suite -make test - -# Verify CLI installation -./target/release/probing --version - -# Test basic functionality -PROBE=1 python examples/test_probing.py -``` - -### Advanced Testing - -```bash -# Test PyTorch integration -PROBE_TORCH_EXPRS="loss@train,acc1@train" PROBE=1 python examples/imagenet.py - -# Test SQL interface -./target/release/probing $ENDPOINT query "SELECT * FROM information_schema.df_settings" - -# Test web UI (if built) -cd web && dx serve -``` - -### Performance Testing - -```bash -# Benchmark core components -cargo bench - -# Memory usage testing -valgrind ./target/release/probing $ENDPOINT inject - -# Load testing -./scripts/load_test.sh -``` - -## Installation - -### System-wide Installation - -```bash -# Install to system location -sudo cp target/release/probing /usr/local/bin/ -sudo cp target/release/libprobing.so /usr/local/lib/ - -# Update library cache -sudo ldconfig -``` - -### User Installation - -```bash -# Install to user directory -mkdir -p ~/.local/bin ~/.local/lib -cp target/release/probing ~/.local/bin/ -cp target/release/libprobing.so ~/.local/lib/ - -# Add to PATH if needed -echo 'export PATH="$HOME/.local/bin:$PATH"' >> ~/.bashrc -``` - -### Python Package Installation - -```bash -# Install Python wheel -pip install dist/probing-*.whl --force-reinstall - -# Verify Python integration -python -c "import probing; print('Success')" -``` - -## Development Setup - -### IDE Configuration - -**VS Code setup:** -```json -// .vscode/settings.json -{ - "rust-analyzer.cargo.features": ["all"], - "rust-analyzer.check.command": "clippy" -} -``` - -**Environment variables:** -```bash -# Development environment -export RUST_LOG=debug -export PROBING_LOGLEVEL=debug -export RUST_BACKTRACE=1 -``` - -### Pre-commit Hooks - -```bash -# Install pre-commit hooks -pip install pre-commit -pre-commit install - -# Manual format and lint -cargo fmt -cargo clippy -- -D warnings -``` - -## Troubleshooting - -### Common Build Issues - -**Linker errors:** -```bash -# Install required system libraries -sudo apt install build-essential pkg-config libssl-dev - -# Check linker configuration -rustc --print cfg -``` - -**Python extension issues:** -```bash -# Ensure Python headers are available -sudo apt install python3-dev - -# Check Python version compatibility -python3 --version -``` - -**Cross-compilation issues:** -```bash -# Install cross-compilation target -rustup target add x86_64-unknown-linux-musl - -# Use Zig for better compatibility -cargo install cargo-zigbuild -cargo zigbuild --release -``` - -### Performance Issues - -**Slow builds:** -```bash -# Use more CPU cores -export CARGO_BUILD_JOBS=8 - -# Enable parallel frontend -export RUSTFLAGS="-C codegen-units=16" - -# Use faster linker (Linux) -sudo apt install lld -export RUSTFLAGS="-C link-arg=-fuse-ld=lld" -``` - -**Memory usage during build:** -```bash -# Reduce memory usage -export RUSTFLAGS="-C opt-level=1" - -# Build with reduced parallelism -cargo build --jobs 2 -``` - -## Project Structure - -Understanding the codebase organization: - -``` -probing/ -├── probing/cli/ # Command-line interface -├── probing/core/ # Core profiling engine -├── probing/extensions/ # Language-specific extensions -│ ├── python/ # Python integration -│ └── cc/ # C++ integration -├── probing/server/ # HTTP API server -├── web/ # Web UI source and build output (Dioxus + WebAssembly) -│ └── dist/ # Web UI build output -├── python/ # Python hooks and bindings -├── examples/ # Usage examples and demos -└── docs/ # Documentation -``` - -### Key Files - -- `Cargo.toml` - Main workspace configuration -- `Makefile` - Build automation -- `make_wheel.py` - Python package builder -- `probing/cli/src/main.rs` - CLI entry point -- `probing/core/src/lib.rs` - Core library -- `python/probing/__init__.py` - Python package entry - -## Contributing to the Build System - -### Adding New Features - -1. **Add Cargo features:** -```toml -# In Cargo.toml -[features] -new-feature = ["dep:some-crate"] -``` - -2. **Update build scripts:** -```bash -# In Makefile -build-new-feature: - cargo build --features new-feature -``` - -3. **Test integration:** -```bash -# Add tests -cargo test --features new-feature -``` - -### Build Optimizations - -**Profile-guided optimization:** -```bash -# Generate profile data -RUSTFLAGS="-C profile-generate=/tmp/pgo-data" cargo build --release - -# Run representative workload -./target/release/probing - -# Build with profile data -RUSTFLAGS="-C profile-use=/tmp/pgo-data" cargo build --release -``` - -**Link-time optimization:** -```bash -# Enable LTO in Cargo.toml -[profile.release] -lto = true -codegen-units = 1 -``` - -For development workflow and contribution guidelines, see the main [README.md](../README.md) and [Contributing Guide](contributing.md). diff --git a/docs/src/examples/index.md b/docs/src/examples/index.md new file mode 100644 index 00000000..ba34d97c --- /dev/null +++ b/docs/src/examples/index.md @@ -0,0 +1,81 @@ +# Examples + +Real-world examples demonstrating Probing's capabilities. + +## Overview + +These examples show common debugging and profiling scenarios in AI/ML workflows. + +| Example | Description | +|---------|-------------| +| [Training Debugging](training-debugging.md) | Debug training issues | +| [Memory Leak](memory-leak.md) | Find and fix memory leaks | +| [Performance Analysis](performance-analysis.md) | Identify bottlenecks | + +## Quick Examples + +### Check Training Progress + +```bash +probing $ENDPOINT eval " +print(f'Step: {trainer.current_step}') +print(f'Loss: {trainer.last_loss:.4f}') +print(f'LR: {optimizer.param_groups[0][\"lr\"]:.6f}')" +``` + +### Monitor GPU Memory + +```bash +probing $ENDPOINT eval " +import torch +allocated = torch.cuda.memory_allocated() / 1024**3 +reserved = torch.cuda.memory_reserved() / 1024**3 +print(f'Allocated: {allocated:.2f} GB') +print(f'Reserved: {reserved:.2f} GB')" +``` + +### Find Slow Operations + +```bash +probing $ENDPOINT query " +SELECT module, AVG(duration) as avg_time +FROM python.torch_trace +WHERE step > (SELECT MAX(step) - 5 FROM python.torch_trace) +GROUP BY module +ORDER BY avg_time DESC +LIMIT 5" +``` + +### Check Thread States + +```bash +probing $ENDPOINT eval " +import threading +for t in threading.enumerate(): + print(f'{t.name}: alive={t.is_alive()}, daemon={t.daemon}')" +``` + +## Running Examples + +All examples assume you have: + +1. A running Python process with Probing enabled +2. The `$ENDPOINT` environment variable set + +```bash +# Set endpoint +export ENDPOINT=12345 # Process ID +# or +export ENDPOINT=host:8080 # Remote address + +# Run example commands +probing $ENDPOINT eval "..." +``` + +## Contributing Examples + +Have a useful debugging pattern? Contributions welcome! + +1. Fork the repository +2. Add your example to `docs/src/examples/` +3. Submit a pull request diff --git a/docs/src/examples/index.zh.md b/docs/src/examples/index.zh.md new file mode 100644 index 00000000..e05d55f6 --- /dev/null +++ b/docs/src/examples/index.zh.md @@ -0,0 +1,81 @@ +# 示例 + +展示 Probing 能力的真实示例。 + +## 概览 + +这些示例展示了 AI/ML 工作流中常见的调试和分析场景。 + +| 示例 | 描述 | +|------|------| +| [训练调试](training-debugging.md) | 调试训练问题 | +| [内存泄漏](memory-leak.md) | 查找和修复内存泄漏 | +| [性能分析](performance-analysis.md) | 识别瓶颈 | + +## 快速示例 + +### 检查训练进度 + +```bash +probing $ENDPOINT eval " +print(f'步骤: {trainer.current_step}') +print(f'损失: {trainer.last_loss:.4f}') +print(f'学习率: {optimizer.param_groups[0][\"lr\"]:.6f}')" +``` + +### 监控 GPU 内存 + +```bash +probing $ENDPOINT eval " +import torch +allocated = torch.cuda.memory_allocated() / 1024**3 +reserved = torch.cuda.memory_reserved() / 1024**3 +print(f'已分配: {allocated:.2f} GB') +print(f'已保留: {reserved:.2f} GB')" +``` + +### 查找慢操作 + +```bash +probing $ENDPOINT query " +SELECT module, AVG(duration) as avg_time +FROM python.torch_trace +WHERE step > (SELECT MAX(step) - 5 FROM python.torch_trace) +GROUP BY module +ORDER BY avg_time DESC +LIMIT 5" +``` + +### 检查线程状态 + +```bash +probing $ENDPOINT eval " +import threading +for t in threading.enumerate(): + print(f'{t.name}: alive={t.is_alive()}, daemon={t.daemon}')" +``` + +## 运行示例 + +所有示例假设您已经： + +1. 一个启用了 Probing 的运行中 Python 进程 +2. 设置了 `$ENDPOINT` 环境变量 + +```bash +# 设置端点 +export ENDPOINT=12345 # 进程 ID +# 或 +export ENDPOINT=host:8080 # 远程地址 + +# 运行示例命令 +probing $ENDPOINT eval "..." +``` + +## 贡献示例 + +有实用的调试模式？欢迎贡献！ + +1. Fork 仓库 +2. 将您的示例添加到 `docs/src/examples/` +3. 提交 Pull Request diff --git a/docs/src/examples/memory-leak.md b/docs/src/examples/memory-leak.md new file mode 100644 index 00000000..48b9863c --- /dev/null +++ b/docs/src/examples/memory-leak.md @@ -0,0 +1,268 @@ +# Memory Leak Examples + +Detecting and fixing memory leaks in AI applications. + +## Identifying Memory Leaks + +### Memory Growth Pattern + +```bash +# Track memory over steps +probing $ENDPOINT query " +SELECT + step, + MAX(allocated) as peak_memory_mb +FROM python.torch_trace +GROUP BY step +ORDER BY step" +``` + +### Monotonic Growth Detection + +```sql +WITH memory_trend AS ( + SELECT + step, + MAX(allocated) as peak, + LAG(MAX(allocated)) OVER (ORDER BY step) as prev_peak + FROM python.torch_trace + GROUP BY step +) +SELECT + step, + peak, + peak - prev_peak as growth +FROM memory_trend +WHERE peak > prev_peak +ORDER BY step; +``` + +## Common Leak Patterns + +### Pattern 1: Accumulating Tensors in Lists + +**Problem:** + +```python +# BAD: Tensors accumulate in list +all_losses = [] +for batch in dataloader: + loss = model(batch) + all_losses.append(loss) # Holds computation graph! +``` + +**Detection:** + +```bash +probing $ENDPOINT eval " +import gc +import torch +tensors = [obj for obj in gc.get_objects() if isinstance(obj, torch.Tensor)] +print(f'Total tensors: {len(tensors)}')" +``` + +**Fix:** + +```bash +probing $ENDPOINT eval " +# Use .item() to extract scalar +all_losses.clear() +print('Cleared loss list')" +``` + +### Pattern 2: Forgotten Gradient Graphs + +**Problem:** + +```python +# BAD: Intermediate tensors hold grad_fn +intermediate = model.encoder(x) +# ... lots of operations ... +# intermediate still holds computation graph +``` + +**Detection:** + +```bash +probing $ENDPOINT eval " +import torch +for name, param in model.named_parameters(): + if param.grad is not None: + print(f'{name}: grad_fn={param.grad.grad_fn}')" +``` + +**Fix:** + +```bash +probing $ENDPOINT eval " +model.zero_grad(set_to_none=True) +import torch +torch.cuda.empty_cache() +print('Cleared gradients')" +``` + +### Pattern 3: Circular References + +**Detection:** + +```bash +probing $ENDPOINT eval " +import gc +gc.set_debug(gc.DEBUG_SAVEALL) +gc.collect() +print(f'Uncollectable: {len(gc.garbage)}')" +``` + +## GPU Memory Leaks + +### Check CUDA Memory State + +```bash +probing $ENDPOINT eval " +import torch +print(f'Allocated: {torch.cuda.memory_allocated() / 1024**3:.2f} GB') +print(f'Reserved: {torch.cuda.memory_reserved() / 1024**3:.2f} GB') +print(f'Max allocated: {torch.cuda.max_memory_allocated() / 1024**3:.2f} GB')" +``` + +### Memory Snapshot + +```bash +probing $ENDPOINT eval " +import torch +if torch.cuda.is_available(): + snapshot = torch.cuda.memory_snapshot() + print(f'Number of blocks: {len(snapshot)}')" +``` + +### Force CUDA Cleanup + +```bash +probing $ENDPOINT eval " +import torch +import gc + +# Clear all references +gc.collect() + +# Empty CUDA cache +torch.cuda.empty_cache() + +# Reset peak stats +torch.cuda.reset_peak_memory_stats() + +print('CUDA memory cleaned')" +``` + +## CPU Memory Leaks + +### Track Process Memory + +```bash +probing $ENDPOINT eval " +import psutil +import os + +proc = psutil.Process(os.getpid()) +mem = proc.memory_info() +print(f'RSS: {mem.rss / 1024**3:.2f} GB') +print(f'VMS: {mem.vms / 1024**3:.2f} GB')" +``` + +### Find Large Objects + +```bash +probing $ENDPOINT eval " +import sys +import gc + +# Find largest objects +objects = gc.get_objects() +sizes = [(sys.getsizeof(obj), type(obj).__name__) for obj in objects[:1000]] +sizes.sort(reverse=True) +for size, name in sizes[:10]: + print(f'{name}: {size / 1024:.1f} KB')" +``` + +## Data Loader Leaks + +### Check Worker State + +```bash +probing $ENDPOINT eval " +print(f'Num workers: {train_loader.num_workers}') +print(f'Batch size: {train_loader.batch_size}')" +``` + +### Inspect Prefetched Data + +```bash +probing $ENDPOINT eval " +# Check if data is being held +if hasattr(train_loader, '_iterator'): + print('Iterator exists') +else: + print('No active iterator')" +``` + +## Monitoring Over Time + +### Periodic Memory Check + +```bash +# Run every minute +while true; do + probing $ENDPOINT eval " +import torch +import psutil +import os +proc = psutil.Process(os.getpid()) +print(f'CPU: {proc.memory_info().rss / 1024**3:.2f} GB, GPU: {torch.cuda.memory_allocated() / 1024**3:.2f} GB')" + sleep 60 +done +``` + +### SQL-Based Monitoring + +```sql +-- Memory trend over last 100 steps +SELECT + step, + AVG(allocated) as avg_memory, + MAX(allocated) as peak_memory +FROM python.torch_trace +WHERE step > (SELECT MAX(step) - 100 FROM python.torch_trace) +GROUP BY step +ORDER BY step; +``` + +## Prevention Best Practices + +### 1. Use Context Managers + +```python +# Good practice +with torch.no_grad(): + output = model(input) +``` + +### 2. Detach Tensors + +```python +# When storing for later +stored_output = output.detach().cpu() +``` + +### 3. Clear Caches Periodically + +```python +if step % 100 == 0: + gc.collect() + torch.cuda.empty_cache() +``` + +### 4. Use `set_to_none=True` + +```python +optimizer.zero_grad(set_to_none=True) # More efficient +``` diff --git a/docs/src/examples/memory-leak.zh.md b/docs/src/examples/memory-leak.zh.md new file mode 100644 index 00000000..5c5770d6 --- /dev/null +++ b/docs/src/examples/memory-leak.zh.md @@ -0,0 +1,268 @@ +# 内存泄漏示例 + +检测和修复 AI 应用中的内存泄漏。 + +## 识别内存泄漏 + +### 内存增长模式 + +```bash +# 追踪各步骤的内存 +probing $ENDPOINT query " +SELECT + step, + MAX(allocated) as peak_memory_mb +FROM python.torch_trace +GROUP BY step +ORDER BY step" +``` + +### 单调增长检测 + +```sql +WITH memory_trend AS ( + SELECT + step, + MAX(allocated) as peak, + LAG(MAX(allocated)) OVER (ORDER BY step) as prev_peak + FROM python.torch_trace + GROUP BY step +) +SELECT + step, + peak, + peak - prev_peak as growth +FROM memory_trend +WHERE peak > prev_peak +ORDER BY step; +``` + +## 常见泄漏模式 + +### 模式 1：列表中累积张量 + +**问题：** + +```python +# 错误：张量在列表中累积 +all_losses = [] +for batch in dataloader: + loss = model(batch) + all_losses.append(loss) # 保持计算图！ +``` + +**检测：** + +```bash +probing $ENDPOINT eval " +import gc +import torch +tensors = [obj for obj in gc.get_objects() if isinstance(obj, torch.Tensor)] +print(f'张量总数: {len(tensors)}')" +``` + +**修复：** + +```bash +probing $ENDPOINT eval " +# 使用 .item() 提取标量 +all_losses.clear() +print('已清空损失列表')" +``` + +### 模式 2：遗忘的梯度图 + +**问题：** + +```python +# 错误：中间张量保持 grad_fn +intermediate = model.encoder(x) +# ... 很多操作 ... +# intermediate 仍然保持计算图 +``` + +**检测：** + +```bash +probing $ENDPOINT eval " +import torch +for name, param in model.named_parameters(): + if param.grad is not None: + print(f'{name}: grad_fn={param.grad.grad_fn}')" +``` + +**修复：** + +```bash +probing $ENDPOINT eval " +model.zero_grad(set_to_none=True) +import torch +torch.cuda.empty_cache() +print('已清除梯度')" +``` + +### 模式 3：循环引用 + +**检测：** + +```bash +probing $ENDPOINT eval " +import gc +gc.set_debug(gc.DEBUG_SAVEALL) +gc.collect() +print(f'无法回收: {len(gc.garbage)}')" +``` + +## GPU 内存泄漏 + +### 检查 CUDA 内存状态 + +```bash +probing $ENDPOINT eval " +import torch +print(f'已分配: {torch.cuda.memory_allocated() / 1024**3:.2f} GB') +print(f'已保留: {torch.cuda.memory_reserved() / 1024**3:.2f} GB') +print(f'峰值: {torch.cuda.max_memory_allocated() / 1024**3:.2f} GB')" +``` + +### 内存快照 + +```bash +probing $ENDPOINT eval " +import torch +if torch.cuda.is_available(): + snapshot = torch.cuda.memory_snapshot() + print(f'块数量: {len(snapshot)}')" +``` + +### 强制 CUDA 清理 + +```bash +probing $ENDPOINT eval " +import torch +import gc + +# 清除所有引用 +gc.collect() + +# 清空 CUDA 缓存 +torch.cuda.empty_cache() + +# 重置峰值统计 +torch.cuda.reset_peak_memory_stats() + +print('CUDA 内存已清理')" +``` + +## CPU 内存泄漏 + +### 追踪进程内存 + +```bash +probing $ENDPOINT eval " +import psutil +import os + +proc = psutil.Process(os.getpid()) +mem = proc.memory_info() +print(f'RSS: {mem.rss / 1024**3:.2f} GB') +print(f'VMS: {mem.vms / 1024**3:.2f} GB')" +``` + +### 查找大对象 + +```bash +probing $ENDPOINT eval " +import sys +import gc + +# 查找最大对象 +objects = gc.get_objects() +sizes = [(sys.getsizeof(obj), type(obj).__name__) for obj in objects[:1000]] +sizes.sort(reverse=True) +for size, name in sizes[:10]: + print(f'{name}: {size / 1024:.1f} KB')" +``` + +## 数据加载器泄漏 + +### 检查 Worker 状态 + +```bash +probing $ENDPOINT eval " +print(f'Num workers: {train_loader.num_workers}') +print(f'Batch size: {train_loader.batch_size}')" +``` + +### 检查预取数据 + +```bash +probing $ENDPOINT eval " +# 检查数据是否被保持 +if hasattr(train_loader, '_iterator'): + print('迭代器存在') +else: + print('没有活动的迭代器')" +``` + +## 持续监控 + +### 定期内存检查 + +```bash +# 每分钟运行 +while true; do + probing $ENDPOINT eval " +import torch +import psutil +import os +proc = psutil.Process(os.getpid()) +print(f'CPU: {proc.memory_info().rss / 1024**3:.2f} GB, GPU: {torch.cuda.memory_allocated() / 1024**3:.2f} GB')" + sleep 60 +done +``` + +### 基于 SQL 的监控 + +```sql +-- 最近 100 步的内存趋势 +SELECT + step, + AVG(allocated) as avg_memory, + MAX(allocated) as peak_memory +FROM python.torch_trace +WHERE step > (SELECT MAX(step) - 100 FROM python.torch_trace) +GROUP BY step +ORDER BY step; +``` + +## 预防最佳实践 + +### 1. 使用上下文管理器 + +```python +# 好的做法 +with torch.no_grad(): + output = model(input) +``` + +### 2. 分离张量 + +```python +# 存储时 +stored_output = output.detach().cpu() +``` + +### 3. 定期清理缓存 + +```python +if step % 100 == 0: + gc.collect() + torch.cuda.empty_cache() +``` + +### 4. 使用 `set_to_none=True` + +```python +optimizer.zero_grad(set_to_none=True) # 更高效 +``` diff --git a/docs/src/examples/performance-analysis.md b/docs/src/examples/performance-analysis.md new file mode 100644 index 00000000..8704df7f --- /dev/null +++ b/docs/src/examples/performance-analysis.md @@ -0,0 +1,262 @@ +# Performance Analysis Examples + +Identify and fix performance bottlenecks in AI workloads. + +## Finding Bottlenecks + +### Overall Performance Profile + +```bash +probing $ENDPOINT query " +SELECT + module, + stage, + COUNT(*) as executions, + AVG(duration) as avg_time_sec, + SUM(duration) as total_time_sec, + SUM(duration) * 100.0 / SUM(SUM(duration)) OVER () as pct_time +FROM python.torch_trace +WHERE step > (SELECT MAX(step) - 10 FROM python.torch_trace) +GROUP BY module, stage +ORDER BY total_time_sec DESC +LIMIT 15" +``` + +### Per-Step Breakdown + +```bash +probing $ENDPOINT query " +SELECT + step, + SUM(CASE WHEN stage = 'forward' THEN duration ELSE 0 END) as forward_time, + SUM(CASE WHEN stage = 'backward' THEN duration ELSE 0 END) as backward_time, + SUM(CASE WHEN stage = 'step' THEN duration ELSE 0 END) as optimizer_time +FROM python.torch_trace +WHERE step > (SELECT MAX(step) - 5 FROM python.torch_trace) +GROUP BY step +ORDER BY step" +``` + +## GPU Utilization + +### Check Current Utilization + +```bash +probing $ENDPOINT eval " +import subprocess +result = subprocess.run( + ['nvidia-smi', '--query-gpu=utilization.gpu,utilization.memory,temperature.gpu', + '--format=csv,noheader,nounits'], + capture_output=True, text=True +) +for i, line in enumerate(result.stdout.strip().split('\\n')): + gpu_util, mem_util, temp = line.split(', ') + print(f'GPU {i}: Util={gpu_util}%, Mem={mem_util}%, Temp={temp}°C')" +``` + +### CUDA Synchronization Overhead + +```bash +probing $ENDPOINT eval " +import torch +import time + +# Measure sync overhead +start = time.perf_counter() +torch.cuda.synchronize() +sync_time = time.perf_counter() - start +print(f'CUDA sync time: {sync_time*1000:.2f} ms')" +``` + +## Memory Bandwidth + +### Memory-Bound Operations + +```bash +probing $ENDPOINT query " +SELECT + module, + AVG(allocated) as avg_memory_mb, + AVG(duration) as avg_time_sec, + AVG(allocated) / AVG(duration) as memory_bandwidth_mb_per_sec +FROM python.torch_trace +WHERE duration > 0.001 +GROUP BY module +ORDER BY memory_bandwidth_mb_per_sec DESC +LIMIT 10" +``` + +## Data Loading Performance + +### Data Loader Timing + +```bash +probing $ENDPOINT eval " +import time + +# Time one batch load +start = time.perf_counter() +batch = next(iter(train_loader)) +load_time = time.perf_counter() - start +print(f'Batch load time: {load_time*1000:.2f} ms')" +``` + +### Worker Analysis + +```bash +probing $ENDPOINT eval " +print(f'Num workers: {train_loader.num_workers}') +print(f'Pin memory: {train_loader.pin_memory}') +print(f'Prefetch factor: {getattr(train_loader, \"prefetch_factor\", 2)}')" +``` + +## Communication Overhead (Distributed) + +### NCCL Operation Times + +```bash +probing $ENDPOINT query " +SELECT + operation_type, + COUNT(*) as count, + AVG(duration_ms) as avg_time_ms, + MAX(duration_ms) as max_time_ms +FROM python.nccl_trace +GROUP BY operation_type +ORDER BY avg_time_ms DESC" +``` + +### All-Reduce Scaling + +```bash +probing $ENDPOINT eval " +import torch.distributed as dist +import time + +if dist.is_initialized(): + tensor = torch.randn(1000000, device='cuda') + + start = time.perf_counter() + dist.all_reduce(tensor) + torch.cuda.synchronize() + allreduce_time = time.perf_counter() - start + + print(f'All-reduce time for 4MB: {allreduce_time*1000:.2f} ms')" +``` + +## Attention Bottlenecks + +### Self-Attention Analysis + +```bash +probing $ENDPOINT query " +SELECT + module, + AVG(duration) as avg_time, + AVG(allocated) as avg_memory +FROM python.torch_trace +WHERE module LIKE '%attention%' OR module LIKE '%attn%' +GROUP BY module +ORDER BY avg_time DESC" +``` + +### Memory per Sequence Length + +```bash +probing $ENDPOINT eval " +import torch + +# Check attention memory scaling +seq_len = model.config.max_position_embeddings +hidden = model.config.hidden_size +num_heads = model.config.num_attention_heads + +# Attention score memory: O(seq_len^2) +attention_memory = seq_len * seq_len * num_heads * 4 / 1024**3 # GB +print(f'Estimated attention memory: {attention_memory:.2f} GB')" +``` + +## Optimization Recommendations + +### Profile-Guided Optimization + +```bash +# 1. Identify slowest modules +probing $ENDPOINT query " +SELECT module, AVG(duration) as avg_time +FROM python.torch_trace +GROUP BY module +ORDER BY avg_time DESC +LIMIT 5" + +# 2. Check if compute-bound or memory-bound +probing $ENDPOINT eval " +import torch +# High compute utilization + low memory bandwidth = compute-bound +# Low compute utilization + high memory utilization = memory-bound" +``` + +### Common Optimizations + +#### Enable Torch Compile + +```bash +probing $ENDPOINT eval " +import torch +if hasattr(torch, 'compile'): + model = torch.compile(model) + print('Model compiled with torch.compile')" +``` + +#### Enable Mixed Precision + +```bash +probing $ENDPOINT eval " +from torch.cuda.amp import autocast +print(f'AMP enabled: {torch.cuda.amp.autocast_mode.is_autocast_enabled()}')" +``` + +#### Check Gradient Checkpointing + +```bash +probing $ENDPOINT eval " +# Check if gradient checkpointing is enabled +for name, module in model.named_modules(): + if hasattr(module, 'gradient_checkpointing'): + print(f'{name}: checkpoint={module.gradient_checkpointing}')" +``` + +## Benchmarking + +### Throughput Measurement + +```bash +probing $ENDPOINT eval " +import time + +# Measure throughput over 10 steps +steps = 10 +start = time.perf_counter() +for _ in range(steps): + trainer.train_step() +elapsed = time.perf_counter() - start + +samples_per_sec = (steps * batch_size) / elapsed +print(f'Throughput: {samples_per_sec:.1f} samples/sec')" +``` + +### Compare Before/After + +```bash +# Before optimization +probing $ENDPOINT query " +SELECT AVG(duration) as before_avg +FROM python.torch_trace +WHERE step BETWEEN 100 AND 110" + +# After optimization +probing $ENDPOINT query " +SELECT AVG(duration) as after_avg +FROM python.torch_trace +WHERE step BETWEEN 200 AND 210" +``` diff --git a/docs/src/examples/performance-analysis.zh.md b/docs/src/examples/performance-analysis.zh.md new file mode 100644 index 00000000..a15fedac --- /dev/null +++ b/docs/src/examples/performance-analysis.zh.md @@ -0,0 +1,262 @@ +# 性能分析示例 + +识别和修复 AI 工作负载中的性能瓶颈。 + +## 查找瓶颈 + +### 整体性能概况 + +```bash +probing $ENDPOINT query " +SELECT + module, + stage, + COUNT(*) as executions, + AVG(duration) as avg_time_sec, + SUM(duration) as total_time_sec, + SUM(duration) * 100.0 / SUM(SUM(duration)) OVER () as pct_time +FROM python.torch_trace +WHERE step > (SELECT MAX(step) - 10 FROM python.torch_trace) +GROUP BY module, stage +ORDER BY total_time_sec DESC +LIMIT 15" +``` + +### 每步分解 + +```bash +probing $ENDPOINT query " +SELECT + step, + SUM(CASE WHEN stage = 'forward' THEN duration ELSE 0 END) as forward_time, + SUM(CASE WHEN stage = 'backward' THEN duration ELSE 0 END) as backward_time, + SUM(CASE WHEN stage = 'step' THEN duration ELSE 0 END) as optimizer_time +FROM python.torch_trace +WHERE step > (SELECT MAX(step) - 5 FROM python.torch_trace) +GROUP BY step +ORDER BY step" +``` + +## GPU 利用率 + +### 检查当前利用率 + +```bash +probing $ENDPOINT eval " +import subprocess +result = subprocess.run( + ['nvidia-smi', '--query-gpu=utilization.gpu,utilization.memory,temperature.gpu', + '--format=csv,noheader,nounits'], + capture_output=True, text=True +) +for i, line in enumerate(result.stdout.strip().split('\\n')): + gpu_util, mem_util, temp = line.split(', ') + print(f'GPU {i}: 利用率={gpu_util}%, 内存={mem_util}%, 温度={temp}°C')" +``` + +### CUDA 同步开销 + +```bash +probing $ENDPOINT eval " +import torch +import time + +# 测量同步开销 +start = time.perf_counter() +torch.cuda.synchronize() +sync_time = time.perf_counter() - start +print(f'CUDA 同步时间: {sync_time*1000:.2f} ms')" +``` + +## 内存带宽 + +### 内存受限操作 + +```bash +probing $ENDPOINT query " +SELECT + module, + AVG(allocated) as avg_memory_mb, + AVG(duration) as avg_time_sec, + AVG(allocated) / AVG(duration) as memory_bandwidth_mb_per_sec +FROM python.torch_trace +WHERE duration > 0.001 +GROUP BY module +ORDER BY memory_bandwidth_mb_per_sec DESC +LIMIT 10" +``` + +## 数据加载性能 + +### 数据加载器计时 + +```bash +probing $ENDPOINT eval " +import time + +# 计时一个批次加载 +start = time.perf_counter() +batch = next(iter(train_loader)) +load_time = time.perf_counter() - start +print(f'批次加载时间: {load_time*1000:.2f} ms')" +``` + +### Worker 分析 + +```bash +probing $ENDPOINT eval " +print(f'Num workers: {train_loader.num_workers}') +print(f'Pin memory: {train_loader.pin_memory}') +print(f'Prefetch factor: {getattr(train_loader, \"prefetch_factor\", 2)}')" +``` + +## 通信开销（分布式） + +### NCCL 操作时间 + +```bash +probing $ENDPOINT query " +SELECT + operation_type, + COUNT(*) as count, + AVG(duration_ms) as avg_time_ms, + MAX(duration_ms) as max_time_ms +FROM python.nccl_trace +GROUP BY operation_type +ORDER BY avg_time_ms DESC" +``` + +### All-Reduce 扩展 + +```bash +probing $ENDPOINT eval " +import torch.distributed as dist +import time + +if dist.is_initialized(): + tensor = torch.randn(1000000, device='cuda') + + start = time.perf_counter() + dist.all_reduce(tensor) + torch.cuda.synchronize() + allreduce_time = time.perf_counter() - start + + print(f'4MB All-reduce 时间: {allreduce_time*1000:.2f} ms')" +``` + +## Attention 瓶颈 + +### Self-Attention 分析 + +```bash +probing $ENDPOINT query " +SELECT + module, + AVG(duration) as avg_time, + AVG(allocated) as avg_memory +FROM python.torch_trace +WHERE module LIKE '%attention%' OR module LIKE '%attn%' +GROUP BY module +ORDER BY avg_time DESC" +``` + +### 每序列长度的内存 + +```bash +probing $ENDPOINT eval " +import torch + +# 检查 attention 内存扩展 +seq_len = model.config.max_position_embeddings +hidden = model.config.hidden_size +num_heads = model.config.num_attention_heads + +# Attention 分数内存: O(seq_len^2) +attention_memory = seq_len * seq_len * num_heads * 4 / 1024**3 # GB +print(f'估计 attention 内存: {attention_memory:.2f} GB')" +``` + +## 优化建议 + +### 基于分析的优化 + +```bash +# 1. 识别最慢模块 +probing $ENDPOINT query " +SELECT module, AVG(duration) as avg_time +FROM python.torch_trace +GROUP BY module +ORDER BY avg_time DESC +LIMIT 5" + +# 2. 检查是计算受限还是内存受限 +probing $ENDPOINT eval " +import torch +# 高计算利用率 + 低内存带宽 = 计算受限 +# 低计算利用率 + 高内存利用率 = 内存受限" +``` + +### 常见优化 + +#### 启用 Torch Compile + +```bash +probing $ENDPOINT eval " +import torch +if hasattr(torch, 'compile'): + model = torch.compile(model) + print('模型已用 torch.compile 编译')" +``` + +#### 启用混合精度 + +```bash +probing $ENDPOINT eval " +from torch.cuda.amp import autocast +print(f'AMP 已启用: {torch.cuda.amp.autocast_mode.is_autocast_enabled()}')" +``` + +#### 检查梯度检查点 + +```bash +probing $ENDPOINT eval " +# 检查是否启用梯度检查点 +for name, module in model.named_modules(): + if hasattr(module, 'gradient_checkpointing'): + print(f'{name}: checkpoint={module.gradient_checkpointing}')" +``` + +## 基准测试 + +### 吞吐量测量 + +```bash +probing $ENDPOINT eval " +import time + +# 测量 10 步的吞吐量 +steps = 10 +start = time.perf_counter() +for _ in range(steps): + trainer.train_step() +elapsed = time.perf_counter() - start + +samples_per_sec = (steps * batch_size) / elapsed +print(f'吞吐量: {samples_per_sec:.1f} samples/sec')" +``` + +### 优化前后对比 + +```bash +# 优化前 +probing $ENDPOINT query " +SELECT AVG(duration) as before_avg +FROM python.torch_trace +WHERE step BETWEEN 100 AND 110" + +# 优化后 +probing $ENDPOINT query " +SELECT AVG(duration) as after_avg +FROM python.torch_trace +WHERE step BETWEEN 200 AND 210" +``` diff --git a/docs/src/examples/training-debugging.md b/docs/src/examples/training-debugging.md new file mode 100644 index 00000000..9a7bb041 --- /dev/null +++ b/docs/src/examples/training-debugging.md @@ -0,0 +1,221 @@ +# Training Debugging Examples + +Common training debugging scenarios and solutions. + +## Training Hangs + +### Symptoms + +- Loss stops updating +- No error messages +- Process still running + +### Diagnosis + +```bash +# 1. Capture stack trace +probing $ENDPOINT backtrace + +# 2. Check where execution is stuck +probing $ENDPOINT query " +SELECT func, file, lineno, depth +FROM python.backtrace +ORDER BY depth +LIMIT 10" + +# 3. Check thread states +probing $ENDPOINT eval " +import threading +for t in threading.enumerate(): + print(f'{t.name}: alive={t.is_alive()}')" +``` + +### Common Causes + +#### Data Loader Stall + +```bash +# Check data loader +probing $ENDPOINT eval " +import torch.utils.data +print(f'DataLoader workers: {train_loader.num_workers}') +print(f'Prefetch factor: {train_loader.prefetch_factor}')" +``` + +#### Distributed Deadlock + +```bash +# Check distributed state +probing $ENDPOINT eval " +import torch.distributed as dist +if dist.is_initialized(): + print(f'Rank: {dist.get_rank()}') + print(f'Waiting for barrier...') + # Don't actually call barrier here!" +``` + +## Loss Explosion + +### Symptoms + +- Loss becomes NaN or Inf +- Training diverges + +### Diagnosis + +```bash +# Check for NaN in gradients +probing $ENDPOINT eval " +import torch +for name, param in model.named_parameters(): + if param.grad is not None: + has_nan = torch.isnan(param.grad).any().item() + has_inf = torch.isinf(param.grad).any().item() + if has_nan or has_inf: + print(f'{name}: NaN={has_nan}, Inf={has_inf}')" +``` + +### Fix: Gradient Clipping + +```bash +# Check current clipping +probing $ENDPOINT eval " +print(f'Grad clip value: {getattr(trainer, \"grad_clip\", None)}')" + +# Apply emergency clipping +probing $ENDPOINT eval " +torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)" +``` + +## Slow Training + +### Diagnosis + +```bash +# Find slowest modules +probing $ENDPOINT query " +SELECT + module, + stage, + COUNT(*) as count, + AVG(duration) as avg_time, + SUM(duration) as total_time +FROM python.torch_trace +WHERE step > (SELECT MAX(step) - 5 FROM python.torch_trace) +GROUP BY module, stage +ORDER BY total_time DESC +LIMIT 10" +``` + +### Check GPU Utilization + +```bash +probing $ENDPOINT eval " +import subprocess +result = subprocess.run(['nvidia-smi', '--query-gpu=utilization.gpu', '--format=csv,noheader'], + capture_output=True, text=True) +print(f'GPU Utilization: {result.stdout.strip()}')" +``` + +## Memory Issues During Training + +### Track Memory Growth + +```bash +probing $ENDPOINT query " +SELECT + step, + MAX(allocated) as peak_memory, + MAX(allocated) - MIN(allocated) as memory_range +FROM python.torch_trace +WHERE step > (SELECT MAX(step) - 20 FROM python.torch_trace) +GROUP BY step +ORDER BY step" +``` + +### Force Cleanup + +```bash +probing $ENDPOINT eval " +import gc +import torch + +# Clear gradients +model.zero_grad(set_to_none=True) + +# Garbage collection +gc.collect() + +# Clear CUDA cache +if torch.cuda.is_available(): + torch.cuda.empty_cache() + +print('Cleanup complete')" +``` + +## Checkpoint Recovery + +### Save Emergency Checkpoint + +```bash +probing $ENDPOINT eval " +import torch +checkpoint = { + 'step': trainer.current_step, + 'model': model.state_dict(), + 'optimizer': optimizer.state_dict(), +} +torch.save(checkpoint, 'emergency_checkpoint.pt') +print('Emergency checkpoint saved')" +``` + +### Inspect Checkpoint + +```bash +probing $ENDPOINT eval " +import torch +ckpt = torch.load('checkpoint.pt', map_location='cpu') +print(f'Keys: {ckpt.keys()}') +print(f'Step: {ckpt.get(\"step\", \"N/A\")}')" +``` + +## Learning Rate Issues + +### Check Current LR + +```bash +probing $ENDPOINT eval " +for i, pg in enumerate(optimizer.param_groups): + print(f'Group {i}: lr={pg[\"lr\"]}, weight_decay={pg.get(\"weight_decay\", 0)}')" +``` + +### Adjust LR + +```bash +# Reduce learning rate +probing $ENDPOINT eval " +for pg in optimizer.param_groups: + pg['lr'] *= 0.1 +print(f'New LR: {optimizer.param_groups[0][\"lr\"]}')" +``` + +## Distributed Training Issues + +### Check All Ranks + +```bash +# Run on each node +probing -t node1:8080 eval "print(f'Rank 0 step: {trainer.current_step}')" +probing -t node2:8080 eval "print(f'Rank 1 step: {trainer.current_step}')" +``` + +### Verify Synchronization + +```bash +probing $ENDPOINT eval " +import torch.distributed as dist +if dist.is_initialized(): + tensor = torch.tensor([trainer.current_step], device='cuda') + dist.all_reduce(tensor) + print(f'Sum of steps across ranks: {tensor.item()}')" +``` diff --git a/docs/src/examples/training-debugging.zh.md b/docs/src/examples/training-debugging.zh.md new file mode 100644 index 00000000..5e65b0a0 --- /dev/null +++ b/docs/src/examples/training-debugging.zh.md @@ -0,0 +1,220 @@ +# 训练调试示例 + +常见的训练调试场景和解决方案。 + +## 训练卡住 + +### 症状 + +- 损失停止更新 +- 没有错误消息 +- 进程仍在运行 + +### 诊断 + +```bash +# 1. 捕获堆栈跟踪 +probing $ENDPOINT backtrace + +# 2. 检查执行卡在哪里 +probing $ENDPOINT query " +SELECT func, file, lineno, depth +FROM python.backtrace +ORDER BY depth +LIMIT 10" + +# 3. 检查线程状态 +probing $ENDPOINT eval " +import threading +for t in threading.enumerate(): + print(f'{t.name}: alive={t.is_alive()}')" +``` + +### 常见原因 + +#### 数据加载器阻塞 + +```bash +# 检查数据加载器 +probing $ENDPOINT eval " +import torch.utils.data +print(f'DataLoader workers: {train_loader.num_workers}') +print(f'Prefetch factor: {train_loader.prefetch_factor}')" +``` + +#### 分布式死锁 + +```bash +# 检查分布式状态 +probing $ENDPOINT eval " +import torch.distributed as dist +if dist.is_initialized(): + print(f'Rank: {dist.get_rank()}') + print(f'World Size: {dist.get_world_size()}')" +``` + +## 损失爆炸 + +### 症状 + +- 损失变成 NaN 或 Inf +- 训练发散 + +### 诊断 + +```bash +# 检查梯度中的 NaN +probing $ENDPOINT eval " +import torch +for name, param in model.named_parameters(): + if param.grad is not None: + has_nan = torch.isnan(param.grad).any().item() + has_inf = torch.isinf(param.grad).any().item() + if has_nan or has_inf: + print(f'{name}: NaN={has_nan}, Inf={has_inf}')" +``` + +### 修复：梯度裁剪 + +```bash +# 检查当前裁剪设置 +probing $ENDPOINT eval " +print(f'Grad clip value: {getattr(trainer, \"grad_clip\", None)}')" + +# 应用紧急裁剪 +probing $ENDPOINT eval " +torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)" +``` + +## 训练慢 + +### 诊断 + +```bash +# 找到最慢的模块 +probing $ENDPOINT query " +SELECT + module, + stage, + COUNT(*) as count, + AVG(duration) as avg_time, + SUM(duration) as total_time +FROM python.torch_trace +WHERE step > (SELECT MAX(step) - 5 FROM python.torch_trace) +GROUP BY module, stage +ORDER BY total_time DESC +LIMIT 10" +``` + +### 检查 GPU 利用率 + +```bash +probing $ENDPOINT eval " +import subprocess +result = subprocess.run(['nvidia-smi', '--query-gpu=utilization.gpu', '--format=csv,noheader'], + capture_output=True, text=True) +print(f'GPU 利用率: {result.stdout.strip()}')" +``` + +## 训练期间的内存问题 + +### 追踪内存增长 + +```bash +probing $ENDPOINT query " +SELECT + step, + MAX(allocated) as peak_memory, + MAX(allocated) - MIN(allocated) as memory_range +FROM python.torch_trace +WHERE step > (SELECT MAX(step) - 20 FROM python.torch_trace) +GROUP BY step +ORDER BY step" +``` + +### 强制清理 + +```bash +probing $ENDPOINT eval " +import gc +import torch + +# 清除梯度 +model.zero_grad(set_to_none=True) + +# 垃圾回收 +gc.collect() + +# 清理 CUDA 缓存 +if torch.cuda.is_available(): + torch.cuda.empty_cache() + +print('清理完成')" +``` + +## 检查点恢复 + +### 保存紧急检查点 + +```bash +probing $ENDPOINT eval " +import torch +checkpoint = { + 'step': trainer.current_step, + 'model': model.state_dict(), + 'optimizer': optimizer.state_dict(), +} +torch.save(checkpoint, 'emergency_checkpoint.pt') +print('紧急检查点已保存')" +``` + +### 检查检查点 + +```bash +probing $ENDPOINT eval " +import torch +ckpt = torch.load('checkpoint.pt', map_location='cpu') +print(f'Keys: {ckpt.keys()}') +print(f'Step: {ckpt.get(\"step\", \"N/A\")}')" +``` + +## 学习率问题 + +### 检查当前学习率 + +```bash +probing $ENDPOINT eval " +for i, pg in enumerate(optimizer.param_groups): + print(f'Group {i}: lr={pg[\"lr\"]}, weight_decay={pg.get(\"weight_decay\", 0)}')" +``` + +### 调整学习率 + +```bash +# 降低学习率 +probing $ENDPOINT eval " +for pg in optimizer.param_groups: + pg['lr'] *= 0.1 +print(f'新学习率: {optimizer.param_groups[0][\"lr\"]}')" +``` + +## 分布式训练问题 + +### 检查所有 Rank + +```bash +# 在每个节点上运行 +probing -t node1:8080 eval "print(f'Rank 0 step: {trainer.current_step}')" +probing -t node2:8080 eval "print(f'Rank 1 step: {trainer.current_step}')" +``` + +### 验证同步 + +```bash +probing $ENDPOINT eval " +import torch.distributed as dist +if dist.is_initialized(): + tensor = torch.tensor([trainer.current_step], device='cuda') + dist.all_reduce(tensor) + print(f'所有 rank 步数之和: {tensor.item()}')" +``` diff --git a/docs/src/getting-started/introduction.md b/docs/src/getting-started/introduction.md deleted file mode 100644 index 58c659c5..00000000 --- a/docs/src/getting-started/introduction.md +++ /dev/null @@ -1,452 +0,0 @@ -# Introduction - -Welcome to **Probing** - a dynamic performance profiler and stability diagnostic tool designed specifically for AI applications. Probing addresses the unique challenges of debugging and optimizing large-scale, distributed, long-running AI workloads such as LLM training and inference. - -## What is Probing? - -Probing is a runtime analysis tool that injects probes into running Python processes to collect detailed performance data and enable real-time monitoring without requiring code modifications or process restarts. Think of it as a powerful magnifying glass for your AI applications that reveals exactly what's happening under the hood. - -## Core Principles - -Probing is built on three fundamental principles: - -### 🔍 **Zero Intrusion** -- No code modifications required -- No environment setup changes needed -- No workflow disruptions -- Dynamic probe injection into running processes - -### 🎯 **Zero Learning Curve** -- Standard SQL interface for data analysis -- Familiar database query patterns -- Intuitive command-line tools -- Web-based dashboard for visualization - -### 📦 **Zero Deployment Burden** -- Single binary deployment (Rust-based) -- Static compilation with minimal dependencies -- Cross-platform compatibility -- Elastic scaling capabilities - -## Key Features - -### 🎯 **Live Code Execution (`eval`)** -- **Arbitrary Python Code**: Run any Python code inside your target process -- **Real-time State Inspection**: Check variables, objects, and system state instantly -- **Dynamic Behavior Modification**: Change configuration, clear caches, or trigger actions -- **Custom Metrics Collection**: Gather any data your application can compute - -### 📊 **SQL-Based Analytics (`query`)** -- **Structured Performance Data**: Query PyTorch traces, call stacks, and system metrics -- **Familiar SQL Interface**: Use standard SQL syntax with powerful time-series functions -- **Real Tables, Real Data**: Query actual collected data, not synthetic examples -- **Cross-correlation Analysis**: Understand relationships between performance metrics - -### 🔍 **Deep Stack Analysis (`backtrace`)** -- **Real-time Call Stack Capture**: Get live execution context with variable values from the main thread -- **Python Variable Inspection**: See current local variables, function arguments, and object state -- **Live Stack Analysis**: Query current execution state of the main thread -- **Main Thread Focus**: Analyze the primary execution path and main thread activity - -### 🚀 **Production-Ready Foundation** -- **Zero-Intrusion Injection**: Attach to running processes without code changes -- **Minimal Performance Impact**: < 5% overhead in most real-world scenarios -- **Cross-Platform Support**: Works on Linux, macOS, and Windows -- **Distributed System Ready**: Monitor processes across multiple machines - -## Three Core Capabilities - -Probing provides three powerful capabilities that work together to give you complete insight into your running applications: - -> **📝 Note**: All examples below use `$ENDPOINT` to represent your target process. Set this to your process ID (e.g., `export ENDPOINT=12345`) or remote address (e.g., `export ENDPOINT=host:port`). See the [Quick Start](#quick-start-your-first-5-minutes) section for detailed setup instructions. - -### 🎯 **eval**: Execute Code in Live Processes -Run arbitrary Python code directly inside your target process to inspect state, modify behavior, or gather custom metrics. - -```bash -# Check training threads -probing $ENDPOINT eval "import threading; [print(f'{t.name}: {t.is_alive()}') for t in threading.enumerate()]" -# Expected output: -# MainThread: True -# Thread-1 (train_worker): True -# Thread-2 (data_loader): True - -# Check GPU memory usage -probing $ENDPOINT eval "import torch; print(f'GPU: {torch.cuda.memory_allocated()/1024**3:.1f}GB allocated, {torch.cuda.memory_reserved()/1024**3:.1f}GB cached') if torch.cuda.is_available() else print('CUDA unavailable')" -# Expected output (with GPU): -# GPU: 2.4GB allocated, 3.2GB cached -# Expected output (without GPU): -# CUDA unavailable -``` - -### 📊 **query**: Analyze Data with SQL -Query structured performance data using familiar SQL syntax to identify patterns, bottlenecks, and trends. - -```bash -# Analyze PyTorch training patterns -probing $ENDPOINT query " -SELECT - step, - module, - SUM(allocated) as total_memory_mb, - COUNT(*) as operation_count -FROM python.torch_trace -WHERE step > 100 -GROUP BY step, module -ORDER BY total_memory_mb DESC -LIMIT 10" -# Expected output: -# step | module | total_memory_mb | operation_count -# ------|------------------|-----------------|---------------- -# 150 | transformer.attn | 2048.5 | 24 -# 149 | transformer.attn | 2047.8 | 24 -# 151 | transformer.mlp | 1536.2 | 16 -# 150 | transformer.mlp | 1535.9 | 16 -# ... -``` - -### 🔍 **backtrace**: Debug with Stack Context -Capture detailed call stacks with Python variable values to understand exactly what your main thread is doing at any moment. - -```bash -# Capture current call stack from the main thread -probing $ENDPOINT backtrace - -# Query the live main thread stack trace with variable inspection -probing $ENDPOINT query "SELECT func, file, lineno FROM python.backtrace ORDER BY depth LIMIT 3" -# Expected output: -# func | file | lineno -# ---------------|-----------------------|-------- -# forward_pass | /app/training_loop.py | 89 -# transformer | /app/model.py | 245 -# multi_head_attn| /app/attention.py | 156 - -# The python.backtrace table shows main thread execution context: -# - func: function name -# - file: source file path -# - lineno: line number -# - depth: call stack depth (0 = deepest frame) -``` - ---- - -## Real-World Debugging Scenarios - -Now that you understand the three core capabilities, let's see how they work together to solve common AI/ML debugging challenges: - -### Scenario 1: Training Process Hanging -**Problem**: PyTorch training suddenly stops progressing. -**Solution**: Use the three capabilities in sequence: - -```bash -# 1. BACKTRACE: See what main thread is doing right now -probing $ENDPOINT backtrace - -# 2. EVAL: Check broader system state -probing $ENDPOINT eval "import threading; [(t.name, t.is_alive(), t.daemon) for t in threading.enumerate()]" -# Expected output: -# [('MainThread', True, False), ('Thread-1', False, False), ('Thread-2', True, True)] - -# 3. QUERY: Analyze the captured stack context -probing $ENDPOINT query "SELECT func, file, lineno, depth FROM python.backtrace ORDER BY depth LIMIT 10" -# Expected output: (shows top 10 stack frames) -# func | file | lineno | depth -# -------------|-------------------|--------|------- -# wait_for_data| /app/dataloader.py| 234 | 0 -# get_batch | /app/training.py | 89 | 1 -# train_step | /app/main.py | 156 | 2 -# ... (up to 10 frames total) -``` - -### Scenario 2: Memory Leak Investigation -**Problem**: Memory usage keeps growing during training. -**Solution**: Monitor, analyze, then correlate: - -```bash -# EVAL: Force cleanup and get current state -probing $ENDPOINT eval "import gc, torch; gc.collect(); torch.cuda.empty_cache() if torch.cuda.is_available() else None; print('Cleanup complete')" -# Expected output: -# Cleanup complete - -# QUERY: Analyze allocation trends over recent training steps -probing $ENDPOINT query "SELECT step, AVG(allocated) as avg_memory, MAX(allocated) as peak_memory FROM python.torch_trace WHERE step >= (SELECT MAX(step) - 20 FROM python.torch_trace) GROUP BY step ORDER BY step" -# Expected output: -# step | avg_memory | peak_memory -# -----|------------|------------- -# 980 | 1024.5 | 1156.8 -# 981 | 1026.2 | 1158.9 -# 982 | 1028.7 | 1161.4 # ← Memory growing! -# 983 | 1031.1 | 1164.2 -# ... -``` - -### Scenario 3: Performance Bottleneck Analysis -**Problem**: Need to identify which model components are slowest. -**Solution**: Real-time profiling with contextual analysis: - -```bash -# BACKTRACE: Capture execution state during slow periods -probing $ENDPOINT backtrace - -# QUERY: Find most expensive operations across recent steps -probing $ENDPOINT query "SELECT module, stage, AVG(allocated) as avg_memory, COUNT(*) as frequency FROM python.torch_trace WHERE step >= (SELECT MAX(step) - 5 FROM python.torch_trace) GROUP BY module, stage ORDER BY avg_memory DESC LIMIT 10" -# Expected output: -# module | stage | avg_memory | frequency -# ---------------------|----------|------------|---------- -# transformer.self_attn| forward | 2048.7 | 25 -# transformer.mlp | forward | 1536.4 | 20 -# embedding_layer | forward | 512.8 | 15 -# layer_norm | forward | 64.2 | 40 -# ... - -# EVAL: Get real-time system metrics for correlation -probing $ENDPOINT eval "import psutil; proc = psutil.Process(); print(f'CPU: {proc.cpu_percent():.1f}%, Memory: {proc.memory_info().rss/1024**3:.2f}GB, Threads: {proc.num_threads()}')" -# Expected output: -# CPU: 85.3%, Memory: 12.45GB, Threads: 8 -```` - -## Quick Start: Your First 5 Minutes - -Get immediate value from Probing with this streamlined workflow: - -### Step 1: Set Your Target Process - -All Probing commands need a target endpoint. Set `$ENDPOINT` to either a local process ID or remote address: - -```bash -# Local process - find and set your Python process ID -export ENDPOINT=$(pgrep -f "python.*your_script") -# Expected result: ENDPOINT now contains your process ID (e.g., 12345) - -# Or for remote processes -export ENDPOINT=remote-host:8080 -# Expected result: ENDPOINT set to remote address -``` - -> **💡 Need help finding processes?** Use `ps aux | grep python` or `pgrep -f "python.*train"` to locate your target. - -### Step 2: Connect and Explore (30 seconds) - -```bash -# Connect to your process -probing $ENDPOINT inject -# Expected output: -# Successfully injected probes into process 12345 - -# Get basic process info -probing $ENDPOINT eval "import os, psutil; proc = psutil.Process(); print(f'PID: {os.getpid()}, Memory: {proc.memory_info().rss/1024**2:.1f}MB')" -# Expected output: -# PID: 12345, Memory: 1250.4MB -``` - -### Step 3: Try All Three Core Capabilities (2 minutes) - -**📊 Query structured data:** -```bash -probing $ENDPOINT query "SELECT name, value FROM information_schema.df_settings LIMIT 5" -# Expected output: -# name | value -# ------------------------|-------- -# max_threads | 8 -# memory_pool_size | 2048 -# cache_enabled | true -# debug_mode | false -# profiling_interval | 100 -``` - -**🎯 Execute live code:** -```bash -probing $ENDPOINT eval "import torch; print(f'CUDA: {torch.cuda.is_available()}') if 'torch' in globals() else print('PyTorch not loaded')" -# Expected output (with PyTorch): -# CUDA: True -# Expected output (without PyTorch): -# PyTorch not loaded -``` - -**🔍 Capture execution context:** -```bash -probing $ENDPOINT backtrace - -probing $ENDPOINT query "SELECT func, file, lineno FROM python.backtrace ORDER BY depth LIMIT 5" -# Expected output: -# func | file | lineno -# ---------------|-----------------------|-------- -# forward | /app/model.py | 89 -# train_step | /app/training.py | 156 -# main_loop | /app/main.py | 234 -# ... -``` - -### Step 4: Real Debugging Workflow (2 minutes) - -Combine all capabilities to debug like a pro: -```bash -# 1. Capture current state -probing $ENDPOINT backtrace - -# 2. Check what's happening now -probing $ENDPOINT eval "import threading; print('Active threads:', len(threading.enumerate()))" -# Expected output: -# Active threads: 5 - -# 3. Analyze the results -probing $ENDPOINT query "SELECT func, file, lineno FROM python.backtrace ORDER BY depth" -# Expected output: -# func | file | lineno -# --------------------|-------------------------|-------- -# compute_loss | /app/loss.py | 45 -# forward_pass | /app/model.py | 123 -# train_batch | /app/training.py | 89 -# main_loop | /app/main.py | 234 -# | /app/main.py | 15 -``` - -**🎉 That's it!** You're now using all three core capabilities. Continue reading for advanced techniques and real-world scenarios. - ---- - -## Detailed Setup Guide - -For production deployments and distributed setups, here's the complete configuration: - -### Local Process Discovery -```bash -# Find your training process -ps aux | grep python | grep train -# Expected output: -# user 12345 15.2 8.5 2048576 1048576 ? S 10:30 0:05 python train_model.py -# user 12346 2.1 1.2 524288 131072 ? S 10:31 0:01 python data_loader.py - -# Or use pgrep for specific patterns -pgrep -f "python.*train" -# Expected output: -# 12345 -# 12346 -``` - -### Remote Process Setup -For distributed setups, target processes need network server enabled: -```bash -# On the remote machine - start your Python process with remote server -PROBING_PORT=8080 python your_training_script.py -# Expected output: -# Probing server listening on 0.0.0.0:8080 -# Starting training... - -# From your local machine - connect directly to the remote process -export ENDPOINT=remote-host:8080 -# Expected result: Commands now target the remote process -``` - -### Process Launch Options -```bash -# Option A: Launch your application with probing enabled -PROBE=1 python your_app.py -# Expected output: -# Probing enabled for process 12345 -# Your application output follows... - -# Option B: Attach to an already running process (recommended) -probing $ENDPOINT inject -# Expected output: -# Successfully injected probes into process 12345 -``` - ---- - -## Who Should Use Probing? - -Based on the capabilities and scenarios above, Probing is designed for different roles in AI/ML teams: - -### AI/ML Engineers -**Debug training instabilities and optimize model performance** -- "Why did my training suddenly diverge at step 15,000?" → Use `backtrace` + `query` to see exact main thread state -- "Which layer is using the most GPU memory?" → Use `eval` to inspect torch memory + `query` torch_trace -- "Is my data loader causing bottlenecks?" → Use `eval` to check thread states and `backtrace` for main thread analysis - -### DevOps Engineers -**Monitor production AI services and troubleshoot issues** -- "Service is using 90% CPU but I can't reproduce it" → Use `inject` + `eval` to inspect live production -- "Memory usage keeps growing, is it a leak?" → Use `eval` + `query` for memory trend analysis -- "Which requests are taking the longest?" → Use `backtrace` to capture current main thread execution state of slow requests - -### Research Scientists -**Profile experimental models and analyze performance** -- "How does my new attention mechanism compare?" → Use `query` to analyze torch_trace data -- "Are my optimizations actually faster?" → Use `eval` for before/after benchmarks -- "What's the memory footprint of different model sizes?" → Use `query` for memory analytics - -### Platform Engineers -**Build monitoring infrastructure and optimize resource allocation** -- "Need custom metrics for our ML platform" → Use `eval` to collect any Python data -- "Want to track training progress across the fleet" → Use `query` for aggregated analytics -- "Need to debug distributed training issues" → Use `backtrace` to inspect live main thread execution state, combined with `eval` for multi-thread analysis - ---- - -## How Probing Works: Architecture Overview - -Understanding Probing's architecture helps you use it more effectively: - -### Data Plane (Probes) -- Lightweight probing components injected into target processes -- Collect performance metrics and system data -- Minimal performance overhead (<5% in most cases) -- Distributed architecture with no single point of failure - -### Control Plane (Interface) -- Command-line tools for scripting and automation (what you've been using above) -- Web UI for visualization and dashboard creation -- REST API for integration with other tools -- SQL query engine for data analysis - -This architecture enables the zero-intrusion, zero-setup experience you've seen in the examples above. - ---- - -## Comparison with Other Tools - -| Feature | Probing | Traditional Profilers | APM Solutions | -|---------|---------|---------------------|---------------| -| **AI/ML Focus** | ✅ Native PyTorch support | ❌ Generic profiling | ⚠️ Limited ML features | -| **Zero Setup** | ✅ Dynamic injection | ❌ Code instrumentation | ❌ Agent installation | -| **SQL Interface** | ✅ Full SQL support | ❌ Limited querying | ⚠️ Vendor-specific | -| **Real-time** | ✅ Live monitoring | ⚠️ Post-mortem analysis | ✅ Real-time | -| **Distributed** | ✅ Multi-node support | ❌ Single process | ✅ Distributed | -| **Cost** | ✅ Open source | ⚠️ Mixed | ❌ Expensive | - -## Getting Started - -Ready to dive deeper? Here's your recommended learning path: - -### 🚀 **Start Here** (Essential - 30 minutes) -1. **[Installation](installation.md)** - Set up Probing on your system - -### 🎯 **Deep Dive** (Choose based on your needs) -- **[SQL Analytics](../user-guide/sql-analytics.md)** - Advanced `query` techniques and data analysis -- **[Memory Analysis](../user-guide/memory-analysis.md)** - Tools for analyzing memory usage -- **[Debugging Guide](../user-guide/debugging.md)** - Expert `backtrace` usage for complex issues - -### 🔧 **Production & Advanced** (When you're ready to scale) -- **[Production Deployment](../deployment/production.md)** - Scale Probing for production workloads -- **[Performance Tuning](../user-guide/performance.md)** - Optimize Probing itself for minimal overhead -- **[Building from Source](../development/building.md)** - Build Probing from source code - -### 💡 **Learn from Examples** -Browse `examples/` directory for real-world patterns: -- **Training debugging workflows** - Common PyTorch issues and solutions -- **Production monitoring setups** - Real deployment patterns -- **Custom analytics queries** - Advanced SQL patterns for ML workloads - -## Community and Support - -- **GitHub Repository**: [github.com/reiase/probing](https://github.com/reiase/probing) -- **Documentation**: Browse this guide for comprehensive information -- **Examples**: Check the `examples/` directory for real-world usage patterns -- **Issues**: Report bugs and feature requests on GitHub - -Probing is actively developed and welcomes community contributions. Whether you're fixing bugs, adding features, or improving documentation, your help makes Probing better for everyone. - ---- - -**Next**: Learn how to [install Probing](installation.md) on your system. diff --git a/docs/src/guide/debugging.md b/docs/src/guide/debugging.md new file mode 100644 index 00000000..ea3a7dca --- /dev/null +++ b/docs/src/guide/debugging.md @@ -0,0 +1,229 @@ +# Debugging Guide + +Master the art of debugging AI applications with Probing's powerful introspection capabilities. + +## Overview + +Probing provides three complementary debugging approaches: + +- **backtrace**: Capture execution context with stack frames +- **eval**: Execute arbitrary Python code in the target process +- **query**: Analyze collected data with SQL + +## Stack Analysis + +### Capture Current Stack + +```bash +# Get current execution stack +probing $ENDPOINT backtrace + +# Query stack frames +probing $ENDPOINT query " +SELECT func, file, lineno, depth +FROM python.backtrace +ORDER BY depth +LIMIT 10" +``` + +### Understanding Stack Frames + +The `python.backtrace` table provides: + +| Column | Description | +|--------|-------------| +| `func` | Function name | +| `file` | Source file path | +| `lineno` | Line number | +| `depth` | Stack depth (0 = deepest) | +| `frame_type` | 'Python' or 'Native' | + +### Find Where Code Is Stuck + +```bash +# Capture stack +probing $ENDPOINT backtrace + +# Check top of stack +probing $ENDPOINT query " +SELECT func, file, lineno +FROM python.backtrace +WHERE depth = 0" +``` + +## Live Inspection + +### Inspect Variables + +```bash +# Check global variables +probing $ENDPOINT eval "print(globals().keys())" + +# Inspect specific object +probing $ENDPOINT eval "print(type(model), model)" + +# Check model parameters +probing $ENDPOINT eval " +for name, param in model.named_parameters(): + print(f'{name}: {param.shape}, grad={param.grad is not None}') +" +``` + +### Check Thread State + +```bash +probing $ENDPOINT eval " +import threading +for t in threading.enumerate(): + print(f'{t.name}: alive={t.is_alive()}, daemon={t.daemon}') +" +``` + +### Monitor Training Progress + +```bash +probing $ENDPOINT eval " +# Check current step +print(f'Current step: {trainer.current_step}') +print(f'Current loss: {trainer.last_loss}') +print(f'Learning rate: {optimizer.param_groups[0][\"lr\"]}') +" +``` + +## Debugging Scenarios + +### Scenario 1: Training Hangs + +**Symptoms**: Training progress stops, no errors. + +**Diagnosis**: + +```bash +# 1. Capture stack +probing $ENDPOINT backtrace + +# 2. Check what's blocking +probing $ENDPOINT query " +SELECT func, file, lineno +FROM python.backtrace +WHERE depth < 5" + +# 3. Check thread states +probing $ENDPOINT eval " +import threading +for t in threading.enumerate(): + print(f'{t.name}: {t.is_alive()}')" + +# 4. Check for deadlocks +probing $ENDPOINT eval " +import torch.distributed as dist +if dist.is_initialized(): + print(f'Rank: {dist.get_rank()}, World: {dist.get_world_size()}')" +``` + +### Scenario 2: NaN/Inf in Gradients + +**Symptoms**: Loss becomes NaN or Inf. + +**Diagnosis**: + +```bash +# Check for NaN in model parameters +probing $ENDPOINT eval " +import torch +for name, param in model.named_parameters(): + if param.grad is not None: + if torch.isnan(param.grad).any(): + print(f'NaN gradient in {name}') + if torch.isinf(param.grad).any(): + print(f'Inf gradient in {name}')" + +# Check loss value +probing $ENDPOINT eval " +import torch +print(f'Loss: {loss.item()}') +print(f'IsNaN: {torch.isnan(loss).item()}') +print(f'IsInf: {torch.isinf(loss).item()}')" +``` + +### Scenario 3: Slow Training + +**Symptoms**: Training slower than expected. + +**Diagnosis**: + +```sql +-- Find slowest operations +SELECT module, stage, avg(duration) as avg_time +FROM python.torch_trace +WHERE step > (SELECT max(step) - 5 FROM python.torch_trace) +GROUP BY module, stage +ORDER BY avg_time DESC +LIMIT 10; +``` + +```bash +# Check GPU utilization +probing $ENDPOINT eval " +import torch +if torch.cuda.is_available(): + print(f'CUDA synchronize time test...') + import time + start = time.time() + torch.cuda.synchronize() + print(f'Sync took: {time.time() - start:.3f}s')" +``` + +## Advanced Debugging + +### Conditional Breakpoints (Conceptual) + +```bash +# Monitor until condition is met +probing $ENDPOINT eval " +import torch +step = trainer.current_step +loss = trainer.last_loss +if loss > 10 or torch.isnan(torch.tensor(loss)): + print(f'ALERT: Step {step}, Loss {loss}') + # Trigger investigation +" +``` + +### Data Pipeline Debugging + +```bash +# Check data loader +probing $ENDPOINT eval " +batch = next(iter(train_loader)) +print(f'Batch shape: {batch[0].shape}') +print(f'Batch dtype: {batch[0].dtype}') +print(f'Has NaN: {torch.isnan(batch[0]).any()}')" +``` + +### Distributed Debugging + +```bash +# Check distributed state +probing $ENDPOINT eval " +import torch.distributed as dist +if dist.is_initialized(): + print(f'Backend: {dist.get_backend()}') + print(f'Rank: {dist.get_rank()}') + print(f'World size: {dist.get_world_size()}') + print(f'Is NCCL available: {dist.is_nccl_available()}')" +``` + +## Best Practices + +1. **Start with backtrace** - Understand where execution is before diving deeper +2. **Use query for trends** - SQL is great for analyzing patterns over time +3. **Use eval for real-time** - Get current state with Python code +4. **Combine approaches** - backtrace → eval → query workflow +5. **Log important states** - Use eval to print diagnostic information + +## Next Steps + +- [Memory Analysis](memory-analysis.md) - Debug memory issues +- [Troubleshooting](troubleshooting.md) - Common problems and solutions +- [Design Architecture](../design/architecture.md) - Understand internals diff --git a/docs/src/guide/debugging.zh.md b/docs/src/guide/debugging.zh.md new file mode 100644 index 00000000..3208b503 --- /dev/null +++ b/docs/src/guide/debugging.zh.md @@ -0,0 +1,177 @@ +# 调试指南 + +掌握使用 Probing 强大的内省能力调试 AI 应用的艺术。 + +## 概览 + +Probing 提供三种互补的调试方式： + +- **backtrace**：捕获带有堆栈帧的执行上下文 +- **eval**：在目标进程中执行任意 Python 代码 +- **query**：使用 SQL 分析收集的数据 + +## 堆栈分析 + +### 捕获当前堆栈 + +```bash +# 获取当前执行堆栈 +probing $ENDPOINT backtrace + +# 查询堆栈帧 +probing $ENDPOINT query " +SELECT func, file, lineno, depth +FROM python.backtrace +ORDER BY depth +LIMIT 10" +``` + +### 理解堆栈帧 + +每个堆栈帧包括： + +| 字段 | 描述 | +|------|------| +| func | 函数名 | +| file | 源文件路径 | +| lineno | 行号 | +| depth | 堆栈深度（0 = 最内层）| +| frame_type | Python 或 Native | + +### 找到代码卡住的位置 + +```bash +# 捕获堆栈 +probing $ENDPOINT backtrace + +# 检查堆栈顶部 +probing $ENDPOINT query " +SELECT func, file, lineno +FROM python.backtrace +WHERE depth = 0" +``` + +## 实时检查 + +### 检查变量 + +```bash +# 检查全局变量 +probing $ENDPOINT eval "print(globals().keys())" + +# 检查特定对象 +probing $ENDPOINT eval "print(type(model), model)" + +# 检查模型参数 +probing $ENDPOINT eval " +for name, param in model.named_parameters(): + print(f'{name}: {param.shape}, grad={param.grad is not None}') +" +``` + +### 检查线程状态 + +```bash +probing $ENDPOINT eval " +import threading +for t in threading.enumerate(): + print(f'{t.name}: alive={t.is_alive()}, daemon={t.daemon}') +" +``` + +### 监控训练进度 + +```bash +probing $ENDPOINT eval " +# 检查当前步骤 +print(f'当前步骤: {trainer.current_step}') +print(f'当前损失: {trainer.last_loss}') +print(f'学习率: {optimizer.param_groups[0][\"lr\"]}') +" +``` + +## 调试场景 + +### 场景 1：训练卡住 + +**症状**：训练进度停止，没有错误。 + +**诊断**： + +```bash +# 1. 捕获堆栈 +probing $ENDPOINT backtrace + +# 2. 检查是什么阻塞了 +probing $ENDPOINT query " +SELECT func, file, lineno +FROM python.backtrace +WHERE depth < 5" + +# 3. 检查线程状态 +probing $ENDPOINT eval " +import threading +for t in threading.enumerate(): + print(f'{t.name}: {t.is_alive()}')" + +# 4. 检查死锁 +probing $ENDPOINT eval " +import torch.distributed as dist +if dist.is_initialized(): + print(f'Rank: {dist.get_rank()}, World: {dist.get_world_size()}')" +``` + +### 场景 2：梯度中出现 NaN/Inf + +**症状**：损失变成 NaN 或 Inf。 + +**诊断**： + +```bash +# 检查模型参数中的 NaN +probing $ENDPOINT eval " +import torch +for name, param in model.named_parameters(): + if param.grad is not None: + if torch.isnan(param.grad).any(): + print(f'{name} 梯度中有 NaN') + if torch.isinf(param.grad).any(): + print(f'{name} 梯度中有 Inf')" + +# 检查损失值 +probing $ENDPOINT eval " +import torch +print(f'损失: {loss.item()}') +print(f'是否 NaN: {torch.isnan(loss).item()}') +print(f'是否 Inf: {torch.isinf(loss).item()}')" +``` + +### 场景 3：训练慢 + +**症状**：训练比预期慢。 + +**诊断**： + +```sql +-- 找到最慢的操作 +SELECT module, stage, avg(duration) as avg_time +FROM python.torch_trace +WHERE step > (SELECT max(step) - 5 FROM python.torch_trace) +GROUP BY module, stage +ORDER BY avg_time DESC +LIMIT 10; +``` + +## 最佳实践 + +1. **先用 backtrace** - 在深入之前先了解执行位置 +2. **用 query 分析趋势** - SQL 非常适合分析时间模式 +3. **用 eval 获取实时状态** - 用 Python 代码获取当前状态 +4. **组合使用** - backtrace → eval → query 工作流 +5. **记录重要状态** - 使用 eval 打印诊断信息 + +## 下一步 + +- [内存分析](memory-analysis.zh.md) - 调试内存问题 +- [常见问题](troubleshooting.zh.md) - 常见问题和解决方案 +- [系统架构](../design/architecture.md) - 了解内部实现 diff --git a/docs/src/guide/index.md b/docs/src/guide/index.md new file mode 100644 index 00000000..b5c308a0 --- /dev/null +++ b/docs/src/guide/index.md @@ -0,0 +1,67 @@ +# User Guide + +Welcome to the Probing User Guide. This section covers the core features and usage patterns. + +## Overview + +Probing provides three core capabilities for analyzing and debugging your AI applications: + +| Capability | Command | Description | +|------------|---------|-------------| +| **eval** | `probing $ENDPOINT eval "..."` | Execute Python code in target process | +| **query** | `probing $ENDPOINT query "..."` | Query performance data with SQL | +| **backtrace** | `probing $ENDPOINT backtrace` | Capture execution stack with variables | + +## Getting Started + +If you're new to Probing, we recommend starting with these guides in order: + +1. **[SQL Analytics](sql-analytics.md)** - Learn the powerful SQL query interface +2. **[Memory Analysis](memory-analysis.md)** - Debug memory leaks and usage patterns +3. **[Debugging](debugging.md)** - Master stack analysis and live debugging +4. **[Troubleshooting](troubleshooting.md)** - Common issues and solutions + +## Core Concepts + +### Target Endpoint + +All Probing commands require a target endpoint, which can be: + +- **Process ID**: Local process (e.g., `12345`) +- **Remote Address**: Network endpoint (e.g., `host:8080`) + +```bash +# Set target endpoint +export ENDPOINT=12345 # or host:8080 +``` + +### Data Tables + +Probing exposes performance data through SQL tables: + +| Table | Description | +|-------|-------------| +| `python.backtrace` | Stack trace information | +| `python.torch_trace` | PyTorch execution traces | +| `python.variables` | Variable tracking | +| `information_schema.df_settings` | Configuration settings | + +### Workflow Patterns + +**Debugging Workflow:** +```bash +# 1. Capture current state +probing $ENDPOINT backtrace + +# 2. Inspect specific values +probing $ENDPOINT eval "print(my_variable)" + +# 3. Query historical data +probing $ENDPOINT query "SELECT * FROM python.torch_trace" +``` + +## Advanced Topics + +- **[Architecture](../design/architecture.md)** - System design and internals +- **[Distributed](../design/distributed.md)** - Multi-node monitoring +- **[Extensibility](../design/extensibility.md)** - Custom tables and metrics diff --git a/docs/src/guide/index.zh.md b/docs/src/guide/index.zh.md new file mode 100644 index 00000000..1446d41d --- /dev/null +++ b/docs/src/guide/index.zh.md @@ -0,0 +1,67 @@ +# 用户指南 + +欢迎阅读 Probing 用户指南。本章节介绍核心功能和使用模式。 + +## 概览 + +Probing 提供三个核心能力来分析和调试您的 AI 应用： + +| 能力 | 命令 | 描述 | +|------|------|------| +| **eval** | `probing $ENDPOINT eval "..."` | 在目标进程中执行 Python 代码 | +| **query** | `probing $ENDPOINT query "..."` | 使用 SQL 查询性能数据 | +| **backtrace** | `probing $ENDPOINT backtrace` | 捕获带变量的执行堆栈 | + +## 入门指南 + +如果您是 Probing 新用户，建议按以下顺序阅读这些指南： + +1. **[SQL 分析](sql-analytics.zh.md)** - 学习强大的 SQL 查询接口 +2. **[内存分析](memory-analysis.zh.md)** - 调试内存泄漏和使用模式 +3. **[调试指南](debugging.zh.md)** - 掌握堆栈分析和实时调试 +4. **[常见问题](troubleshooting.zh.md)** - 常见问题和解决方案 + +## 核心概念 + +### 目标端点 + +所有 Probing 命令都需要一个目标端点，可以是： + +- **进程 ID**：本地进程（如 `12345`） +- **远程地址**：网络端点（如 `host:8080`） + +```bash +# 设置目标端点 +export ENDPOINT=12345 # 或 host:8080 +``` + +### 数据表 + +Probing 通过 SQL 表暴露性能数据： + +| 表名 | 描述 | +|------|------| +| `python.backtrace` | 堆栈跟踪信息 | +| `python.torch_trace` | PyTorch 执行跟踪 | +| `python.variables` | 变量追踪 | +| `information_schema.df_settings` | 配置设置 | + +### 工作流模式 + +**调试工作流：** +```bash +# 1. 捕获当前状态 +probing $ENDPOINT backtrace + +# 2. 检查特定值 +probing $ENDPOINT eval "print(my_variable)" + +# 3. 查询历史数据 +probing $ENDPOINT query "SELECT * FROM python.torch_trace" +``` + +## 进阶主题 + +- **[系统架构](../design/architecture.md)** - 系统设计和内部实现 +- **[分布式](../design/distributed.md)** - 多节点监控 +- **[扩展机制](../design/extensibility.md)** - 自定义表和指标 diff --git a/docs/src/guide/memory-analysis.md b/docs/src/guide/memory-analysis.md new file mode 100644 index 00000000..1aca949a --- /dev/null +++ b/docs/src/guide/memory-analysis.md @@ -0,0 +1,217 @@ +# Memory Analysis + +Probing provides comprehensive tools for analyzing memory usage in AI applications. + +## Overview + +Memory issues are common in AI workloads, especially during training. Probing helps you: + +- Track GPU and CPU memory allocation +- Detect memory leaks +- Analyze memory usage patterns +- Optimize memory efficiency + +## Quick Memory Check + +```bash +# Get current memory state +probing $ENDPOINT eval " +import torch +import psutil + +proc = psutil.Process() +print(f'CPU Memory: {proc.memory_info().rss / 1024**3:.2f} GB') + +if torch.cuda.is_available(): + print(f'GPU Allocated: {torch.cuda.memory_allocated() / 1024**3:.2f} GB') + print(f'GPU Cached: {torch.cuda.memory_reserved() / 1024**3:.2f} GB') +" +``` + +## Memory Usage Trends + +### Track Memory Over Training Steps + +```sql +SELECT + step, + avg(allocated) as avg_memory_mb, + max(allocated) as peak_memory_mb, + min(allocated) as min_memory_mb +FROM python.torch_trace +WHERE step IS NOT NULL +GROUP BY step +ORDER BY step; +``` + +### Detect Memory Growth + +```sql +SELECT + step, + max(allocated) - min(allocated) as memory_growth_mb +FROM python.torch_trace +WHERE step > (SELECT max(step) - 10 FROM python.torch_trace) +GROUP BY step +HAVING max(allocated) - min(allocated) > 50 +ORDER BY step; +``` + +## Memory by Module + +Identify which model components use the most memory: + +```sql +SELECT + module, + stage, + avg(allocated) as avg_memory, + count(*) as execution_count +FROM python.torch_trace +WHERE module IS NOT NULL +GROUP BY module, stage +ORDER BY avg_memory DESC +LIMIT 10; +``` + +## Memory Leak Detection + +### Pattern: Monotonic Growth + +```sql +WITH memory_trend AS ( + SELECT + step, + max(allocated) as peak_memory, + LAG(max(allocated)) OVER (ORDER BY step) as prev_peak + FROM python.torch_trace + GROUP BY step +) +SELECT step, peak_memory, prev_peak, + peak_memory - prev_peak as growth +FROM memory_trend +WHERE peak_memory > prev_peak +ORDER BY step; +``` + +### Force Garbage Collection + +```bash +probing $ENDPOINT eval " +import gc +import torch + +# Force garbage collection +gc.collect() + +# Clear CUDA cache +if torch.cuda.is_available(): + torch.cuda.empty_cache() + print('CUDA cache cleared') + +print(f'Garbage collected: {gc.get_count()}') +" +``` + +## GPU Memory Analysis + +### Current GPU State + +```bash +probing $ENDPOINT eval " +import torch + +if torch.cuda.is_available(): + for i in range(torch.cuda.device_count()): + props = torch.cuda.get_device_properties(i) + allocated = torch.cuda.memory_allocated(i) / 1024**3 + reserved = torch.cuda.memory_reserved(i) / 1024**3 + total = props.total_memory / 1024**3 + + print(f'GPU {i}: {props.name}') + print(f' Total: {total:.2f} GB') + print(f' Allocated: {allocated:.2f} GB') + print(f' Reserved: {reserved:.2f} GB') + print(f' Free: {total - reserved:.2f} GB') +" +``` + +### Memory by Operation Stage + +```sql +SELECT + stage, + avg(allocated) as avg_allocated, + avg(max_allocated) as avg_peak, + avg(cached) as avg_cached +FROM python.torch_trace +WHERE step = (SELECT max(step) FROM python.torch_trace) +GROUP BY stage +ORDER BY avg_peak DESC; +``` + +## Best Practices + +### 1. Regular Memory Snapshots + +Take periodic snapshots during training: + +```bash +# Add to training loop +probing $ENDPOINT eval " +import torch +step = ... # current step +if step % 100 == 0: + allocated = torch.cuda.memory_allocated() / 1024**3 + print(f'Step {step}: {allocated:.2f} GB') +" +``` + +### 2. Profile Memory-Intensive Operations + +```sql +-- Find operations with highest memory variance +SELECT + module, + stage, + stddev(allocated) as memory_variance, + max(allocated) - min(allocated) as memory_range +FROM python.torch_trace +GROUP BY module, stage +HAVING stddev(allocated) > 10 +ORDER BY memory_variance DESC; +``` + +### 3. Monitor Memory Fragmentation + +```bash +probing $ENDPOINT eval " +import torch +if torch.cuda.is_available(): + stats = torch.cuda.memory_stats() + print(f'Allocated blocks: {stats.get(\"allocated_bytes.all.current\", 0) / 1024**3:.2f} GB') + print(f'Reserved blocks: {stats.get(\"reserved_bytes.all.current\", 0) / 1024**3:.2f} GB') +" +``` + +## Troubleshooting + +### Out of Memory (OOM) + +1. Check current memory state +2. Identify memory-heavy modules +3. Force garbage collection +4. Reduce batch size or model size + +### Memory Not Released + +1. Check for circular references +2. Verify tensors are not held in lists/dicts +3. Use `del` explicitly for large tensors +4. Call `torch.cuda.empty_cache()` + +## Next Steps + +- [SQL Analytics](sql-analytics.md) - More query patterns +- [Debugging](debugging.md) - Stack analysis techniques +- [Troubleshooting](troubleshooting.md) - Common issues diff --git a/docs/src/guide/memory-analysis.zh.md b/docs/src/guide/memory-analysis.zh.md new file mode 100644 index 00000000..8b0d4e14 --- /dev/null +++ b/docs/src/guide/memory-analysis.zh.md @@ -0,0 +1,190 @@ +# 内存分析 + +Probing 提供全面的工具用于分析 AI 应用中的内存使用。 + +## 概览 + +内存问题在 AI 工作负载中很常见，特别是在训练期间。Probing 帮助您： + +- 追踪 GPU 和 CPU 内存分配 +- 检测内存泄漏 +- 分析内存使用模式 +- 优化内存效率 + +## 快速内存检查 + +```bash +# 获取当前内存状态 +probing $ENDPOINT eval " +import torch +import psutil + +proc = psutil.Process() +print(f'CPU 内存: {proc.memory_info().rss / 1024**3:.2f} GB') + +if torch.cuda.is_available(): + print(f'GPU 已分配: {torch.cuda.memory_allocated() / 1024**3:.2f} GB') + print(f'GPU 缓存: {torch.cuda.memory_reserved() / 1024**3:.2f} GB') +" +``` + +## 内存使用趋势 + +### 追踪训练步骤的内存变化 + +```sql +SELECT + step, + avg(allocated) as avg_memory_mb, + max(allocated) as peak_memory_mb, + min(allocated) as min_memory_mb +FROM python.torch_trace +WHERE step IS NOT NULL +GROUP BY step +ORDER BY step; +``` + +### 检测内存增长 + +```sql +SELECT + step, + max(allocated) - min(allocated) as memory_growth_mb +FROM python.torch_trace +WHERE step > (SELECT max(step) - 10 FROM python.torch_trace) +GROUP BY step +HAVING max(allocated) - min(allocated) > 50 +ORDER BY step; +``` + +## 按模块分析内存 + +识别哪些模型组件使用最多内存： + +```sql +SELECT + module, + stage, + avg(allocated) as avg_memory, + count(*) as execution_count +FROM python.torch_trace +WHERE module IS NOT NULL +GROUP BY module, stage +ORDER BY avg_memory DESC +LIMIT 10; +``` + +## 内存泄漏检测 + +### 模式：单调增长 + +```sql +WITH memory_trend AS ( + SELECT + step, + max(allocated) as peak_memory, + LAG(max(allocated)) OVER (ORDER BY step) as prev_peak + FROM python.torch_trace + GROUP BY step +) +SELECT step, peak_memory, prev_peak, + peak_memory - prev_peak as growth +FROM memory_trend +WHERE peak_memory > prev_peak +ORDER BY step; +``` + +### 强制垃圾回收 + +```bash +probing $ENDPOINT eval " +import gc +import torch + +# 强制垃圾回收 +gc.collect() + +# 清理 CUDA 缓存 +if torch.cuda.is_available(): + torch.cuda.empty_cache() + print('CUDA 缓存已清理') + +print(f'垃圾回收完成: {gc.get_count()}') +" +``` + +## GPU 内存分析 + +### 当前 GPU 状态 + +```bash +probing $ENDPOINT eval " +import torch + +if torch.cuda.is_available(): + for i in range(torch.cuda.device_count()): + props = torch.cuda.get_device_properties(i) + allocated = torch.cuda.memory_allocated(i) / 1024**3 + reserved = torch.cuda.memory_reserved(i) / 1024**3 + total = props.total_memory / 1024**3 + + print(f'GPU {i}: {props.name}') + print(f' 总计: {total:.2f} GB') + print(f' 已分配: {allocated:.2f} GB') + print(f' 已保留: {reserved:.2f} GB') + print(f' 空闲: {total - reserved:.2f} GB') +" +``` + +## 最佳实践 + +### 1. 定期内存快照 + +在训练过程中定期拍摄快照： + +```bash +probing $ENDPOINT eval " +import torch +step = trainer.current_step +if step % 100 == 0: + allocated = torch.cuda.memory_allocated() / 1024**3 + print(f'步骤 {step}: {allocated:.2f} GB') +" +``` + +### 2. 分析内存密集型操作 + +```sql +-- 查找内存波动最大的操作 +SELECT + module, + stage, + stddev(allocated) as memory_variance, + max(allocated) - min(allocated) as memory_range +FROM python.torch_trace +GROUP BY module, stage +HAVING stddev(allocated) > 10 +ORDER BY memory_variance DESC; +``` + +## 故障排除 + +### 内存不足 (OOM) + +1. 检查当前内存状态 +2. 识别内存密集型模块 +3. 强制垃圾回收 +4. 减小批次大小或模型大小 + +### 内存未释放 + +1. 检查循环引用 +2. 确认张量未被保存在列表/字典中 +3. 显式使用 `del` 删除大张量 +4. 调用 `torch.cuda.empty_cache()` + +## 下一步 + +- [SQL 分析](sql-analytics.zh.md) - 更多查询模式 +- [调试指南](debugging.zh.md) - 堆栈分析技术 +- [常见问题](troubleshooting.zh.md) - 常见问题解决 diff --git a/docs/src/guide/sql-analytics.md b/docs/src/guide/sql-analytics.md new file mode 100644 index 00000000..31ce6970 --- /dev/null +++ b/docs/src/guide/sql-analytics.md @@ -0,0 +1,171 @@ +# SQL Analytics Interface + +Probing provides a powerful SQL interface for analyzing performance and monitoring data. + +## Overview + +The SQL analytics interface transforms complex performance analysis into intuitive database queries. All monitoring data is accessible through standard SQL operations including `SELECT`, `WHERE`, `GROUP BY`, `ORDER BY`, and advanced analytical functions. + +## Basic Query Structure + +```bash +probing $ENDPOINT query "SELECT columns FROM table WHERE conditions" +``` + +## Core Tables + +### Configuration and Metadata + +**`information_schema.df_settings`** - System configuration and settings + +```sql +SELECT * FROM information_schema.df_settings +WHERE name LIKE 'probing.%'; +``` + +### Python Namespace Tables + +**`python.backtrace`** - Stack trace information + +```sql +SELECT * FROM python.backtrace LIMIT 10; +``` + +Common columns: + +- `ip` - Instruction pointer (for native frames) +- `file` - Source file name +- `func` - Function name +- `lineno` - Line number +- `depth` - Stack depth +- `frame_type` - Frame type ('Python' or 'Native') + +## PyTorch Integration + +When monitoring PyTorch applications, additional tables become available: + +**`python.torch_trace`** - PyTorch execution traces + +```sql +SELECT step, module, stage, duration, allocated +FROM python.torch_trace +WHERE step >= 5 +ORDER BY step DESC, seq; +``` + +Common columns: + +- `step` - Training step number +- `seq` - Sequence number within step +- `module` - Module name +- `stage` - Execution stage (forward, backward, step) +- `allocated` - GPU memory allocated (MB) +- `duration` - Execution duration (seconds) + +## Advanced Analytics + +### Time-Series Analysis + +**Memory growth over time:** + +```sql +SELECT + step, + stage, + avg(allocated) as avg_memory_mb, + max(allocated) as peak_memory_mb +FROM python.torch_trace +WHERE step > (SELECT max(step) - 10 FROM python.torch_trace) +GROUP BY step, stage +ORDER BY step, stage; +``` + +**Rolling averages:** + +```sql +SELECT + step, + module, + duration, + AVG(duration) OVER ( + PARTITION BY module + ORDER BY step, seq + ROWS BETWEEN 4 PRECEDING AND CURRENT ROW + ) as avg_duration_5_samples +FROM python.torch_trace +WHERE step > (SELECT max(step) - 5 FROM python.torch_trace); +``` + +### Performance Analysis + +**Top slowest operations:** + +```sql +SELECT + module, + stage, + count(*) as execution_count, + avg(duration) as avg_duration, + max(duration) as max_duration +FROM python.torch_trace +WHERE step > (SELECT max(step) - 10 FROM python.torch_trace) + AND duration > 0 +GROUP BY module, stage +ORDER BY avg_duration DESC +LIMIT 10; +``` + +## Aggregation Functions + +### Statistical Functions + +```sql +SELECT + module, + stage, + count(*) as total_executions, + avg(duration) as mean_duration, + percentile_cont(0.5) WITHIN GROUP (ORDER BY duration) as median_duration, + percentile_cont(0.95) WITHIN GROUP (ORDER BY duration) as p95_duration, + min(duration) as min_duration, + max(duration) as max_duration +FROM python.torch_trace +WHERE duration > 0 +GROUP BY module, stage; +``` + +### Window Functions + +```sql +SELECT + step, + allocated, + LAG(allocated) OVER (ORDER BY step, seq) as prev_memory, + LEAD(allocated) OVER (ORDER BY step, seq) as next_memory, + ROW_NUMBER() OVER (ORDER BY allocated DESC) as memory_rank +FROM python.torch_trace +WHERE step > (SELECT max(step) - 5 FROM python.torch_trace); +``` + +## Data Export + +Results can be exported for further analysis: + +```bash +# Export to JSON +probing $ENDPOINT query "SELECT * FROM python.torch_trace" > torch_traces.json + +# Time-series data for plotting +probing $ENDPOINT query " + SELECT step, stage, avg(duration), avg(allocated) + FROM python.torch_trace + GROUP BY step, stage +" > training_metrics.json +``` + +## Best Practices + +1. **Use step-based filtering** - Always include step constraints for better performance +2. **Limit result sets** - Use `LIMIT` clauses for large datasets +3. **Aggregate appropriately** - Use `GROUP BY` for summary statistics +4. **Test queries incrementally** - Start simple and add complexity gradually diff --git a/docs/src/guide/sql-analytics.zh.md b/docs/src/guide/sql-analytics.zh.md new file mode 100644 index 00000000..01332ffa --- /dev/null +++ b/docs/src/guide/sql-analytics.zh.md @@ -0,0 +1,171 @@ +# SQL 分析接口 + +Probing 提供强大的 SQL 接口用于分析性能和监控数据。 + +## 概览 + +SQL 分析接口将复杂的性能分析转化为直观的数据库查询。所有监控数据都可以通过标准 SQL 操作访问，包括 `SELECT`、`WHERE`、`GROUP BY`、`ORDER BY` 和高级分析函数。 + +## 基本查询结构 + +```bash +probing $ENDPOINT query "SELECT columns FROM table WHERE conditions" +``` + +## 核心表 + +### 配置和元数据 + +**`information_schema.df_settings`** - 系统配置和设置 + +```sql +SELECT * FROM information_schema.df_settings +WHERE name LIKE 'probing.%'; +``` + +### Python 命名空间表 + +**`python.backtrace`** - 堆栈跟踪信息 + +```sql +SELECT * FROM python.backtrace LIMIT 10; +``` + +常用列： + +- `ip` - 指令指针（用于原生帧） +- `file` - 源文件名 +- `func` - 函数名 +- `lineno` - 行号 +- `depth` - 堆栈深度 +- `frame_type` - 帧类型（'Python' 或 'Native'） + +## PyTorch 集成 + +监控 PyTorch 应用时，可用额外的表： + +**`python.torch_trace`** - PyTorch 执行跟踪 + +```sql +SELECT step, module, stage, duration, allocated +FROM python.torch_trace +WHERE step >= 5 +ORDER BY step DESC, seq; +``` + +常用列： + +- `step` - 训练步数 +- `seq` - 步内序号 +- `module` - 模块名 +- `stage` - 执行阶段（forward、backward、step） +- `allocated` - GPU 已分配内存（MB） +- `duration` - 执行时长（秒） + +## 高级分析 + +### 时间序列分析 + +**内存随时间增长：** + +```sql +SELECT + step, + stage, + avg(allocated) as avg_memory_mb, + max(allocated) as peak_memory_mb +FROM python.torch_trace +WHERE step > (SELECT max(step) - 10 FROM python.torch_trace) +GROUP BY step, stage +ORDER BY step, stage; +``` + +**滚动平均：** + +```sql +SELECT + step, + module, + duration, + AVG(duration) OVER ( + PARTITION BY module + ORDER BY step, seq + ROWS BETWEEN 4 PRECEDING AND CURRENT ROW + ) as avg_duration_5_samples +FROM python.torch_trace +WHERE step > (SELECT max(step) - 5 FROM python.torch_trace); +``` + +### 性能分析 + +**最慢操作排名：** + +```sql +SELECT + module, + stage, + count(*) as execution_count, + avg(duration) as avg_duration, + max(duration) as max_duration +FROM python.torch_trace +WHERE step > (SELECT max(step) - 10 FROM python.torch_trace) + AND duration > 0 +GROUP BY module, stage +ORDER BY avg_duration DESC +LIMIT 10; +``` + +## 聚合函数 + +### 统计函数 + +```sql +SELECT + module, + stage, + count(*) as total_executions, + avg(duration) as mean_duration, + percentile_cont(0.5) WITHIN GROUP (ORDER BY duration) as median_duration, + percentile_cont(0.95) WITHIN GROUP (ORDER BY duration) as p95_duration, + min(duration) as min_duration, + max(duration) as max_duration +FROM python.torch_trace +WHERE duration > 0 +GROUP BY module, stage; +``` + +### 窗口函数 + +```sql +SELECT + step, + allocated, + LAG(allocated) OVER (ORDER BY step, seq) as prev_memory, + LEAD(allocated) OVER (ORDER BY step, seq) as next_memory, + ROW_NUMBER() OVER (ORDER BY allocated DESC) as memory_rank +FROM python.torch_trace +WHERE step > (SELECT max(step) - 5 FROM python.torch_trace); +``` + +## 数据导出 + +结果可以导出用于进一步分析： + +```bash +# 导出为 JSON +probing $ENDPOINT query "SELECT * FROM python.torch_trace" > torch_traces.json + +# 时间序列数据用于绘图 +probing $ENDPOINT query " + SELECT step, stage, avg(duration), avg(allocated) + FROM python.torch_trace + GROUP BY step, stage +" > training_metrics.json +``` + +## 最佳实践 + +1. **使用基于步数的过滤** - 始终包含步数约束以获得更好的性能 +2. **限制结果集** - 对大数据集使用 `LIMIT` 子句 +3. **适当聚合** - 使用 `GROUP BY` 获取汇总统计 +4. **渐进式测试查询** - 从简单开始，逐步增加复杂度 diff --git a/docs/src/guide/troubleshooting.md b/docs/src/guide/troubleshooting.md new file mode 100644 index 00000000..3c06ff84 --- /dev/null +++ b/docs/src/guide/troubleshooting.md @@ -0,0 +1,272 @@ +# Troubleshooting + +Common issues and their solutions when using Probing. + +## Connection Issues + +### Cannot Connect to Process + +**Symptom**: `probing $ENDPOINT inject` fails or times out. + +**Solutions**: + +1. **Verify process exists**: + ```bash + ps aux | grep $ENDPOINT + ``` + +2. **Check Linux requirement**: + Injection only works on Linux. On other platforms, start your process with: + ```bash + PROBING=1 python your_script.py + ``` + +3. **Check permissions**: + ```bash + # May need sudo for injection + sudo probing $ENDPOINT inject + ``` + +### Connection Refused (Remote) + +**Symptom**: Cannot connect to remote process. + +**Solutions**: + +1. **Verify server is running**: + ```bash + # On remote machine + netstat -tlnp | grep $PORT + ``` + +2. **Check firewall**: + ```bash + # Allow port + sudo ufw allow $PORT + ``` + +3. **Verify endpoint format**: + ```bash + export ENDPOINT=hostname:port # Not just hostname + ``` + +## Query Issues + +### Table Not Found + +**Symptom**: `Table 'python.torch_trace' not found` + +**Solutions**: + +1. **Check if PyTorch profiling is enabled**: + ```bash + probing $ENDPOINT eval " + import probing + print(probing.get_config())" + ``` + +2. **Enable PyTorch tracing**: + ```bash + PROBING_TORCH_PROFILING=on python your_script.py + ``` + +3. **Wait for data collection**: + Tables are populated as operations occur. Run some training steps first. + +### Empty Results + +**Symptom**: Query returns no rows. + +**Solutions**: + +1. **Check table contents**: + ```sql + SELECT COUNT(*) FROM python.torch_trace; + ``` + +2. **Verify filter conditions**: + ```sql + -- Remove filters to debug + SELECT * FROM python.torch_trace LIMIT 5; + ``` + +3. **Check step range**: + ```sql + SELECT MIN(step), MAX(step) FROM python.torch_trace; + ``` + +## Eval Issues + +### Code Execution Fails + +**Symptom**: `probing eval` returns error or unexpected result. + +**Solutions**: + +1. **Check syntax**: + ```bash + # Use proper quoting + probing $ENDPOINT eval "print('hello')" + ``` + +2. **Handle imports**: + ```bash + # Import modules first + probing $ENDPOINT eval "import torch; print(torch.__version__)" + ``` + +3. **Check variable scope**: + ```bash + # Use globals() to see available variables + probing $ENDPOINT eval "print(list(globals().keys())[:10])" + ``` + +### Import Errors + +**Symptom**: `ModuleNotFoundError` in eval. + +**Solutions**: + +1. **Check if module is loaded**: + ```bash + probing $ENDPOINT eval "import sys; print('torch' in sys.modules)" + ``` + +2. **Use try-except**: + ```bash + probing $ENDPOINT eval " + try: + import torch + print(torch.__version__) + except ImportError: + print('torch not available')" + ``` + +## Performance Issues + +### High Overhead + +**Symptom**: Application runs slower with Probing. + +**Solutions**: + +1. **Reduce sampling rate**: + ```bash + probing $ENDPOINT config probing.sample_rate=0.01 + ``` + +2. **Disable unused features**: + ```bash + PROBING_TORCH_PROFILING=off python your_script.py + ``` + +3. **Use targeted profiling**: + Only enable profiling for specific modules or operations. + +### Query Timeout + +**Symptom**: SQL queries take too long. + +**Solutions**: + +1. **Add LIMIT clause**: + ```sql + SELECT * FROM python.torch_trace LIMIT 100; + ``` + +2. **Use step filtering**: + ```sql + WHERE step > (SELECT MAX(step) - 10 FROM python.torch_trace) + ``` + +3. **Aggregate data**: + ```sql + SELECT step, AVG(duration) FROM python.torch_trace GROUP BY step; + ``` + +## Data Issues + +### Missing Data + +**Symptom**: Expected data not appearing in tables. + +**Solutions**: + +1. **Verify profiling is active**: + ```bash + probing $ENDPOINT eval " + import probing + print(probing.is_profiling_active())" + ``` + +2. **Check data retention**: + ```bash + probing $ENDPOINT config | grep retention + ``` + +3. **Force data flush**: + ```bash + probing $ENDPOINT eval " + import probing + probing.flush()" + ``` + +### Incorrect Values + +**Symptom**: Data values seem wrong. + +**Solutions**: + +1. **Verify units**: + - Memory is typically in MB + - Duration is in seconds + +2. **Check for aggregation**: + ```sql + -- Sum vs individual values + SELECT SUM(allocated) vs SELECT allocated + ``` + +3. **Validate manually**: + ```bash + probing $ENDPOINT eval " + import torch + print(torch.cuda.memory_allocated() / 1024**2)" # MB + ``` + +## Platform-Specific Issues + +### Linux + +- **ptrace errors**: May need `CAP_SYS_PTRACE` capability +- **SELinux**: May need to adjust policies + +### macOS + +- **Injection not supported**: Use `PROBING=1` at startup +- **SIP restrictions**: May affect some features + +### Windows + +- **Limited support**: Only query/eval with pre-enabled processes + +## Getting Help + +If you're still stuck: + +1. **Check logs**: + ```bash + probing $ENDPOINT eval " + import logging + logging.basicConfig(level=logging.DEBUG)" + ``` + +2. **Report issue**: + [GitHub Issues](https://github.com/DeepLink-org/probing/issues) + +3. **Include diagnostics**: + ```bash + probing --version + python --version + uname -a + ``` diff --git a/docs/src/guide/troubleshooting.zh.md b/docs/src/guide/troubleshooting.zh.md new file mode 100644 index 00000000..285ab300 --- /dev/null +++ b/docs/src/guide/troubleshooting.zh.md @@ -0,0 +1,185 @@ +# 常见问题 + +使用 Probing 时的常见问题及解决方案。 + +## 连接问题 + +### 无法连接到进程 + +**症状**：`probing $ENDPOINT inject` 失败或超时。 + +**解决方案**： + +1. **验证进程存在**： + ```bash + ps aux | grep $ENDPOINT + ``` + +2. **检查 Linux 要求**： + 注入功能仅在 Linux 上可用。在其他平台上，请在启动时启用： + ```bash + PROBING=1 python your_script.py + ``` + +3. **检查权限**： + ```bash + # 可能需要 sudo 进行注入 + sudo probing $ENDPOINT inject + ``` + +### 连接被拒绝（远程） + +**症状**：无法连接到远程进程。 + +**解决方案**： + +1. **验证服务器正在运行**： + ```bash + # 在远程机器上 + netstat -tlnp | grep $PORT + ``` + +2. **检查防火墙**： + ```bash + # 允许端口 + sudo ufw allow $PORT + ``` + +3. **验证端点格式**： + ```bash + export ENDPOINT=hostname:port # 不只是 hostname + ``` + +## 查询问题 + +### 表不存在 + +**症状**：`Table 'python.torch_trace' not found` + +**解决方案**： + +1. **检查 PyTorch 分析是否启用**： + ```bash + probing $ENDPOINT eval " + import probing + print(probing.get_config())" + ``` + +2. **启用 PyTorch 追踪**： + ```bash + PROBING_TORCH_PROFILING=on python your_script.py + ``` + +3. **等待数据收集**： + 表在操作发生时填充。先运行一些训练步骤。 + +### 结果为空 + +**症状**：查询没有返回行。 + +**解决方案**： + +1. **检查表内容**： + ```sql + SELECT COUNT(*) FROM python.torch_trace; + ``` + +2. **验证过滤条件**： + ```sql + -- 移除过滤器来调试 + SELECT * FROM python.torch_trace LIMIT 5; + ``` + +3. **检查步骤范围**： + ```sql + SELECT MIN(step), MAX(step) FROM python.torch_trace; + ``` + +## Eval 问题 + +### 代码执行失败 + +**症状**：`probing eval` 返回错误或意外结果。 + +**解决方案**： + +1. **检查语法**： + ```bash + # 使用正确的引号 + probing $ENDPOINT eval "print('hello')" + ``` + +2. **处理导入**： + ```bash + # 先导入模块 + probing $ENDPOINT eval "import torch; print(torch.__version__)" + ``` + +3. **检查变量作用域**： + ```bash + # 使用 globals() 查看可用变量 + probing $ENDPOINT eval "print(list(globals().keys())[:10])" + ``` + +## 性能问题 + +### 开销过高 + +**症状**：启用 Probing 后应用运行变慢。 + +**解决方案**： + +1. **降低采样率**： + ```bash + probing $ENDPOINT config probing.sample_rate=0.01 + ``` + +2. **禁用未使用的功能**： + ```bash + PROBING_TORCH_PROFILING=off python your_script.py + ``` + +3. **使用针对性分析**： + 只为特定模块或操作启用分析。 + +### 查询超时 + +**症状**：SQL 查询耗时太长。 + +**解决方案**： + +1. **添加 LIMIT 子句**： + ```sql + SELECT * FROM python.torch_trace LIMIT 100; + ``` + +2. **使用步骤过滤**： + ```sql + WHERE step > (SELECT MAX(step) - 10 FROM python.torch_trace) + ``` + +3. **聚合数据**： + ```sql + SELECT step, AVG(duration) FROM python.torch_trace GROUP BY step; + ``` + +## 获取帮助 + +如果仍有问题： + +1. **检查日志**： + ```bash + probing $ENDPOINT eval " + import logging + logging.basicConfig(level=logging.DEBUG)" + ``` + +2. **报告问题**： + [GitHub Issues](https://github.com/DeepLink-org/probing/issues) + +3. **包含诊断信息**： + ```bash + probing --version + python --version + uname -a + ``` diff --git a/docs/src/index.md b/docs/src/index.md new file mode 100644 index 00000000..f5dc49d1 --- /dev/null +++ b/docs/src/index.md @@ -0,0 +1,46 @@ +--- +template: home.html +title: Probing - Dynamic Performance Profiler for Distributed AI +description: A production-grade performance profiler designed specifically for distributed AI workloads. Zero-intrusion, SQL-powered analytics, real-time introspection. +hide: toc +--- + + + +# Probing + +**Probing** is a dynamic performance profiler for distributed AI applications. + +## Key Features + +- **Zero Intrusion** - Attach to running processes without code changes +- **SQL Analytics** - Query performance data with standard SQL +- **Live Execution** - Run Python code in target processes +- **Stack Analysis** - Capture call stacks with variable values +- **Distributed Ready** - Monitor processes across multiple nodes + +## Quick Start + +```bash +# Install +pip install probing + +# Inject into running process +probing -t inject + +# Query performance data +probing -t query "SELECT * FROM python.torch_trace LIMIT 10" +``` + +## Use Cases + +- **Training Debugging** - Debug training instabilities and hangs +- **Memory Analysis** - Track GPU/CPU memory usage +- **Performance Profiling** - Identify bottlenecks in model execution +- **Production Monitoring** - Monitor AI services without restarts + +## Community + +- [GitHub Repository](https://github.com/DeepLink-org/probing) +- [Issue Tracker](https://github.com/DeepLink-org/probing/issues) +- [PyPI Package](https://pypi.org/project/probing/) diff --git a/docs/src/index.zh.md b/docs/src/index.zh.md new file mode 100644 index 00000000..facff997 --- /dev/null +++ b/docs/src/index.zh.md @@ -0,0 +1,46 @@ +--- +template: home.html +title: Probing - 分布式 AI 动态性能分析器 +description: 专为分布式 AI 工作负载设计的生产级性能分析器。零侵入、SQL 驱动分析、实时内省。 +hide: toc +--- + + + +# Probing + +**Probing** 是一个面向分布式 AI 应用的动态性能分析器。 + +## 核心特性 + +- **零侵入** - 无需修改代码即可附加到运行中的进程 +- **SQL 分析** - 使用标准 SQL 查询性能数据 +- **实时执行** - 在目标进程中运行 Python 代码 +- **堆栈分析** - 捕获带有变量值的调用栈 +- **分布式支持** - 监控跨多节点的进程 + +## 快速开始 + +```bash +# 安装 +pip install probing + +# 注入到运行中的进程 +probing -t inject + +# 查询性能数据 +probing -t query "SELECT * FROM python.torch_trace LIMIT 10" +``` + +## 使用场景 + +- **训练调试** - 调试训练不稳定和卡住问题 +- **内存分析** - 追踪 GPU/CPU 内存使用 +- **性能分析** - 识别模型执行中的瓶颈 +- **生产监控** - 无需重启即可监控 AI 服务 + +## 社区 + +- [GitHub 仓库](https://github.com/DeepLink-org/probing) +- [问题追踪](https://github.com/DeepLink-org/probing/issues) +- [PyPI 包](https://pypi.org/project/probing/) diff --git a/docs/getting-started/installation.md b/docs/src/installation.md similarity index 62% rename from docs/getting-started/installation.md rename to docs/src/installation.md index 774c1b72..62a3a9d9 100644 --- a/docs/getting-started/installation.md +++ b/docs/src/installation.md @@ -14,8 +14,6 @@ Before you begin, ensure you have the following: ## Installation Methods -You can install Probing using one of the following methods: - ### 1. Using Pip (Recommended) This is the easiest way to install Probing: @@ -32,7 +30,7 @@ If you want the latest development version or want to contribute to Probing, you ```bash # 1. Clone the repository -git clone https://github.com/reiase/probing.git +git clone https://github.com/DeepLink-org/probing.git cd probing # 2. Build and install the Python package @@ -42,7 +40,7 @@ pip install dist/probing-*.whl This will compile the Rust components and build the Python wheel for installation. -For detailed instructions on building from source, including prerequisites and troubleshooting, see the [Building from Source](../development/building.md) guide. +For detailed instructions on building from source, including prerequisites and troubleshooting, see the [Building from Source](design/architecture.md) guide. ## Verifying the Installation @@ -59,11 +57,28 @@ probing 0.2.3 ``` You can also check if the `probing` command is available: + ```bash probing list ``` + This command should list available probing commands or indicate that no processes are currently being probed. +## Platform Support + +| Platform | Injection | Query/Eval | +|----------|-----------|------------| +| Linux | ✅ Full support | ✅ Full support | +| macOS | ❌ Not supported | ✅ Supported | +| Windows | ❌ Not supported | ✅ Supported | + +!!! note "Linux Required for Injection" + The dynamic probe injection feature (`probing inject`) requires Linux. On other platforms, you can still use query and eval features if the target process has probing enabled at startup. + ## Next Steps -With Probing installed, you are ready to start using it. Head back to the [Introduction](introduction.md) to learn about its core capabilities and how to get started with your first analysis. +With Probing installed, you are ready to start using it: + +- [Quick Start](quickstart.md) - Get started with your first analysis +- [SQL Analytics](guide/sql-analytics.md) - Learn the SQL query interface +- [Memory Analysis](guide/memory-analysis.md) - Debug memory issues diff --git a/docs/src/installation.zh.md b/docs/src/installation.zh.md new file mode 100644 index 00000000..825bdeeb --- /dev/null +++ b/docs/src/installation.zh.md @@ -0,0 +1,82 @@ +# 安装指南 + +本指南介绍如何在您的系统上安装 Probing。 + +## 环境要求 + +在开始之前，请确保您的系统满足以下要求： + +- Python（3.7 或更高版本） +- Pip（Python 包安装器） +- 如需从源码构建： + - Rust（推荐最新稳定版） + - Cargo（Rust 的包管理器和构建系统） + +## 安装方式 + +### 1. 使用 Pip（推荐） + +这是安装 Probing 最简单的方式： + +```bash +pip install probing +``` + +此命令将从 Python Package Index (PyPI) 下载并安装 Probing 的最新稳定版本。 + +### 2. 从源码构建 + +如果您需要最新的开发版本或想要贡献代码，可以从源码构建： + +```bash +# 1. 克隆仓库 +git clone https://github.com/DeepLink-org/probing.git +cd probing + +# 2. 构建并安装 Python 包 +make wheel +pip install dist/probing-*.whl +``` + +这将编译 Rust 组件并构建用于安装的 Python wheel 包。 + +## 验证安装 + +安装完成后，可以通过以下命令验证 Probing 是否正确安装： + +```bash +probing --version +``` + +应该会输出已安装的 Probing 版本，例如： + +``` +probing 0.2.3 +``` + +您也可以检查 `probing` 命令是否可用： + +```bash +probing list +``` + +此命令应该会列出可用的 probing 命令或显示当前没有进程正在被探测。 + +## 平台支持 + +| 平台 | 注入功能 | 查询/执行 | +|------|----------|-----------| +| Linux | ✅ 完全支持 | ✅ 完全支持 | +| macOS | ❌ 不支持 | ✅ 支持 | +| Windows | ❌ 不支持 | ✅ 支持 | + +!!! note "注入功能需要 Linux" + 动态探针注入功能（`probing inject`）需要 Linux 系统。在其他平台上，如果目标进程在启动时启用了 probing，您仍然可以使用查询和执行功能。 + +## 下一步 + +安装完成后，您可以开始使用 Probing： + +- [快速开始](quickstart.zh.md) - 开始您的第一次分析 +- [SQL 分析](guide/sql-analytics.zh.md) - 学习 SQL 查询接口 +- [内存分析](guide/memory-analysis.zh.md) - 调试内存问题 diff --git a/docs/src/quickstart.md b/docs/src/quickstart.md new file mode 100644 index 00000000..5a6a7e7f --- /dev/null +++ b/docs/src/quickstart.md @@ -0,0 +1,147 @@ +# Quick Start + +Get immediate value from Probing with this streamlined workflow. + +## Your First 5 Minutes + +### Step 1: Set Your Target Process + +All Probing commands need a target endpoint. Set `$ENDPOINT` to either a local process ID or remote address: + +```bash +# Local process - find and set your Python process ID +export ENDPOINT=$(pgrep -f "python.*your_script") + +# Or for remote processes +export ENDPOINT=remote-host:8080 +``` + +!!! tip "Finding Processes" + Use `ps aux | grep python` or `pgrep -f "python.*train"` to locate your target. + +### Step 2: Connect and Explore + +```bash +# Connect to your process (Linux only) +probing $ENDPOINT inject + +# Get basic process info +probing $ENDPOINT eval "import os, psutil; proc = psutil.Process(); print(f'PID: {os.getpid()}, Memory: {proc.memory_info().rss/1024**2:.1f}MB')" +``` + +### Step 3: Try All Three Core Capabilities + +#### 📊 Query structured data + +```bash +probing $ENDPOINT query "SELECT name, value FROM information_schema.df_settings LIMIT 5" +``` + +#### 🎯 Execute live code + +```bash +probing $ENDPOINT eval "import torch; print(f'CUDA: {torch.cuda.is_available()}')" +``` + +#### 🔍 Capture execution context + +```bash +probing $ENDPOINT backtrace + +probing $ENDPOINT query "SELECT func, file, lineno FROM python.backtrace ORDER BY depth LIMIT 5" +``` + +## Three Core Capabilities + +Probing provides three powerful capabilities that work together: + +### 🎯 eval: Execute Code in Live Processes + +Run arbitrary Python code directly inside your target process: + +```bash +# Check training threads +probing $ENDPOINT eval "import threading; [print(f'{t.name}: {t.is_alive()}') for t in threading.enumerate()]" + +# Check GPU memory usage +probing $ENDPOINT eval "import torch; print(f'GPU: {torch.cuda.memory_allocated()/1024**3:.1f}GB allocated')" +``` + +### 📊 query: Analyze Data with SQL + +Query structured performance data using familiar SQL syntax: + +```bash +probing $ENDPOINT query " +SELECT + step, + module, + SUM(allocated) as total_memory_mb, + COUNT(*) as operation_count +FROM python.torch_trace +WHERE step > 100 +GROUP BY step, module +ORDER BY total_memory_mb DESC +LIMIT 10" +``` + +### 🔍 backtrace: Debug with Stack Context + +Capture detailed call stacks with Python variable values: + +```bash +# Capture current call stack +probing $ENDPOINT backtrace + +# Query the stack trace +probing $ENDPOINT query "SELECT func, file, lineno FROM python.backtrace ORDER BY depth LIMIT 3" +``` + +## Real-World Debugging Scenarios + +### Scenario 1: Training Process Hanging + +**Problem**: PyTorch training suddenly stops progressing. + +```bash +# 1. See what main thread is doing +probing $ENDPOINT backtrace + +# 2. Check thread states +probing $ENDPOINT eval "import threading; [(t.name, t.is_alive()) for t in threading.enumerate()]" + +# 3. Analyze stack context +probing $ENDPOINT query "SELECT func, file, lineno FROM python.backtrace ORDER BY depth LIMIT 10" +``` + +### Scenario 2: Memory Leak Investigation + +**Problem**: Memory usage keeps growing during training. + +```bash +# Force cleanup and get current state +probing $ENDPOINT eval "import gc, torch; gc.collect(); torch.cuda.empty_cache()" + +# Analyze allocation trends +probing $ENDPOINT query "SELECT step, AVG(allocated) as avg_memory FROM python.torch_trace GROUP BY step ORDER BY step" +``` + +### Scenario 3: Performance Bottleneck Analysis + +**Problem**: Need to identify which model components are slowest. + +```bash +# Find most expensive operations +probing $ENDPOINT query " +SELECT module, stage, AVG(duration) as avg_duration +FROM python.torch_trace +GROUP BY module, stage +ORDER BY avg_duration DESC +LIMIT 10" +``` + +## Next Steps + +- [SQL Analytics](guide/sql-analytics.md) - Advanced query techniques +- [Memory Analysis](guide/memory-analysis.md) - Deep dive into memory debugging +- [Debugging Guide](guide/debugging.md) - Expert debugging patterns diff --git a/docs/src/quickstart.zh.md b/docs/src/quickstart.zh.md new file mode 100644 index 00000000..0f38caf5 --- /dev/null +++ b/docs/src/quickstart.zh.md @@ -0,0 +1,147 @@ +# 快速开始 + +通过这个精简的工作流程，快速获得 Probing 的价值。 + +## 5 分钟上手 + +### 步骤 1：设置目标进程 + +所有 Probing 命令都需要一个目标端点。将 `$ENDPOINT` 设置为本地进程 ID 或远程地址： + +```bash +# 本地进程 - 查找并设置 Python 进程 ID +export ENDPOINT=$(pgrep -f "python.*your_script") + +# 或者远程进程 +export ENDPOINT=remote-host:8080 +``` + +!!! tip "查找进程" + 使用 `ps aux | grep python` 或 `pgrep -f "python.*train"` 来定位目标进程。 + +### 步骤 2：连接并探索 + +```bash +# 连接到进程（仅 Linux） +probing $ENDPOINT inject + +# 获取基本进程信息 +probing $ENDPOINT eval "import os, psutil; proc = psutil.Process(); print(f'PID: {os.getpid()}, 内存: {proc.memory_info().rss/1024**2:.1f}MB')" +``` + +### 步骤 3：尝试三大核心功能 + +#### 📊 查询结构化数据 + +```bash +probing $ENDPOINT query "SELECT name, value FROM information_schema.df_settings LIMIT 5" +``` + +#### 🎯 执行实时代码 + +```bash +probing $ENDPOINT eval "import torch; print(f'CUDA: {torch.cuda.is_available()}')" +``` + +#### 🔍 捕获执行上下文 + +```bash +probing $ENDPOINT backtrace + +probing $ENDPOINT query "SELECT func, file, lineno FROM python.backtrace ORDER BY depth LIMIT 5" +``` + +## 三大核心能力 + +Probing 提供三个强大的能力，它们协同工作： + +### 🎯 eval：在运行中的进程执行代码 + +直接在目标进程中运行任意 Python 代码： + +```bash +# 检查训练线程 +probing $ENDPOINT eval "import threading; [print(f'{t.name}: {t.is_alive()}') for t in threading.enumerate()]" + +# 检查 GPU 内存使用 +probing $ENDPOINT eval "import torch; print(f'GPU: {torch.cuda.memory_allocated()/1024**3:.1f}GB 已分配')" +``` + +### 📊 query：用 SQL 分析数据 + +使用熟悉的 SQL 语法查询结构化性能数据： + +```bash +probing $ENDPOINT query " +SELECT + step, + module, + SUM(allocated) as total_memory_mb, + COUNT(*) as operation_count +FROM python.torch_trace +WHERE step > 100 +GROUP BY step, module +ORDER BY total_memory_mb DESC +LIMIT 10" +``` + +### 🔍 backtrace：带堆栈上下文的调试 + +捕获带有 Python 变量值的详细调用栈： + +```bash +# 捕获当前调用栈 +probing $ENDPOINT backtrace + +# 查询堆栈跟踪 +probing $ENDPOINT query "SELECT func, file, lineno FROM python.backtrace ORDER BY depth LIMIT 3" +``` + +## 真实调试场景 + +### 场景 1：训练进程卡住 + +**问题**：PyTorch 训练突然停止进展。 + +```bash +# 1. 查看主线程在做什么 +probing $ENDPOINT backtrace + +# 2. 检查线程状态 +probing $ENDPOINT eval "import threading; [(t.name, t.is_alive()) for t in threading.enumerate()]" + +# 3. 分析堆栈上下文 +probing $ENDPOINT query "SELECT func, file, lineno FROM python.backtrace ORDER BY depth LIMIT 10" +``` + +### 场景 2：内存泄漏排查 + +**问题**：训练过程中内存使用持续增长。 + +```bash +# 强制清理并获取当前状态 +probing $ENDPOINT eval "import gc, torch; gc.collect(); torch.cuda.empty_cache()" + +# 分析分配趋势 +probing $ENDPOINT query "SELECT step, AVG(allocated) as avg_memory FROM python.torch_trace GROUP BY step ORDER BY step" +``` + +### 场景 3：性能瓶颈分析 + +**问题**：需要找出哪些模型组件最慢。 + +```bash +# 查找最耗时的操作 +probing $ENDPOINT query " +SELECT module, stage, AVG(duration) as avg_duration +FROM python.torch_trace +GROUP BY module, stage +ORDER BY avg_duration DESC +LIMIT 10" +``` + +## 下一步 + +- [SQL 分析](guide/sql-analytics.zh.md) - 高级查询技巧 +- [内存分析](guide/memory-analysis.zh.md) - 深入内存调试 +- [调试指南](guide/debugging.zh.md) - 专家级调试模式 diff --git a/docs/src/user-guide/debugging.md b/docs/src/user-guide/debugging.md deleted file mode 100644 index 4c54c153..00000000 --- a/docs/src/user-guide/debugging.md +++ /dev/null @@ -1,414 +0,0 @@ - - -# 使用 Probing 进行代码调试 - -本文介绍如何通过 Probing 进行 Python 应用代码调试，关于 Probing 的整体架构设计，请参考[架构概览](../advanced/architecture.md)。 - -系统调试一直是分布式系统开发和优化的难题，尤其是异构分布式训练系统，需要综合定位从硬件、系统到框架、模型等多个层面的错误和问题。对于分布式系统的调试需求主要集中在几方面： - -1. 断点：断点是调试程序的最基本手段，通过断点可以用来观察程序状态、变量值，进而帮助BUG的分析与解决，断点又分为两类： - 1. 位置断点：用户指定在特定函数或者特定代码的特定位置中断程序执行，并进入调试器； - 2. 条件断点：用户指定某个变量或者内存地址，当发生变化时触发程序中断； -2. 插桩：通常是在目标代码位置插入日志，查看变量值或者系统状态； -3. 现场捕获：当出现异常时，第一时间捕获现场，或者触发断点，供进一步调试； - -## Debug方法论 - -### CPU Debug方法的实现 - -本节讨论CPU侧Debugger的实现方法，用于参考和借鉴。 - -#### 如何控制目标进程 - -当调试一个进程时，第一步是如何获得目标进程的控制权限，控制目标进程暂停或者恢复执行。这一步在Linux系统中主要通过ptrace系统调用实现，以下是ptrace系统调用的函数原型： -```C -#include - -long ptrace(enum __ptrace_request op, pid_t pid, - void *addr, void *data); -``` - -`ptrace` 提供了一种控制目标进程执行的方法，它可以让调试器与目标进程进行交互，从而实现调试功能。__ptrace_request常用的取值如下： - -- `PTRACE_ATTACH`: 附加到目标进程，使其成为当前进程的tracee； -- `PTRACE_INTERRUPT`: 暂停目标tracee； -- `PTRACE_CONT`: 让目标进程继续执行； -- `PTRACE_DETACH`: 释放目标tracee； -- `PTRACE_GETREGS/PTRACE_SETREGS`: 读写目标进程寄存器； -- `PTRACE_PEEKDATA/PTRACE_POKEDATA`： 读写目标进程内存，一次一个WORD； -- `/proc//mem`: 大块读写内存； - -常见的一个debugger的工作流程如下： -1. attach到目标进程； -2. 通过读写目标进程TEXT段插入断点； -3. 恢复目标进程执行，并用`waitpid`等待目标进程断点暂停； -4. 等到目标进程暂停，通过读写内存查看信息； - -#### CPU断点调试 - -##### 软件断点 - -X86处理器支持一个特殊的中断指令(INT 3, 0xCC)，当CPU执行到该指令时，会触发中断让调试器捕获。插入断点需要直接修改目标进程的代码段。 - -##### 硬件断点 - -X86处理器提供一系列调试寄存器(DR0-DR3)[^dr0_3]，可以在不修改内存的情况下，监视寄存器指向的地址。当目标地址被访问或者执行时，会触发CPU的中断，通知GDB。寄存器定义如下[^x86_regs]： - -- DR0-DR3：断点的虚拟地址 -- DR6：状态寄存器，存放DEBUG相关的状态指示位； -- DR7：控制寄存器，控制断点相关行为 - -### Python调试方法的实现 - -#### trace机制 - -Python 自身的调试器主要通过trace机制[^pytrace]实现: - -```python -sys.settrace(tracefunc) -``` - -`tracefunc`会从Python解释器接收5种事件： - -- call：执行函数调用； -- line：执行一行代码； -- return：函数返回； -- exception：发生一场； -- opcode：执行一条字节码；由于opcode的trace性能开销过大，需要设置`f_trace_opcodes`才能开启 - -借助`trace`机制，可以实现对Python变量的watch方法： - -```python -def trace(self, frame: FrameType, event: AnyStr, arg: Any): - if event != "line": return self.trace # 忽略line以外事件 - - for k, v in self.watch.items(): # 遍历watch列表 - if k in frame.f_locals and id(frame.f_locals[k]) != v: # 检测变量id变化 - print(f"variable update {k} = {frame.f_locals[k]}") - self.watch[k] = id(frame.f_locals[k]) - return self.trace # 继续跟踪, 每次需要返回一个新的trace函数 -``` - -#### 执行事件监控机制 - -自3.12起，Python引入了Execution event monitoring机制[^monitoring]，用于向各种工具提供Python解释器的内部执行事件。这个机制主要面向Debugger、Profiler和Optimizer开发者提供。我们先来看一个简单的例子，通过`sys.monitoring`来捕获Python执行过程中的异常： - -```python -import sys - -def hook(*args, **kwargs): # 定义事件hook - print("=== hook", args, kwargs) - -# 声明调试工具 -sys.monitoring.use_tool_id(sys.monitoring.DEBUGGER_ID, "debugging") - -# 启用异常的RAISE事件 -sys.monitoring.set_events(sys.monitoring.DEBUGGER_ID, sys.monitoring.events.RAISE) - -# 为异常事件注册钩子 -sys.monitoring.register_callback( - sys.monitoring.DEBUGGER_ID, - sys.monitoring.events.RAISE, - hook, -) - -# 测试代码 -def foo(a): - b = 2 - bar(a, b) - -def bar(a, b): - c = a+b - raise Exception('error') - -foo(1) -``` -在执行上述代码，会在`bar`函数触发异常时，打印如下消息： -``` -=== hook (, 32, Exception('error')) {} -``` - -##### 注册和使用tools - -monitoring中一个核心概念是 **tool**，用于区分不同的工具，避免彼此冲突。**tool**相关的API如下： -``` -sys.monitoring.use_tool_id(tool_id: int, name: str, /) → None¶ -声明使用**tool**，必须在使用前进行声明； - -sys.monitoring.free_tool_id(tool_id: int, /) → None¶ -释放**tool** - -sys.monitoring.get_tool(tool_id: int, /) → str | None¶ -获取**tool** -``` - -**tool**可以用任意整数ID来定，方便起见，系统预定义了几个tool的ID： -``` -sys.monitoring.DEBUGGER_ID = 0 -sys.monitoring.COVERAGE_ID = 1 -sys.monitoring.PROFILER_ID = 2 -sys.monitoring.OPTIMIZER_ID = 5 -``` - -##### 事件 - -定义了哪些执行阶段的事件会被发送给**tool**，目前支持的事件有 -- **BRANCH**(sys.monitoring.events.BRANCH) -- **CALL**: A call in Python code (event occurs before the call). -- **C_RAISE**: An exception raised from any callable, except for Python functions (event occurs after the exit). -- **C_RETURN**: Return from any callable, except for Python functions (event occurs after the return). -- **EXCEPTION_HANDLED**: An exception is handled. -- **INSTRUCTION**: A VM instruction is about to be executed. -- **JUMP**: An unconditional jump in the control flow graph is made. -- **LINE**: An instruction is about to be executed that has a different line number from the preceding instruction. -- **PY_RESUME**: Resumption of a Python function (for generator and coroutine functions), except for throw() calls. -- **PY_RETURN**: Return from a Python function (occurs immediately before the return, the callee's frame will be on the stack). -- **PY_START**: Start of a Python function (occurs immediately after the call, the callee's frame will be on the stack) -- **PY_THROW**: A Python function is resumed by a throw() call. -- **PY_UNWIND**: Exit from a Python function during exception unwinding. -- **PY_YIELD**: Yield from a Python function (occurs immediately before the yield, the callee's frame will be on the stack). -- **RAISE**: An exception is raised, except those that cause a STOP_ITERATION event. -- **RERAISE**: An exception is re-raised, for example at the end of a finally block. -- **STOP_ITERATION**: An artificial StopIteration is raised; see the STOP_ITERATION event. - -事件支持bool类型的操作，比如 `PY_RETURN | PY_START` 表示同时处理两种事件。 - -##### 注册回调函数 - -```python -sys.monitoring.register_callback(tool_id: int, event: int, func: Callable | None, /) → Callable | None -``` -用于注册回调函数，返回值为老的回调函数。如果需要取消回调函数的注册，可以将参数`func`设置为`None`。 - -##### 对特定函数使能Event - -除了全局使能event以外，还可以针对某些函数来使能event -```python -sys.monitoring.set_local_events(tool_id: int, code: CodeType, event_set: int, /) → None¶ -``` -这里`code`参数可以是某个函数。局部使能event可以有效缩小事件的影响范围，降低性能影响。 - -### Exception捕获 - -```python -sys.excepthook(type, value, traceback)¶ -``` - -通过注册`excepthook`，可以捕获Python进程的异常退出，捕获住未被捕获的异常，比如： -```python -import sys - -def handle(t, v, tb): - print("=== exception handler, ", t, v, tb) - -def foo(a): - b = 2 - bar(a, b) - -def bar(a, b): - raise Exception('error') - -sys.excepthook = handle - -foo(1) -``` - -`excepthook`可以捕获堆栈，但是无法捕获现场与局部变量。 - -## 分布式训练调试的挑战 - -### 典型场景 - -分布式异构训练通常面临如下场景： - -- 数据并行：多个节点同时处理不同的数据子集，并在参数服务器或环形通信中同步梯度。 -- 模型并行：将模型分为不同部分，分别运行在不同的节点上。 -- 异构训练：训练使用GPU这种异构计算设备，在每个节点上都与CPU异步执行。 -- 故障恢复：在分布式训练中，节点故障可能频繁发生，需要有效的故障定位与恢复机制。 - - -1. 分布式断点，观察每个进程的执行情况； -2. 分布式变量观测，观测每个进程的关键变量； -3. 分布式hook：关注关键变量或者tensor的取值与变化情况； -4. 分布式backtrace：观测每个进程的执行堆栈； - -### 主要挑战 - -1. 分布式断点调试： - - 多节点间存在角色差异(不如不同的TP/PP/DP角色) - - 集合通信场景下的断点会影响整体执行流 - - 传统断点调试工具难以适应分布式环境 - - 需要设计轻量级且语义感知的观测机制 - -2. 分布式变量观测： - - 变量获取与序列化开销大 - - 网络传输带宽受限 - - 数据聚合与展示复杂 - - 实时性与系统性能之间的权衡 - -3. 分布式hook与traceback - - 捕获关键动作或变量更新，特别是通信库中的同步原语与异步逻辑； - - 获取每个进程的执行堆栈，并汇总分析； - -4. 规模扩展性 - - 日志数据量随节点数激增 - - 调试信息的汇总与分析困难 - - 存储与查询性能瓶颈 - - 可视化呈现挑战大 - -## Probing的分布式Debug方案 - -Probing目前提供的一些机制能够较好的支撑分布式Debug能力的开发： - -1. 分布式探针 - - Probing的探针可以起到C/C++的ptrace与Python的Trace的作用； - - Probing的探针支持远程控制，可以用于控制集群所有探针； - - 可以借助sys.monitoring，实现轻量级的trace，尽可能降低对性能的影响； - -2. 数据处理 - - Probing自带Query引擎，善于处理大量本地数据； - - Query引擎的分布式能力，能够帮助Probing自动管理集群层面的分布式数据处理； - - Query引擎内部实现了高效的数据压缩； - -3. 自动化 - - Query 引擎提供了编程能力； - - 标准化的SQL查询语句，可以借助大模型自动生成SQL； - -### 高性能tracer - -针对Python 3.12 以上版本，可以借助`sys.monitoring` 实现高效的tracer。在tracer实现的过程中，应该遵循最小scope原则，即控制tracer所影响的Python代码，让其越小约好。 - -1. 控制生效范围：`sys.monitoring`支持基于字节码的trace，即通过`sys.monitoring.set_local_events`只对某个函数的字节码实现trace。 -2. 控制时间类型：越细粒度的事件，trace开销越大。因此在选择事件的时候，应该遵循如下原则： - 1. 优先使用触发性事件，比如`RAISE`这种异常抛出事件； - 2. 其次考虑使用`PY_START`或者`RETUEN`这种函数级别事件； - 3. 最后考虑`LINE`这种细粒度事件； - -针对Python 3.12 以前的版本，可以使用`trace`函数，但需要手动控制其影响范围： -1. 使用`with`语句控制trace范围内，保证tracer只对某个函数调用生效： - -```python -def probe(func=None): - @functools.wraps(func) - def wrapper(*args, **kwargs): - tracer = ProbingTracer(depth, watch) - with tracer: - return func(*args, **kwargs) - - return wrapper -``` - -2. 控制trace深度，及时关闭trace - -```python -# 向调用栈注入trace guard -frame.f_locals["__trace_guard__"] = TracerGuard() - -class TracerGuard: - def __init__(self, callback=None): - self.trace = sys.gettrace() # 保存trace函数 - sys.settrace(None) # 禁用trace - - def __del__(self): - sys.settrace(self.trace) # 恢复trace -``` - -### 变量观测与hook - -针对变量变更的追踪，可以通过以下方式实现： -1. 创建一个Tensor类型的子类； -2. 通过torch的dispatch机制，让对tensor的修改计算，dispatch给`__torch_function__` -3. 通过tracer，将调用堆栈上感兴趣的tensor替换成`HookedTensor`; - -```python -class HookedTensor(torch.Tensor, FakeProbingTensor): - def __format__(self, format_spec): - return f"{self.item().__format__(format_spec)}" - - @classmethod - def __torch_function__(cls, func, types, args=(), kwargs=None): - if kwargs is None: - kwargs = {} - if ( - func is not torch.Tensor.__repr__ - # and func is not torch.Tensor.__format__ - and func is not torch.Tensor.__str__ - and func.__name__.endswith("_") - and not func.__name__.startswith("__") - ): - old_val = f"{args}" - ret = super().__torch_function__(func, types, args, kwargs) - ret_val = f"{args}" - print( - f"probing: tensor update with {func.__name__}: {old_val} => {ret_val}" - ) - return ret - return super().__torch_function__(func, types, args, kwargs) -``` - -## 实际调试案例 - -### 案例1：内存泄漏调试 - -使用 Probing 的内存监控功能来定位 Python 应用中的内存泄漏： - -```python -# 启用内存追踪 -import probing - -# 在训练循环中监控内存使用 -with probing.trace("memory_usage"): - for epoch in range(num_epochs): - train_model(epoch) - -# 通过SQL查询分析内存使用模式 -``` - -### 案例2：分布式训练同步问题 - -监控分布式训练中的通信和同步问题： - -```python -# 监控集合通信操作 -with probing.trace("collective_ops"): - torch.distributed.all_reduce(tensor) - -# 分析不同节点的执行时间差异 -``` - -## 最佳实践 - -1. **最小化性能影响**：只在必要的代码段启用追踪 -2. **合理选择事件类型**：根据调试需求选择合适的监控事件 -3. **利用SQL分析**：使用Probing的SQL接口进行高效的数据分析 -4. **分布式协调**：在分布式环境中注意同步和协调问题 - -## 参考链接 - -- [内存分析指南](memory-analysis.md) -- [分布式训练分析](distributed.md) -- [SQL分析接口](sql-analytics.md) - -[^ptrace]: https://www.man7.org/linux/man-pages/man2/ptrace.2.html -[^dr0_3]: https://sandpile.org/x86/drx.htm -[^x86_regs]: https://wiki.osdev.org/CPU_Registers_x86#DR0_-_DR3 -[^pytrace]: https://docs.python.org/3/library/sys.html -[^monitoring]: https://docs.python.org/3/library/sys.monitoring.html#module-sys.monitoring diff --git a/docs/src/user-guide/memory-analysis.md b/docs/src/user-guide/memory-analysis.md deleted file mode 100644 index f4b57d24..00000000 --- a/docs/src/user-guide/memory-analysis.md +++ /dev/null @@ -1,625 +0,0 @@ -# Memory Analysis - -Probing provides comprehensive memory analysis capabilities for Python applications, helping you identify memory leaks, optimize memory usage, and understand memory allocation patterns through PyTorch integration and Python evaluation capabilities. - -## Overview - -Memory analysis in Probing covers: -- PyTorch GPU memory monitoring -- Tensor memory analysis -- Python memory monitoring via `psutil` and system tools -- Memory allocation patterns via torch traces -- Cross-step memory tracking - -## PyTorch Memory Monitoring - -### Current GPU Memory Status - -Check current GPU memory usage: -```bash -probing $ENDPOINT eval " -import torch -if torch.cuda.is_available(): - for i in range(torch.cuda.device_count()): - print(f'GPU {i}:') - print(f' Allocated: {torch.cuda.memory_allocated(i)/1024**3:.2f} GB') - print(f' Reserved: {torch.cuda.memory_reserved(i)/1024**3:.2f} GB') -else: - print('CUDA not available') -" -``` - -### Memory Trends via Torch Traces - -View memory allocation trends during training: -```bash -probing $ENDPOINT query " - SELECT step, stage, avg(allocated) as avg_memory_mb, max(allocated) as peak_memory_mb - FROM python.torch_trace - WHERE step > (SELECT max(step) - 10 FROM python.torch_trace) - GROUP BY step, stage - ORDER BY step, stage -" -``` - -### Memory Growth Detection - -Identify memory growth patterns: -```bash -probing $ENDPOINT query " - SELECT - step, - stage, - allocated, - LAG(allocated) OVER (PARTITION BY stage ORDER BY step, seq) as prev_memory, - allocated - LAG(allocated) OVER (PARTITION BY stage ORDER BY step, seq) as memory_delta - FROM python.torch_trace - WHERE step > (SELECT max(step) - 5 FROM python.torch_trace) - AND allocated > 0 - HAVING abs(memory_delta) > 50 -" -``` - -## Python Memory Analysis - -### Object Count Tracking - -Monitor Python object counts: -```bash -probing $ENDPOINT eval " -import gc -objects = gc.get_objects() -from collections import Counter -types = Counter(type(obj).__name__ for obj in objects) -for obj_type, count in types.most_common(10): - print(f'{obj_type}: {count}') -" -``` - -### Memory-Heavy Objects - -Find objects consuming the most memory: -```bash -probing $ENDPOINT eval " -import sys -import gc - -def get_size(obj): - try: - return sys.getsizeof(obj) - except: - return 0 - -objects = gc.get_objects() -large_objects = [(get_size(obj), type(obj).__name__, id(obj)) for obj in objects] -large_objects.sort(reverse=True) - -print('Top 10 largest objects:') -for size, obj_type, obj_id in large_objects[:10]: - print(f'{size:>10} bytes: {obj_type} (id: {obj_id})') -" -``` - -### Reference Counting - -Analyze object references: -```bash -probing $ENDPOINT eval " -import sys -import gc - -# Find objects with high reference counts -objects = gc.get_objects() -high_refs = [(sys.getrefcount(obj), type(obj).__name__, id(obj)) for obj in objects] -high_refs.sort(reverse=True) - -print('Objects with highest reference counts:') -for ref_count, obj_type, obj_id in high_refs[:10]: - print(f'{ref_count:>3} refs: {obj_type} (id: {obj_id})') -" -``` - -## Memory Leak Detection - -### GPU Memory Leak Detection via Torch Traces - -Monitor for GPU memory leaks during training: -```bash -probing $ENDPOINT query " - SELECT - step, - max(allocated) as peak_memory_mb, - min(allocated) as min_memory_mb, - max(allocated) - min(allocated) as memory_range_mb - FROM python.torch_trace - WHERE step > (SELECT max(step) - 20 FROM python.torch_trace) - GROUP BY step - HAVING max(allocated) - min(allocated) > 100 -- Alert if memory varies by >100MB in a step - ORDER BY step DESC -" -``` - -### Memory Growth Rate Analysis - -Calculate memory growth rates across training steps: -```bash -probing $ENDPOINT query " - WITH memory_deltas AS ( - SELECT - step, - stage, - allocated, - LAG(allocated) OVER (PARTITION BY stage ORDER BY step) as prev_allocated - FROM python.torch_trace - WHERE step > (SELECT max(step) - 10 FROM python.torch_trace) - AND allocated > 0 - ) - SELECT - step, - stage, - allocated - prev_allocated as memory_growth_mb - FROM memory_deltas - WHERE prev_allocated IS NOT NULL - AND allocated - prev_allocated > 50 -- Only show significant growth - ORDER BY memory_growth_mb DESC -" -``` - -### Python Memory Leak Detection - -Monitor memory usage with basic Python tools: -```bash -probing $ENDPOINT eval " -import gc -import psutil -import os - -# Force garbage collection -gc.collect() - -# Get current process memory usage -process = psutil.Process(os.getpid()) -memory_info = process.memory_info() -rss_mb = memory_info.rss / 1024 / 1024 -vms_mb = memory_info.vms / 1024 / 1024 - -print(f'Process Memory Usage:') -print(f' RSS Memory: {rss_mb:.1f} MB') -print(f' VMS Memory: {vms_mb:.1f} MB') - -# Count objects by type -objects = gc.get_objects() -print(f' Total objects: {len(objects)}') - -# Check for uncollectable objects -uncollectable = gc.garbage -if uncollectable: - print(f' Uncollectable objects: {len(uncollectable)}') -else: - print(' No uncollectable objects found') -" -``` - -### Memory Growth Rate Analysis via Python - -Monitor memory growth using basic Python memory monitoring: -```bash -probing $ENDPOINT eval " -import psutil -import time -import os - -# Get initial memory usage -process = psutil.Process(os.getpid()) -initial_memory = process.memory_info().rss / 1024 / 1024 - -print(f'Initial memory: {initial_memory:.1f} MB') - -# Wait and check again (in practice, you'd check periodically) -time.sleep(1) # Brief pause for demo -current_memory = process.memory_info().rss / 1024 / 1024 - -growth = current_memory - initial_memory -print(f'Current memory: {current_memory:.1f} MB') -print(f'Memory growth: {growth:.1f} MB') - -if growth > 10: # Alert if growth > 10MB - print('WARNING: Significant memory growth detected') -elif growth > 0: - print('Memory is growing slightly') -else: - print('Memory usage is stable') -" -``` - -### Garbage Collection Analysis - -Monitor garbage collection behavior: -```bash -probing $ENDPOINT eval " -import gc - -# Get GC stats -stats = gc.get_stats() -print('Garbage Collection Statistics:') -for i, stat in enumerate(stats): - print(f'Generation {i}: {stat}') - -# Force collection and see what's collected -before = len(gc.get_objects()) -collected = gc.collect() -after = len(gc.get_objects()) - -print(f'\\nObjects before GC: {before}') -print(f'Objects collected: {collected}') -print(f'Objects after GC: {after}') -print(f'Net reduction: {before - after}') -" -``` - -## PyTorch Memory Analysis - -### GPU Memory Monitoring - -For PyTorch applications with GPU usage: -```bash -probing $ENDPOINT eval " -import torch - -if torch.cuda.is_available(): - for i in range(torch.cuda.device_count()): - print(f'GPU {i}:') - print(f' Allocated: {torch.cuda.memory_allocated(i)/1024**3:.2f} GB') - print(f' Reserved: {torch.cuda.memory_reserved(i)/1024**3:.2f} GB') - print(f' Max Allocated: {torch.cuda.max_memory_allocated(i)/1024**3:.2f} GB') - print(f' Max Reserved: {torch.cuda.max_memory_reserved(i)/1024**3:.2f} GB') - print() -else: - print('CUDA not available') -" -``` - -### Tensor Memory Analysis - -Find large tensors: -```bash -probing $ENDPOINT eval " -import torch -import gc - -tensors = [obj for obj in gc.get_objects() if isinstance(obj, torch.Tensor)] -tensor_info = [] - -for tensor in tensors: - try: - size_bytes = tensor.nelement() * tensor.element_size() - size_mb = size_bytes / (1024 * 1024) - tensor_info.append((size_mb, tensor.shape, tensor.dtype, tensor.device)) - except: - continue - -tensor_info.sort(reverse=True) - -print('Largest tensors:') -for size_mb, shape, dtype, device in tensor_info[:15]: - print(f'{size_mb:>8.2f} MB: {shape} {dtype} on {device}') -" -``` - -### Memory Fragmentation Analysis - -Check for memory fragmentation: -```bash -probing $ENDPOINT eval " -import torch - -if torch.cuda.is_available(): - print('CUDA Memory Fragmentation Analysis:') - for i in range(torch.cuda.device_count()): - allocated = torch.cuda.memory_allocated(i) - reserved = torch.cuda.memory_reserved(i) - - if reserved > 0: - fragmentation = (reserved - allocated) / reserved * 100 - print(f'GPU {i}: {fragmentation:.1f}% fragmented') - print(f' Allocated: {allocated/1024**3:.2f} GB') - print(f' Reserved: {reserved/1024**3:.2f} GB') - print(f' Wasted: {(reserved-allocated)/1024**3:.2f} GB') -" -``` - -## Advanced Memory Analysis - -### Memory Profiling Integration - -Create basic memory profiles using system tools: -```bash -probing $ENDPOINT eval " -import psutil -import gc -import os - -# Get current process memory usage -process = psutil.Process(os.getpid()) -memory_info = process.memory_info() - -print(f'Memory Profile:') -print(f' RSS Memory: {memory_info.rss / 1024 / 1024:.1f} MB') -print(f' VMS Memory: {memory_info.vms / 1024 / 1024:.1f} MB') - -# Analyze object counts -objects = gc.get_objects() -from collections import Counter -type_counts = Counter(type(obj).__name__ for obj in objects) - -print(f'\\nTop 10 object types:') -for obj_type, count in type_counts.most_common(10): - print(f' {obj_type}: {count}') - -# Check garbage collection stats -print(f'\\nGarbage Collection:') -for i, stat in enumerate(gc.get_stats()): - print(f' Generation {i}: {stat}') -" -``` - -### Memory Allocation Patterns via Torch Traces - -Analyze GPU memory allocation patterns during training: -```bash -probing $ENDPOINT query " - SELECT - step / 10 * 10 as step_range, -- Group by 10-step ranges - stage, - avg(allocated) as avg_memory_mb, - max(allocated) as peak_memory_mb, - min(allocated) as min_memory_mb, - count(*) as trace_count - FROM python.torch_trace - WHERE step > (SELECT max(step) - 100 FROM python.torch_trace) - AND allocated > 0 - GROUP BY step_range, stage - ORDER BY step_range, stage -" -``` - -### Cross-Process Memory Analysis - -Compare memory usage across multiple processes using Python eval: -```bash -# Create a script to check multiple processes -for pid in 1234 5678 9012; do - echo "=== Process $pid ===" - probing $pid eval " -import os -import psutil - -try: - process = psutil.Process() - memory_info = process.memory_info() - print(f'PID: {os.getpid()}') - print(f'RSS Memory: {memory_info.rss / 1024 / 1024:.1f} MB') - print(f'VMS Memory: {memory_info.vms / 1024 / 1024:.1f} MB') - - # If PyTorch is available, also show GPU memory - try: - import torch - if torch.cuda.is_available(): - allocated = torch.cuda.memory_allocated() / 1024**3 - reserved = torch.cuda.memory_reserved() / 1024**3 - print(f'GPU Allocated: {allocated:.2f} GB') - print(f'GPU Reserved: {reserved:.2f} GB') - except ImportError: - pass -except Exception as e: - print(f'Error getting memory info: {e}') - " -done -``` - -## Memory Optimization Strategies - -### Identifying Memory Hotspots via System Monitoring - -Find memory usage patterns using system monitoring: -```bash -probing $ENDPOINT eval " -import psutil -import gc -import sys -from collections import defaultdict - -# Get current process info -process = psutil.Process() -memory_info = process.memory_info() - -print(f'System Memory Analysis:') -print(f' RSS Memory: {memory_info.rss / 1024 / 1024:.1f} MB') -print(f' VMS Memory: {memory_info.vms / 1024 / 1024:.1f} MB') -print(f' Memory Percent: {process.memory_percent():.1f}%') - -# Analyze large objects -objects = gc.get_objects() -large_objects = [] - -for obj in objects: - try: - size = sys.getsizeof(obj) - if size > 1024 * 1024: # Objects larger than 1MB - large_objects.append((size, type(obj).__name__)) - except: - continue - -large_objects.sort(reverse=True) - -print(f'\\nLarge objects (>1MB):') -for size, obj_type in large_objects[:10]: - print(f' {size / 1024 / 1024:.1f} MB: {obj_type}') - -# Check for memory trends -import time -time.sleep(0.1) # Brief pause -new_memory = process.memory_info().rss / 1024 / 1024 -growth = new_memory - (memory_info.rss / 1024 / 1024) -print(f'\\nMemory growth in 0.1s: {growth:.2f} MB') -" -``` - -### Memory Usage Patterns by Training Phase - -Analyze GPU memory usage patterns by training step ranges: -```bash -probing $ENDPOINT query " - SELECT - CASE - WHEN step % 100 < 25 THEN 'Early Phase' - WHEN step % 100 < 50 THEN 'Mid Phase' - WHEN step % 100 < 75 THEN 'Late Phase' - ELSE 'End Phase' - END as training_phase, - avg(allocated) as avg_memory_mb, - max(allocated) as peak_memory_mb, - count(*) as trace_count - FROM python.torch_trace - WHERE step > (SELECT max(step) - 500 FROM python.torch_trace) - AND allocated > 0 - GROUP BY training_phase - ORDER BY avg_memory_mb DESC -" -``` - -## Alerts and Monitoring - -### Memory Threshold Alerts via Python Monitoring - -Set up alerts for memory usage using Python: -```bash -probing $ENDPOINT eval " -import psutil -import torch - -# Check system memory -process = psutil.Process() -memory_mb = process.memory_info().rss / 1024 / 1024 - -if memory_mb > 8000: # Alert above 8GB - print(f'ALERT: HIGH_MEMORY_USAGE - {memory_mb:.1f} MB') -else: - print(f'Memory usage normal: {memory_mb:.1f} MB') - -# Check GPU memory if available -if torch.cuda.is_available(): - gpu_allocated_gb = torch.cuda.memory_allocated() / 1024**3 - gpu_reserved_gb = torch.cuda.memory_reserved() / 1024**3 - - if gpu_allocated_gb > 10: # Alert above 10GB - print(f'ALERT: HIGH_GPU_MEMORY - Allocated: {gpu_allocated_gb:.2f} GB') - else: - print(f'GPU memory normal: {gpu_allocated_gb:.2f} GB allocated') -" -``` - -### Memory Leak Alerts via Torch Trace Analysis - -Detect potential GPU memory leaks by monitoring allocation trends: -```bash -probing $ENDPOINT query " - WITH memory_trend AS ( - SELECT - step, - allocated, - AVG(allocated) OVER ( - ORDER BY step - ROWS BETWEEN 10 PRECEDING AND CURRENT ROW - ) as moving_avg_allocated - FROM python.torch_trace - WHERE step > (SELECT max(step) - 50 FROM python.torch_trace) - AND allocated > 0 - ) - SELECT - step, - allocated, - moving_avg_allocated, - 'POTENTIAL_MEMORY_LEAK' as alert_type - FROM memory_trend - WHERE allocated > moving_avg_allocated * 1.2 -- 20% above moving average - ORDER BY step DESC -" -``` - -## Best Practices - -1. **Regular Monitoring** - Set up continuous memory monitoring -2. **Baseline Establishment** - Know your application's normal memory patterns -3. **Gradual Analysis** - Start with high-level views, then drill down -4. **Consider Context** - Memory usage often correlates with workload -5. **Clean Up** - Regularly analyze and clean up unnecessary objects - -## Integration with Development Workflow - -### Pre-deployment Memory Checks - -Before deploying, analyze memory patterns using available data: -```bash -# Check for GPU memory growth during recent training -probing $ENDPOINT query " - SELECT - max(allocated) - min(allocated) as memory_growth_mb, - max(allocated) as peak_memory_mb, - count(DISTINCT step) as steps_analyzed - FROM python.torch_trace - WHERE step > (SELECT max(step) - 100 FROM python.torch_trace) - AND allocated > 0 -" - -# Also check Python memory usage -probing $ENDPOINT eval " -import psutil - -process = psutil.Process() -rss_mb = process.memory_info().rss / 1024 / 1024 - -print(f'Memory analysis complete:') -print(f' RSS Memory: {rss_mb:.1f} MB') -print(f' Memory Percent: {process.memory_percent():.1f}%') - -# Basic memory health check -if rss_mb > 8000: # > 8GB - print(' Status: HIGH MEMORY USAGE') -elif rss_mb > 4000: # > 4GB - print(' Status: MODERATE MEMORY USAGE') -else: - print(' Status: NORMAL MEMORY USAGE') -" -``` - -### Performance Regression Detection - -Compare memory usage between different training runs: -```bash -# Export current PyTorch memory profile -probing $ENDPOINT query " - SELECT - module, - stage, - avg(allocated) as avg_memory_mb, - max(allocated) as peak_memory_mb - FROM python.torch_trace - WHERE step > (SELECT max(step) - 100 FROM python.torch_trace) - GROUP BY module, stage -" > current_memory_profile.json - -# Compare with previous runs by analyzing torch trace patterns -probing $ENDPOINT query " - SELECT - step % 100 as relative_step, - avg(allocated) as avg_memory_mb, - stddev(allocated) as memory_variance - FROM python.torch_trace - WHERE step > (SELECT max(step) - 200 FROM python.torch_trace) - GROUP BY relative_step - ORDER BY relative_step -" > memory_pattern_analysis.json -``` - -For more detailed analysis techniques, see [SQL Analytics](sql-analytics.md) and [Basic Usage](basic-usage.md). diff --git a/docs/src/user-guide/sql-analytics.md b/docs/src/user-guide/sql-analytics.md deleted file mode 100644 index bb787819..00000000 --- a/docs/src/user-guide/sql-analytics.md +++ /dev/null @@ -1,389 +0,0 @@ -# SQL Analytics Interface - -Probing provides a powerful SQL interface for analyzing performance and monitoring data. This allows you to use familiar SQL syntax to query real-time and historical data from your applications. - -## Overview - -The SQL analytics interface transforms complex performance analysis into intuitive database queries. All monitoring data is accessible through standard SQL operations including `SELECT`, `WHERE`, `GROUP BY`, `ORDER BY`, and advanced analytical functions. - -## Basic Query Structure - -```sql -probing $ENDPOINT query "SELECT columns FROM table WHERE conditions" -``` - -## Core Tables - -### Configuration and Metadata - -**`information_schema.df_settings`** - System configuration and settings -```sql -SELECT * FROM information_schema.df_settings -WHERE name LIKE 'probing.%'; -``` - -Common columns: -- `name` - Configuration parameter name -- `value` - Configuration parameter value - -### Python Namespace Tables - -**`python.backtrace`** - Stack trace information -```sql -SELECT * FROM python.backtrace LIMIT 10; -``` - -Common columns: -- `ip` - Instruction pointer (for native frames) -- `file` - Source file name -- `func` - Function name -- `lineno` - Line number -- `depth` - Stack depth -- `frame_type` - Frame type ('Python' or 'Native') - -### Dynamic External Tables - -External tables can be created dynamically through the Python API: -```sql --- Example: Query a custom external table -SELECT * FROM python.my_custom_table; -``` - -## PyTorch Integration - -When monitoring PyTorch applications with the `@table` decorator, additional tables become available: - -**`python.torch_trace`** - PyTorch execution traces -```sql -SELECT step, module, stage, duration, allocated -FROM python.torch_trace -WHERE step >= 5 -ORDER BY step DESC, seq; -``` - -Common columns: -- `step` - Training step number -- `seq` - Sequence number within step -- `module` - Module name -- `stage` - Execution stage (forward, backward, step) -- `allocated` - GPU memory allocated (MB) -- `max_allocated` - Peak GPU memory allocated (MB) -- `cached` - GPU memory cached (MB) -- `max_cached` - Peak GPU memory cached (MB) -- `time_offset` - Time offset -- `duration` - Execution duration (seconds) - -**`python.variables`** - Variable tracking -```sql -SELECT step, func, name, value -FROM python.variables -WHERE step = (SELECT max(step) FROM python.variables); -``` - -Common columns: -- `step` - Training step number -- `func` - Function name -- `name` - Variable name -- `value` - Variable value (string representation) - -## Advanced Analytics - -### Time-Series Analysis - -**Memory growth over time (using torch_trace):** -```sql -SELECT - step, - stage, - avg(allocated) as avg_memory_mb, - max(allocated) as peak_memory_mb -FROM python.torch_trace -WHERE step > (SELECT max(step) - 10 FROM python.torch_trace) -GROUP BY step, stage -ORDER BY step, stage; -``` - -**Rolling averages:** -```sql -SELECT - step, - module, - duration, - AVG(duration) OVER ( - PARTITION BY module - ORDER BY step, seq - ROWS BETWEEN 4 PRECEDING AND CURRENT ROW - ) as avg_duration_5_samples -FROM python.torch_trace -WHERE step > (SELECT max(step) - 5 FROM python.torch_trace); -``` - -### Performance Analysis - -**Top slowest operations:** -```sql -SELECT - module, - stage, - count(*) as execution_count, - avg(duration) as avg_duration, - max(duration) as max_duration, - stddev(duration) as duration_stddev -FROM python.torch_trace -WHERE step > (SELECT max(step) - 10 FROM python.torch_trace) - AND duration > 0 -GROUP BY module, stage -HAVING count(*) > 5 -ORDER BY avg_duration DESC -LIMIT 10; -``` - -**Execution patterns:** -```sql -SELECT - step, - stage, - count(*) as operations_per_step -FROM python.torch_trace -WHERE step > (SELECT max(step) - 5 FROM python.torch_trace) -GROUP BY step, stage -ORDER BY step DESC, operations_per_step DESC; -``` - -### Training Progress Analysis - -**Memory usage trends during training:** -```sql -SELECT - step, - avg(allocated) as avg_memory_allocated, - max(allocated) as peak_memory_allocated, - min(allocated) as min_memory_allocated -FROM python.torch_trace -WHERE step IS NOT NULL -GROUP BY step -ORDER BY step; -``` - -**Module execution time analysis:** -```sql -SELECT - module, - stage, - avg(duration) as avg_duration, - count(*) as execution_count -FROM python.torch_trace -WHERE module IS NOT NULL - AND duration > 0 -GROUP BY module, stage -ORDER BY avg_duration DESC; -``` - -## Data Filtering and Conditions - -### Time-based Filtering - -```sql --- Recent steps only -WHERE step > (SELECT max(step) - 10 FROM python.torch_trace) - --- Specific step range -WHERE step BETWEEN 5 AND 15 - --- Latest data only -WHERE step = (SELECT max(step) FROM python.torch_trace) -``` - -### Performance Filtering - -```sql --- Long-running operations -WHERE duration > 0.1 - --- Memory-intensive operations -WHERE allocated > 1000 -- MB - --- Specific execution stages -WHERE stage IN ('forward', 'backward') - --- Specific modules -WHERE module LIKE '%attention%' -``` - -## Aggregation Functions - -### Statistical Functions - -```sql -SELECT - module, - stage, - count(*) as total_executions, - avg(duration) as mean_duration, - percentile_cont(0.5) WITHIN GROUP (ORDER BY duration) as median_duration, - percentile_cont(0.95) WITHIN GROUP (ORDER BY duration) as p95_duration, - min(duration) as min_duration, - max(duration) as max_duration, - stddev(duration) as std_duration -FROM python.torch_trace -WHERE duration > 0 -GROUP BY module, stage; -``` - -### Window Functions - -```sql -SELECT - step, - allocated, - LAG(allocated) OVER (ORDER BY step, seq) as prev_memory, - LEAD(allocated) OVER (ORDER BY step, seq) as next_memory, - ROW_NUMBER() OVER (ORDER BY allocated DESC) as memory_rank -FROM python.torch_trace -WHERE step > (SELECT max(step) - 5 FROM python.torch_trace); -``` - -## Cross-Table Joins - -**Correlate torch traces with variable tracking:** -```sql -SELECT - t.step, - t.module, - t.duration, - v.name as variable_name, - v.value as variable_value -FROM python.torch_trace t -JOIN python.variables v ON t.step = v.step -WHERE t.step > (SELECT max(step) - 3 FROM python.torch_trace) - AND t.duration > 0.05; -``` - -## Configuration Tables - -### View Current Settings - -```sql -SELECT * FROM information_schema.df_settings -WHERE name LIKE 'probing.%'; -``` - -### System Configuration - -```sql -SELECT * FROM information_schema.df_settings -WHERE name LIKE 'server.%' OR name LIKE 'torch.%'; -``` - -## Real-time Monitoring Queries - -### Dashboard Queries - -**Current training status:** -```sql -SELECT - 'Current Step' as metric, - max(step) as value, - '' as unit -FROM python.torch_trace -UNION ALL -SELECT - 'Peak Memory Usage', - max(allocated), - 'MB' -FROM python.torch_trace -WHERE step = (SELECT max(step) FROM python.torch_trace); -``` - -**Training progress:** -```sql -SELECT - step, - count(*) as total_operations, - avg(duration) as avg_duration, - max(allocated) as peak_memory_mb -FROM python.torch_trace -WHERE step > (SELECT max(step) - 5 FROM python.torch_trace) -GROUP BY step -ORDER BY step DESC -LIMIT 5; -``` - -## Export and Integration - -### Data Export - -Results can be exported for further analysis: - -```bash -# Export to JSON -probing $ENDPOINT query "SELECT * FROM python.torch_trace" > torch_traces.json - -# Time-series data for plotting -probing $ENDPOINT query " - SELECT step, stage, avg(duration), avg(allocated) - FROM python.torch_trace - WHERE step > (SELECT max(step) - 10 FROM python.torch_trace) - GROUP BY step, stage -" > training_metrics.json -``` - -### Integration with Other Tools - -The SQL interface makes it easy to integrate with monitoring and visualization tools: - -- Export data for Grafana dashboards -- Feed metrics into alerting systems -- Generate reports for analysis notebooks - -## Best Practices - -1. **Use step-based filtering** - Always include step constraints for better performance -2. **Limit result sets** - Use `LIMIT` clauses for large datasets -3. **Index-friendly queries** - Leverage step and module columns -4. **Aggregate appropriately** - Use `GROUP BY` for summary statistics -5. **Test queries incrementally** - Start simple and add complexity gradually - -## Common Query Patterns - -### Performance Regression Detection - -```sql -SELECT - module, - stage, - avg(duration) as current_avg, - LAG(avg(duration)) OVER (ORDER BY step) as prev_avg -FROM python.torch_trace -WHERE step > (SELECT max(step) - 10 FROM python.torch_trace) -GROUP BY module, stage, step -HAVING avg(duration) > LAG(avg(duration)) OVER (ORDER BY step) * 1.2; -``` - -### Memory Usage Growth Detection - -```sql -SELECT - step, - max(allocated) - min(allocated) as memory_growth_mb -FROM python.torch_trace -WHERE step > (SELECT max(step) - 5 FROM python.torch_trace) -GROUP BY step -HAVING max(allocated) - min(allocated) > 50 -ORDER BY step; -``` - -### Error Rate Analysis - -For custom external tables with error tracking: -```sql -SELECT - step, - count(*) FILTER (WHERE error_count > 0) * 100.0 / count(*) as error_rate_percent -FROM python.my_error_table -WHERE step > (SELECT max(step) - 20 FROM python.my_error_table) -GROUP BY step -ORDER BY step; -``` - -For more advanced usage patterns, see [Basic Usage](basic-usage.md) and [Memory Analysis](memory-analysis.md). diff --git a/docs/src/versions.md b/docs/src/versions.md new file mode 100644 index 00000000..3d0e5eff --- /dev/null +++ b/docs/src/versions.md @@ -0,0 +1,114 @@ +# Version Compatibility + +This page documents Probing version compatibility and changelog. + +## Current Version + +**Probing v0.6.x** (Latest) + +## System Requirements + +### Python Version + +| Probing Version | Python Support | +|-----------------|----------------| +| 0.6.x | Python 3.9 - 3.12 | +| 0.5.x | Python 3.8 - 3.11 | + +### PyTorch Version + +| Probing Version | PyTorch Support | +|-----------------|-----------------| +| 0.6.x | PyTorch 2.0+ | +| 0.5.x | PyTorch 1.13+ | + +### Operating Systems + +- **Linux**: Full support (recommended for production) +- **macOS**: Full support (Intel and Apple Silicon) +- **Windows**: Experimental (WSL2 recommended) + +## Changelog + +### v0.6.0 + +**New Features** + +- SQL query engine based on DataFusion +- Mermaid diagram support in documentation +- Improved distributed debugging support +- New `torch_trace` table for PyTorch profiling + +**Breaking Changes** + +- Deprecated `probing.trace()` API, use `probing.enable_torch_profiling()` instead +- Configuration format changed from JSON to TOML + +**Bug Fixes** + +- Fixed memory leak in long-running sessions +- Improved error messages for invalid SQL queries + +### v0.5.0 + +**New Features** + +- Initial PyTorch profiling support +- Memory analysis capabilities +- Basic SQL query support + +**Bug Fixes** + +- Various stability improvements + +## Upgrade Guide + +### From v0.5.x to v0.6.x + +1. Update Python to 3.9+ if needed +2. Update PyTorch to 2.0+ if needed +3. Update Probing: + +```bash +pip install --upgrade probing +``` + +4. Update configuration files (if using custom config): + +```python +# Old format (v0.5.x) +probing.trace(enabled=True) + +# New format (v0.6.x) +probing.enable_torch_profiling() +``` + +## Deprecation Policy + +- Major version changes may include breaking changes +- Minor version changes maintain backward compatibility +- Deprecated features show warnings for at least one minor version before removal + +## Feature Support Matrix + +| Feature | v0.5.x | v0.6.x | +|---------|--------|--------| +| Basic Profiling | ✅ | ✅ | +| SQL Queries | Partial | ✅ | +| PyTorch Tracing | Basic | Full | +| Memory Analysis | Basic | Full | +| Distributed Support | ❌ | ✅ | +| Custom Tables | ❌ | ✅ | +| Web UI | ❌ | Beta | + +## Reporting Issues + +For bugs and feature requests, please use the [GitHub Issue Tracker](https://github.com/DeepLink-org/probing/issues). + +When reporting issues, please include: + +- Probing version (`pip show probing`) +- Python version (`python --version`) +- PyTorch version (if applicable) +- Operating system +- Minimal reproduction example diff --git a/docs/src/versions.zh.md b/docs/src/versions.zh.md new file mode 100644 index 00000000..236aba4b --- /dev/null +++ b/docs/src/versions.zh.md @@ -0,0 +1,114 @@ +# 版本兼容性 + +本页记录 Probing 的版本兼容性和更新日志。 + +## 当前版本 + +**Probing v0.6.x** (最新) + +## 系统要求 + +### Python 版本 + +| Probing 版本 | Python 支持 | +|--------------|-------------| +| 0.6.x | Python 3.9 - 3.12 | +| 0.5.x | Python 3.8 - 3.11 | + +### PyTorch 版本 + +| Probing 版本 | PyTorch 支持 | +|--------------|--------------| +| 0.6.x | PyTorch 2.0+ | +| 0.5.x | PyTorch 1.13+ | + +### 操作系统 + +- **Linux**: 完全支持（生产环境推荐） +- **macOS**: 完全支持（Intel 和 Apple Silicon） +- **Windows**: 实验性支持（推荐使用 WSL2） + +## 更新日志 + +### v0.6.0 + +**新功能** + +- 基于 DataFusion 的 SQL 查询引擎 +- 文档支持 Mermaid 图表 +- 改进的分布式调试支持 +- 新增 `torch_trace` 表用于 PyTorch 性能分析 + +**破坏性变更** + +- 废弃 `probing.trace()` API，改用 `probing.enable_torch_profiling()` +- 配置格式从 JSON 改为 TOML + +**Bug 修复** + +- 修复长时间运行会话中的内存泄漏 +- 改进无效 SQL 查询的错误消息 + +### v0.5.0 + +**新功能** + +- 初始 PyTorch 性能分析支持 +- 内存分析能力 +- 基础 SQL 查询支持 + +**Bug 修复** + +- 各种稳定性改进 + +## 升级指南 + +### 从 v0.5.x 升级到 v0.6.x + +1. 如需要，升级 Python 到 3.9+ +2. 如需要，升级 PyTorch 到 2.0+ +3. 更新 Probing： + +```bash +pip install --upgrade probing +``` + +4. 更新配置文件（如果使用自定义配置）： + +```python +# 旧格式 (v0.5.x) +probing.trace(enabled=True) + +# 新格式 (v0.6.x) +probing.enable_torch_profiling() +``` + +## 废弃策略 + +- 主版本变更可能包含破坏性变更 +- 次版本变更保持向后兼容性 +- 废弃功能在移除前至少显示一个次版本的警告 + +## 功能支持矩阵 + +| 功能 | v0.5.x | v0.6.x | +|------|--------|--------| +| 基础性能分析 | ✅ | ✅ | +| SQL 查询 | 部分 | ✅ | +| PyTorch 跟踪 | 基础 | 完整 | +| 内存分析 | 基础 | 完整 | +| 分布式支持 | ❌ | ✅ | +| 自定义表 | ❌ | ✅ | +| Web UI | ❌ | Beta | + +## 报告问题 + +如需报告 Bug 或功能请求，请使用 [GitHub Issue Tracker](https://github.com/DeepLink-org/probing/issues)。 + +报告问题时，请包含： + +- Probing 版本 (`pip show probing`) +- Python 版本 (`python --version`) +- PyTorch 版本（如适用） +- 操作系统 +- 最小复现示例 diff --git a/docs/user-guide/debugging.md b/docs/user-guide/debugging.md deleted file mode 100644 index 32b1fec5..00000000 --- a/docs/user-guide/debugging.md +++ /dev/null @@ -1,417 +0,0 @@ - - -# Debugging Code with Probing - -This document introduces how to debug Python application code using Probing. For Probing's overall architecture design, please refer to [Architecture Overview](../advanced/architecture.md). - -System debugging has always been a challenge in distributed system development and optimization, especially for heterogeneous distributed training systems, which require comprehensive localization of errors and problems across multiple layers from hardware, system, framework, to model. Debugging requirements for distributed systems mainly focus on the following aspects: - -1. **Breakpoints**: Breakpoints are the most basic means of debugging programs. They can be used to observe program state and variable values, helping with BUG analysis and resolution. Breakpoints are divided into two types: - 1. **Location breakpoints**: Users specify a specific function or specific location in code to interrupt program execution and enter the debugger. - 2. **Conditional breakpoints**: Users specify a variable or memory address, and when it changes, program execution is interrupted. -2. **Instrumentation**: Usually inserting logs at target code locations to view variable values or system state. -3. **Crash site capture**: When exceptions occur, capture the crash site immediately or trigger breakpoints for further debugging. - -## Debug Methodology - -### CPU Debug Implementation - -This section discusses CPU-side debugger implementation methods for reference. - -#### How to Control Target Processes - -When debugging a process, the first step is to obtain control permissions over the target process to pause or resume its execution. In Linux systems, this is mainly achieved through the ptrace system call. The following is the function prototype of the ptrace system call: - -```C -#include - -long ptrace(enum __ptrace_request op, pid_t pid, - void *addr, void *data); -``` - -`ptrace` provides a method to control target process execution, allowing the debugger to interact with the target process to achieve debugging functionality. Common values for `__ptrace_request` are: - -- `PTRACE_ATTACH`: Attach to target process, making it the current process's tracee. -- `PTRACE_INTERRUPT`: Pause target tracee. -- `PTRACE_CONT`: Resume target process execution. -- `PTRACE_DETACH`: Release target tracee. -- `PTRACE_GETREGS/PTRACE_SETREGS`: Read/write target process registers. -- `PTRACE_PEEKDATA/PTRACE_POKEDATA`: Read/write target process memory, one WORD at a time. -- `/proc//mem`: Read/write large blocks of memory. - -A typical debugger workflow is as follows: -1. Attach to target process. -2. Insert breakpoints by reading/writing the target process's TEXT segment. -3. Resume target process execution and use `waitpid` to wait for the target process to pause at breakpoint. -4. When target process pauses, view information by reading/writing memory. - -#### CPU Breakpoint Debugging - -##### Software Breakpoints - -X86 processors support a special interrupt instruction (INT 3, 0xCC). When the CPU executes this instruction, it triggers an interrupt for the debugger to capture. Inserting breakpoints requires directly modifying the target process's code segment. - -##### Hardware Breakpoints - -X86 processors provide a series of debug registers (DR0-DR3)[^dr0_3] that can monitor addresses pointed to by registers without modifying memory. When the target address is accessed or executed, it triggers a CPU interrupt, notifying GDB. Register definitions are as follows[^x86_regs]: - -- DR0-DR3: Virtual addresses of breakpoints -- DR6: Status register, stores DEBUG-related status indicator bits. -- DR7: Control register, controls breakpoint-related behavior. - -### Python Debug Implementation - -#### Trace Mechanism - -Python's own debugger is mainly implemented through the trace mechanism[^pytrace]: - -```python -sys.settrace(tracefunc) -``` - -`tracefunc` receives 5 types of events from the Python interpreter: - -- call: Execute function call. -- line: Execute a line of code. -- return: Function returns. -- exception: An exception occurs. -- opcode: Execute a bytecode instruction. Since opcode trace has high performance overhead, `f_trace_opcodes` must be set to enable it. - -Using the `trace` mechanism, a watch method for Python variables can be implemented: - -```python -def trace(self, frame: FrameType, event: AnyStr, arg: Any): - if event != "line": return self.trace # Ignore events other than line - - for k, v in self.watch.items(): # Iterate watch list - if k in frame.f_locals and id(frame.f_locals[k]) != v: # Detect variable id changes - print(f"variable update {k} = {frame.f_locals[k]}") - self.watch[k] = id(frame.f_locals[k]) - return self.trace # Continue tracing, must return a new trace function each time -``` - -#### Execution Event Monitoring Mechanism - -Since Python 3.12, Python introduced the Execution event monitoring mechanism[^monitoring] to provide Python interpreter internal execution events to various tools. This mechanism is mainly targeted at Debugger, Profiler, and Optimizer developers. Let's look at a simple example of capturing exceptions during Python execution using `sys.monitoring`: - -```python -import sys - -def hook(*args, **kwargs): # Define event hook - print("=== hook", args, kwargs) - -# Declare debugging tool -sys.monitoring.use_tool_id(sys.monitoring.DEBUGGER_ID, "debugging") - -# Enable RAISE event for exceptions -sys.monitoring.set_events(sys.monitoring.DEBUGGER_ID, sys.monitoring.events.RAISE) - -# Register hook for exception event -sys.monitoring.register_callback( - sys.monitoring.DEBUGGER_ID, - sys.monitoring.events.RAISE, - hook, -) - -# Test code -def foo(a): - b = 2 - bar(a, b) - -def bar(a, b): - c = a+b - raise Exception('error') - -foo(1) -``` - -When executing the above code, when an exception is triggered in the `bar` function, the following message will be printed: -``` -=== hook (, 32, Exception('error')) {} -``` - -##### Registering and Using Tools - -A core concept in monitoring is **tool**, used to distinguish different tools and avoid conflicts. **Tool**-related APIs are as follows: -``` -sys.monitoring.use_tool_id(tool_id: int, name: str, /) → None -Declare use of **tool**, must be declared before use. - -sys.monitoring.free_tool_id(tool_id: int, /) → None -Release **tool** - -sys.monitoring.get_tool(tool_id: int, /) → str | None -Get **tool** -``` - -**Tool** can be defined with any integer ID. For convenience, the system predefines several tool IDs: -``` -sys.monitoring.DEBUGGER_ID = 0 -sys.monitoring.COVERAGE_ID = 1 -sys.monitoring.PROFILER_ID = 2 -sys.monitoring.OPTIMIZER_ID = 5 -``` - -##### Events - -Defines which execution phase events will be sent to **tool**. Currently supported events include: -- **BRANCH**(sys.monitoring.events.BRANCH) -- **CALL**: A call in Python code (event occurs before the call). -- **C_RAISE**: An exception raised from any callable, except for Python functions (event occurs after the exit). -- **C_RETURN**: Return from any callable, except for Python functions (event occurs after the return). -- **EXCEPTION_HANDLED**: An exception is handled. -- **INSTRUCTION**: A VM instruction is about to be executed. -- **JUMP**: An unconditional jump in the control flow graph is made. -- **LINE**: An instruction is about to be executed that has a different line number from the preceding instruction. -- **PY_RESUME**: Resumption of a Python function (for generator and coroutine functions), except for throw() calls. -- **PY_RETURN**: Return from a Python function (occurs immediately before the return, the callee's frame will be on the stack). -- **PY_START**: Start of a Python function (occurs immediately after the call, the callee's frame will be on the stack) -- **PY_THROW**: A Python function is resumed by a throw() call. -- **PY_UNWIND**: Exit from a Python function during exception unwinding. -- **PY_YIELD**: Yield from a Python function (occurs immediately before the yield, the callee's frame will be on the stack). -- **RAISE**: An exception is raised, except those that cause a STOP_ITERATION event. -- **RERAISE**: An exception is re-raised, for example at the end of a finally block. -- **STOP_ITERATION**: An artificial StopIteration is raised; see the STOP_ITERATION event. - -Events support boolean operations, such as `PY_RETURN | PY_START` representing handling both events simultaneously. - -##### Registering Callback Functions - -```python -sys.monitoring.register_callback(tool_id: int, event: int, func: Callable | None, /) → Callable | None -``` -Used to register callback functions. The return value is the old callback function. To unregister a callback function, set the `func` parameter to `None`. - -##### Enabling Events for Specific Functions - -In addition to globally enabling events, events can also be enabled for specific functions: -```python -sys.monitoring.set_local_events(tool_id: int, code: CodeType, event_set: int, /) → None -``` -Here the `code` parameter can be a function. Locally enabling events can effectively narrow the scope of event impact and reduce performance impact. - -### Exception Capture - -```python -sys.excepthook(type, value, traceback) -``` - -By registering `excepthook`, uncaught exceptions in Python processes can be captured, for example: - -```python -import sys - -def handle(t, v, tb): - print("=== exception handler, ", t, v, tb) - -def foo(a): - b = 2 - bar(a, b) - -def bar(a, b): - raise Exception('error') - -sys.excepthook = handle - -foo(1) -``` - -`excepthook` can capture the stack, but cannot capture the crash site and local variables. - -## Challenges in Distributed Training Debugging - -### Typical Scenarios - -Distributed heterogeneous training typically faces the following scenarios: - -- **Data Parallelism**: Multiple nodes simultaneously process different data subsets and synchronize gradients in parameter servers or ring communication. -- **Model Parallelism**: Split the model into different parts, running on different nodes. -- **Heterogeneous Training**: Training uses heterogeneous computing devices like GPUs, executing asynchronously with CPUs on each node. -- **Fault Recovery**: In distributed training, node failures may occur frequently, requiring effective fault localization and recovery mechanisms. - -Distributed debugging requirements include: -1. Distributed breakpoints to observe execution status of each process. -2. Distributed variable observation to monitor key variables of each process. -3. Distributed hooks: Focus on values and changes of key variables or tensors. -4. Distributed backtrace: Observe execution stack of each process. - -### Main Challenges - -1. **Distributed Breakpoint Debugging**: - - Role differences exist between multiple nodes (e.g., different TP/PP/DP roles). - - Breakpoints in collective communication scenarios affect overall execution flow. - - Traditional breakpoint debugging tools are difficult to adapt to distributed environments. - - Need to design lightweight and semantically-aware observation mechanisms. - -2. **Distributed Variable Observation**: - - High overhead for variable acquisition and serialization. - - Limited network transmission bandwidth. - - Complex data aggregation and display. - - Trade-off between real-time performance and system performance. - -3. **Distributed Hooks and Traceback**: - - Capture key actions or variable updates, especially synchronization primitives and asynchronous logic in communication libraries. - - Obtain execution stack of each process and aggregate analysis. - -4. **Scale Scalability**: - - Log data volume increases dramatically with number of nodes. - - Difficult to aggregate and analyze debugging information. - - Storage and query performance bottlenecks. - - Large visualization challenges. - -## Probing's Distributed Debug Solution - -Probing currently provides mechanisms that can well support the development of distributed debug capabilities: - -1. **Distributed Probes**: - - Probing's probes can serve the role of C/C++ ptrace and Python Trace. - - Probing's probes support remote control and can be used to control all probes in a cluster. - - Can leverage sys.monitoring to implement lightweight tracing, minimizing performance impact. - -2. **Data Processing**: - - Probing has a built-in Query engine, good at processing large amounts of local data. - - The Query engine's distributed capabilities can help Probing automatically manage cluster-level distributed data processing. - - The Query engine internally implements efficient data compression. - -3. **Automation**: - - The Query engine provides programming capabilities. - - Standardized SQL query statements can leverage large models to automatically generate SQL. - -### High-Performance Tracer - -For Python 3.12 and above, efficient tracers can be implemented using `sys.monitoring`. In tracer implementation, the minimum scope principle should be followed, i.e., control the Python code affected by the tracer to be as small as possible. - -1. **Control Effective Scope**: `sys.monitoring` supports bytecode-based tracing, i.e., using `sys.monitoring.set_local_events` to trace only bytecode of specific functions. -2. **Control Event Types**: Finer-grained events have higher trace overhead. Therefore, when selecting events, the following principles should be followed: - 1. Prefer trigger events, such as `RAISE` exception throwing events. - 2. Next consider function-level events like `PY_START` or `RETURN`. - 3. Finally consider fine-grained events like `LINE`. - -For Python versions before 3.12, the `trace` function can be used, but its impact scope needs to be manually controlled: -1. Use `with` statements to control trace scope, ensuring tracer only affects specific function calls: - -```python -def probe(func=None): - @functools.wraps(func) - def wrapper(*args, **kwargs): - tracer = ProbingTracer(depth, watch) - with tracer: - return func(*args, **kwargs) - - return wrapper -``` - -2. Control trace depth and close trace in time: - -```python -# Inject trace guard into call stack -frame.f_locals["__trace_guard__"] = TracerGuard() - -class TracerGuard: - def __init__(self, callback=None): - self.trace = sys.gettrace() # Save trace function - sys.settrace(None) # Disable trace - - def __del__(self): - sys.settrace(self.trace) # Restore trace -``` - -### Variable Observation and Hooks - -For tracking variable changes, the following approach can be implemented: -1. Create a Tensor subclass. -2. Use torch's dispatch mechanism to dispatch tensor modification computations to `__torch_function__`. -3. Use tracer to replace tensors of interest in the call stack with `HookedTensor`. - -```python -class HookedTensor(torch.Tensor, FakeProbingTensor): - def __format__(self, format_spec): - return f"{self.item().__format__(format_spec)}" - - @classmethod - def __torch_function__(cls, func, types, args=(), kwargs=None): - if kwargs is None: - kwargs = {} - if ( - func is not torch.Tensor.__repr__ - # and func is not torch.Tensor.__format__ - and func is not torch.Tensor.__str__ - and func.__name__.endswith("_") - and not func.__name__.startswith("__") - ): - old_val = f"{args}" - ret = super().__torch_function__(func, types, args, kwargs) - ret_val = f"{args}" - print( - f"probing: tensor update with {func.__name__}: {old_val} => {ret_val}" - ) - return ret - return super().__torch_function__(func, types, args, kwargs) -``` - -## Practical Debugging Cases - -### Case 1: Memory Leak Debugging - -Use Probing's memory monitoring capabilities to locate memory leaks in Python applications: - -```python -# Enable memory tracking -import probing - -# Monitor memory usage in training loop -with probing.span("memory_usage"): - for epoch in range(num_epochs): - train_model(epoch) - -# Analyze memory usage patterns through SQL queries -``` - -### Case 2: Distributed Training Synchronization Issues - -Monitor communication and synchronization issues in distributed training: - -```python -# Monitor collective communication operations -with probing.span("collective_ops"): - torch.distributed.all_reduce(tensor) - -# Analyze execution time differences across different nodes -``` - -## Best Practices - -1. **Minimize Performance Impact**: Only enable tracing in necessary code segments. -2. **Choose Event Types Appropriately**: Select appropriate monitoring events based on debugging needs. -3. **Leverage SQL Analysis**: Use Probing's SQL interface for efficient data analysis. -4. **Distributed Coordination**: Pay attention to synchronization and coordination issues in distributed environments. - -## References - -- [Memory Analysis Guide](memory-analysis.md) -- [Distributed Training Analysis](distributed.md) -- [SQL Analytics Interface](sql-analytics.md) - -[^ptrace]: https://www.man7.org/linux/man-pages/man2/ptrace.2.html -[^dr0_3]: https://sandpile.org/x86/drx.htm -[^x86_regs]: https://wiki.osdev.org/CPU_Registers_x86#DR0_-_DR3 -[^pytrace]: https://docs.python.org/3/library/sys.html -[^monitoring]: https://docs.python.org/3/library/sys.monitoring.html#module-sys.monitoring diff --git a/docs/user-guide/memory-analysis.md b/docs/user-guide/memory-analysis.md deleted file mode 100644 index f4b57d24..00000000 --- a/docs/user-guide/memory-analysis.md +++ /dev/null @@ -1,625 +0,0 @@ -# Memory Analysis - -Probing provides comprehensive memory analysis capabilities for Python applications, helping you identify memory leaks, optimize memory usage, and understand memory allocation patterns through PyTorch integration and Python evaluation capabilities. - -## Overview - -Memory analysis in Probing covers: -- PyTorch GPU memory monitoring -- Tensor memory analysis -- Python memory monitoring via `psutil` and system tools -- Memory allocation patterns via torch traces -- Cross-step memory tracking - -## PyTorch Memory Monitoring - -### Current GPU Memory Status - -Check current GPU memory usage: -```bash -probing $ENDPOINT eval " -import torch -if torch.cuda.is_available(): - for i in range(torch.cuda.device_count()): - print(f'GPU {i}:') - print(f' Allocated: {torch.cuda.memory_allocated(i)/1024**3:.2f} GB') - print(f' Reserved: {torch.cuda.memory_reserved(i)/1024**3:.2f} GB') -else: - print('CUDA not available') -" -``` - -### Memory Trends via Torch Traces - -View memory allocation trends during training: -```bash -probing $ENDPOINT query " - SELECT step, stage, avg(allocated) as avg_memory_mb, max(allocated) as peak_memory_mb - FROM python.torch_trace - WHERE step > (SELECT max(step) - 10 FROM python.torch_trace) - GROUP BY step, stage - ORDER BY step, stage -" -``` - -### Memory Growth Detection - -Identify memory growth patterns: -```bash -probing $ENDPOINT query " - SELECT - step, - stage, - allocated, - LAG(allocated) OVER (PARTITION BY stage ORDER BY step, seq) as prev_memory, - allocated - LAG(allocated) OVER (PARTITION BY stage ORDER BY step, seq) as memory_delta - FROM python.torch_trace - WHERE step > (SELECT max(step) - 5 FROM python.torch_trace) - AND allocated > 0 - HAVING abs(memory_delta) > 50 -" -``` - -## Python Memory Analysis - -### Object Count Tracking - -Monitor Python object counts: -```bash -probing $ENDPOINT eval " -import gc -objects = gc.get_objects() -from collections import Counter -types = Counter(type(obj).__name__ for obj in objects) -for obj_type, count in types.most_common(10): - print(f'{obj_type}: {count}') -" -``` - -### Memory-Heavy Objects - -Find objects consuming the most memory: -```bash -probing $ENDPOINT eval " -import sys -import gc - -def get_size(obj): - try: - return sys.getsizeof(obj) - except: - return 0 - -objects = gc.get_objects() -large_objects = [(get_size(obj), type(obj).__name__, id(obj)) for obj in objects] -large_objects.sort(reverse=True) - -print('Top 10 largest objects:') -for size, obj_type, obj_id in large_objects[:10]: - print(f'{size:>10} bytes: {obj_type} (id: {obj_id})') -" -``` - -### Reference Counting - -Analyze object references: -```bash -probing $ENDPOINT eval " -import sys -import gc - -# Find objects with high reference counts -objects = gc.get_objects() -high_refs = [(sys.getrefcount(obj), type(obj).__name__, id(obj)) for obj in objects] -high_refs.sort(reverse=True) - -print('Objects with highest reference counts:') -for ref_count, obj_type, obj_id in high_refs[:10]: - print(f'{ref_count:>3} refs: {obj_type} (id: {obj_id})') -" -``` - -## Memory Leak Detection - -### GPU Memory Leak Detection via Torch Traces - -Monitor for GPU memory leaks during training: -```bash -probing $ENDPOINT query " - SELECT - step, - max(allocated) as peak_memory_mb, - min(allocated) as min_memory_mb, - max(allocated) - min(allocated) as memory_range_mb - FROM python.torch_trace - WHERE step > (SELECT max(step) - 20 FROM python.torch_trace) - GROUP BY step - HAVING max(allocated) - min(allocated) > 100 -- Alert if memory varies by >100MB in a step - ORDER BY step DESC -" -``` - -### Memory Growth Rate Analysis - -Calculate memory growth rates across training steps: -```bash -probing $ENDPOINT query " - WITH memory_deltas AS ( - SELECT - step, - stage, - allocated, - LAG(allocated) OVER (PARTITION BY stage ORDER BY step) as prev_allocated - FROM python.torch_trace - WHERE step > (SELECT max(step) - 10 FROM python.torch_trace) - AND allocated > 0 - ) - SELECT - step, - stage, - allocated - prev_allocated as memory_growth_mb - FROM memory_deltas - WHERE prev_allocated IS NOT NULL - AND allocated - prev_allocated > 50 -- Only show significant growth - ORDER BY memory_growth_mb DESC -" -``` - -### Python Memory Leak Detection - -Monitor memory usage with basic Python tools: -```bash -probing $ENDPOINT eval " -import gc -import psutil -import os - -# Force garbage collection -gc.collect() - -# Get current process memory usage -process = psutil.Process(os.getpid()) -memory_info = process.memory_info() -rss_mb = memory_info.rss / 1024 / 1024 -vms_mb = memory_info.vms / 1024 / 1024 - -print(f'Process Memory Usage:') -print(f' RSS Memory: {rss_mb:.1f} MB') -print(f' VMS Memory: {vms_mb:.1f} MB') - -# Count objects by type -objects = gc.get_objects() -print(f' Total objects: {len(objects)}') - -# Check for uncollectable objects -uncollectable = gc.garbage -if uncollectable: - print(f' Uncollectable objects: {len(uncollectable)}') -else: - print(' No uncollectable objects found') -" -``` - -### Memory Growth Rate Analysis via Python - -Monitor memory growth using basic Python memory monitoring: -```bash -probing $ENDPOINT eval " -import psutil -import time -import os - -# Get initial memory usage -process = psutil.Process(os.getpid()) -initial_memory = process.memory_info().rss / 1024 / 1024 - -print(f'Initial memory: {initial_memory:.1f} MB') - -# Wait and check again (in practice, you'd check periodically) -time.sleep(1) # Brief pause for demo -current_memory = process.memory_info().rss / 1024 / 1024 - -growth = current_memory - initial_memory -print(f'Current memory: {current_memory:.1f} MB') -print(f'Memory growth: {growth:.1f} MB') - -if growth > 10: # Alert if growth > 10MB - print('WARNING: Significant memory growth detected') -elif growth > 0: - print('Memory is growing slightly') -else: - print('Memory usage is stable') -" -``` - -### Garbage Collection Analysis - -Monitor garbage collection behavior: -```bash -probing $ENDPOINT eval " -import gc - -# Get GC stats -stats = gc.get_stats() -print('Garbage Collection Statistics:') -for i, stat in enumerate(stats): - print(f'Generation {i}: {stat}') - -# Force collection and see what's collected -before = len(gc.get_objects()) -collected = gc.collect() -after = len(gc.get_objects()) - -print(f'\\nObjects before GC: {before}') -print(f'Objects collected: {collected}') -print(f'Objects after GC: {after}') -print(f'Net reduction: {before - after}') -" -``` - -## PyTorch Memory Analysis - -### GPU Memory Monitoring - -For PyTorch applications with GPU usage: -```bash -probing $ENDPOINT eval " -import torch - -if torch.cuda.is_available(): - for i in range(torch.cuda.device_count()): - print(f'GPU {i}:') - print(f' Allocated: {torch.cuda.memory_allocated(i)/1024**3:.2f} GB') - print(f' Reserved: {torch.cuda.memory_reserved(i)/1024**3:.2f} GB') - print(f' Max Allocated: {torch.cuda.max_memory_allocated(i)/1024**3:.2f} GB') - print(f' Max Reserved: {torch.cuda.max_memory_reserved(i)/1024**3:.2f} GB') - print() -else: - print('CUDA not available') -" -``` - -### Tensor Memory Analysis - -Find large tensors: -```bash -probing $ENDPOINT eval " -import torch -import gc - -tensors = [obj for obj in gc.get_objects() if isinstance(obj, torch.Tensor)] -tensor_info = [] - -for tensor in tensors: - try: - size_bytes = tensor.nelement() * tensor.element_size() - size_mb = size_bytes / (1024 * 1024) - tensor_info.append((size_mb, tensor.shape, tensor.dtype, tensor.device)) - except: - continue - -tensor_info.sort(reverse=True) - -print('Largest tensors:') -for size_mb, shape, dtype, device in tensor_info[:15]: - print(f'{size_mb:>8.2f} MB: {shape} {dtype} on {device}') -" -``` - -### Memory Fragmentation Analysis - -Check for memory fragmentation: -```bash -probing $ENDPOINT eval " -import torch - -if torch.cuda.is_available(): - print('CUDA Memory Fragmentation Analysis:') - for i in range(torch.cuda.device_count()): - allocated = torch.cuda.memory_allocated(i) - reserved = torch.cuda.memory_reserved(i) - - if reserved > 0: - fragmentation = (reserved - allocated) / reserved * 100 - print(f'GPU {i}: {fragmentation:.1f}% fragmented') - print(f' Allocated: {allocated/1024**3:.2f} GB') - print(f' Reserved: {reserved/1024**3:.2f} GB') - print(f' Wasted: {(reserved-allocated)/1024**3:.2f} GB') -" -``` - -## Advanced Memory Analysis - -### Memory Profiling Integration - -Create basic memory profiles using system tools: -```bash -probing $ENDPOINT eval " -import psutil -import gc -import os - -# Get current process memory usage -process = psutil.Process(os.getpid()) -memory_info = process.memory_info() - -print(f'Memory Profile:') -print(f' RSS Memory: {memory_info.rss / 1024 / 1024:.1f} MB') -print(f' VMS Memory: {memory_info.vms / 1024 / 1024:.1f} MB') - -# Analyze object counts -objects = gc.get_objects() -from collections import Counter -type_counts = Counter(type(obj).__name__ for obj in objects) - -print(f'\\nTop 10 object types:') -for obj_type, count in type_counts.most_common(10): - print(f' {obj_type}: {count}') - -# Check garbage collection stats -print(f'\\nGarbage Collection:') -for i, stat in enumerate(gc.get_stats()): - print(f' Generation {i}: {stat}') -" -``` - -### Memory Allocation Patterns via Torch Traces - -Analyze GPU memory allocation patterns during training: -```bash -probing $ENDPOINT query " - SELECT - step / 10 * 10 as step_range, -- Group by 10-step ranges - stage, - avg(allocated) as avg_memory_mb, - max(allocated) as peak_memory_mb, - min(allocated) as min_memory_mb, - count(*) as trace_count - FROM python.torch_trace - WHERE step > (SELECT max(step) - 100 FROM python.torch_trace) - AND allocated > 0 - GROUP BY step_range, stage - ORDER BY step_range, stage -" -``` - -### Cross-Process Memory Analysis - -Compare memory usage across multiple processes using Python eval: -```bash -# Create a script to check multiple processes -for pid in 1234 5678 9012; do - echo "=== Process $pid ===" - probing $pid eval " -import os -import psutil - -try: - process = psutil.Process() - memory_info = process.memory_info() - print(f'PID: {os.getpid()}') - print(f'RSS Memory: {memory_info.rss / 1024 / 1024:.1f} MB') - print(f'VMS Memory: {memory_info.vms / 1024 / 1024:.1f} MB') - - # If PyTorch is available, also show GPU memory - try: - import torch - if torch.cuda.is_available(): - allocated = torch.cuda.memory_allocated() / 1024**3 - reserved = torch.cuda.memory_reserved() / 1024**3 - print(f'GPU Allocated: {allocated:.2f} GB') - print(f'GPU Reserved: {reserved:.2f} GB') - except ImportError: - pass -except Exception as e: - print(f'Error getting memory info: {e}') - " -done -``` - -## Memory Optimization Strategies - -### Identifying Memory Hotspots via System Monitoring - -Find memory usage patterns using system monitoring: -```bash -probing $ENDPOINT eval " -import psutil -import gc -import sys -from collections import defaultdict - -# Get current process info -process = psutil.Process() -memory_info = process.memory_info() - -print(f'System Memory Analysis:') -print(f' RSS Memory: {memory_info.rss / 1024 / 1024:.1f} MB') -print(f' VMS Memory: {memory_info.vms / 1024 / 1024:.1f} MB') -print(f' Memory Percent: {process.memory_percent():.1f}%') - -# Analyze large objects -objects = gc.get_objects() -large_objects = [] - -for obj in objects: - try: - size = sys.getsizeof(obj) - if size > 1024 * 1024: # Objects larger than 1MB - large_objects.append((size, type(obj).__name__)) - except: - continue - -large_objects.sort(reverse=True) - -print(f'\\nLarge objects (>1MB):') -for size, obj_type in large_objects[:10]: - print(f' {size / 1024 / 1024:.1f} MB: {obj_type}') - -# Check for memory trends -import time -time.sleep(0.1) # Brief pause -new_memory = process.memory_info().rss / 1024 / 1024 -growth = new_memory - (memory_info.rss / 1024 / 1024) -print(f'\\nMemory growth in 0.1s: {growth:.2f} MB') -" -``` - -### Memory Usage Patterns by Training Phase - -Analyze GPU memory usage patterns by training step ranges: -```bash -probing $ENDPOINT query " - SELECT - CASE - WHEN step % 100 < 25 THEN 'Early Phase' - WHEN step % 100 < 50 THEN 'Mid Phase' - WHEN step % 100 < 75 THEN 'Late Phase' - ELSE 'End Phase' - END as training_phase, - avg(allocated) as avg_memory_mb, - max(allocated) as peak_memory_mb, - count(*) as trace_count - FROM python.torch_trace - WHERE step > (SELECT max(step) - 500 FROM python.torch_trace) - AND allocated > 0 - GROUP BY training_phase - ORDER BY avg_memory_mb DESC -" -``` - -## Alerts and Monitoring - -### Memory Threshold Alerts via Python Monitoring - -Set up alerts for memory usage using Python: -```bash -probing $ENDPOINT eval " -import psutil -import torch - -# Check system memory -process = psutil.Process() -memory_mb = process.memory_info().rss / 1024 / 1024 - -if memory_mb > 8000: # Alert above 8GB - print(f'ALERT: HIGH_MEMORY_USAGE - {memory_mb:.1f} MB') -else: - print(f'Memory usage normal: {memory_mb:.1f} MB') - -# Check GPU memory if available -if torch.cuda.is_available(): - gpu_allocated_gb = torch.cuda.memory_allocated() / 1024**3 - gpu_reserved_gb = torch.cuda.memory_reserved() / 1024**3 - - if gpu_allocated_gb > 10: # Alert above 10GB - print(f'ALERT: HIGH_GPU_MEMORY - Allocated: {gpu_allocated_gb:.2f} GB') - else: - print(f'GPU memory normal: {gpu_allocated_gb:.2f} GB allocated') -" -``` - -### Memory Leak Alerts via Torch Trace Analysis - -Detect potential GPU memory leaks by monitoring allocation trends: -```bash -probing $ENDPOINT query " - WITH memory_trend AS ( - SELECT - step, - allocated, - AVG(allocated) OVER ( - ORDER BY step - ROWS BETWEEN 10 PRECEDING AND CURRENT ROW - ) as moving_avg_allocated - FROM python.torch_trace - WHERE step > (SELECT max(step) - 50 FROM python.torch_trace) - AND allocated > 0 - ) - SELECT - step, - allocated, - moving_avg_allocated, - 'POTENTIAL_MEMORY_LEAK' as alert_type - FROM memory_trend - WHERE allocated > moving_avg_allocated * 1.2 -- 20% above moving average - ORDER BY step DESC -" -``` - -## Best Practices - -1. **Regular Monitoring** - Set up continuous memory monitoring -2. **Baseline Establishment** - Know your application's normal memory patterns -3. **Gradual Analysis** - Start with high-level views, then drill down -4. **Consider Context** - Memory usage often correlates with workload -5. **Clean Up** - Regularly analyze and clean up unnecessary objects - -## Integration with Development Workflow - -### Pre-deployment Memory Checks - -Before deploying, analyze memory patterns using available data: -```bash -# Check for GPU memory growth during recent training -probing $ENDPOINT query " - SELECT - max(allocated) - min(allocated) as memory_growth_mb, - max(allocated) as peak_memory_mb, - count(DISTINCT step) as steps_analyzed - FROM python.torch_trace - WHERE step > (SELECT max(step) - 100 FROM python.torch_trace) - AND allocated > 0 -" - -# Also check Python memory usage -probing $ENDPOINT eval " -import psutil - -process = psutil.Process() -rss_mb = process.memory_info().rss / 1024 / 1024 - -print(f'Memory analysis complete:') -print(f' RSS Memory: {rss_mb:.1f} MB') -print(f' Memory Percent: {process.memory_percent():.1f}%') - -# Basic memory health check -if rss_mb > 8000: # > 8GB - print(' Status: HIGH MEMORY USAGE') -elif rss_mb > 4000: # > 4GB - print(' Status: MODERATE MEMORY USAGE') -else: - print(' Status: NORMAL MEMORY USAGE') -" -``` - -### Performance Regression Detection - -Compare memory usage between different training runs: -```bash -# Export current PyTorch memory profile -probing $ENDPOINT query " - SELECT - module, - stage, - avg(allocated) as avg_memory_mb, - max(allocated) as peak_memory_mb - FROM python.torch_trace - WHERE step > (SELECT max(step) - 100 FROM python.torch_trace) - GROUP BY module, stage -" > current_memory_profile.json - -# Compare with previous runs by analyzing torch trace patterns -probing $ENDPOINT query " - SELECT - step % 100 as relative_step, - avg(allocated) as avg_memory_mb, - stddev(allocated) as memory_variance - FROM python.torch_trace - WHERE step > (SELECT max(step) - 200 FROM python.torch_trace) - GROUP BY relative_step - ORDER BY relative_step -" > memory_pattern_analysis.json -``` - -For more detailed analysis techniques, see [SQL Analytics](sql-analytics.md) and [Basic Usage](basic-usage.md). diff --git a/docs/user-guide/sql-analytics.md b/docs/user-guide/sql-analytics.md deleted file mode 100644 index bb787819..00000000 --- a/docs/user-guide/sql-analytics.md +++ /dev/null @@ -1,389 +0,0 @@ -# SQL Analytics Interface - -Probing provides a powerful SQL interface for analyzing performance and monitoring data. This allows you to use familiar SQL syntax to query real-time and historical data from your applications. - -## Overview - -The SQL analytics interface transforms complex performance analysis into intuitive database queries. All monitoring data is accessible through standard SQL operations including `SELECT`, `WHERE`, `GROUP BY`, `ORDER BY`, and advanced analytical functions. - -## Basic Query Structure - -```sql -probing $ENDPOINT query "SELECT columns FROM table WHERE conditions" -``` - -## Core Tables - -### Configuration and Metadata - -**`information_schema.df_settings`** - System configuration and settings -```sql -SELECT * FROM information_schema.df_settings -WHERE name LIKE 'probing.%'; -``` - -Common columns: -- `name` - Configuration parameter name -- `value` - Configuration parameter value - -### Python Namespace Tables - -**`python.backtrace`** - Stack trace information -```sql -SELECT * FROM python.backtrace LIMIT 10; -``` - -Common columns: -- `ip` - Instruction pointer (for native frames) -- `file` - Source file name -- `func` - Function name -- `lineno` - Line number -- `depth` - Stack depth -- `frame_type` - Frame type ('Python' or 'Native') - -### Dynamic External Tables - -External tables can be created dynamically through the Python API: -```sql --- Example: Query a custom external table -SELECT * FROM python.my_custom_table; -``` - -## PyTorch Integration - -When monitoring PyTorch applications with the `@table` decorator, additional tables become available: - -**`python.torch_trace`** - PyTorch execution traces -```sql -SELECT step, module, stage, duration, allocated -FROM python.torch_trace -WHERE step >= 5 -ORDER BY step DESC, seq; -``` - -Common columns: -- `step` - Training step number -- `seq` - Sequence number within step -- `module` - Module name -- `stage` - Execution stage (forward, backward, step) -- `allocated` - GPU memory allocated (MB) -- `max_allocated` - Peak GPU memory allocated (MB) -- `cached` - GPU memory cached (MB) -- `max_cached` - Peak GPU memory cached (MB) -- `time_offset` - Time offset -- `duration` - Execution duration (seconds) - -**`python.variables`** - Variable tracking -```sql -SELECT step, func, name, value -FROM python.variables -WHERE step = (SELECT max(step) FROM python.variables); -``` - -Common columns: -- `step` - Training step number -- `func` - Function name -- `name` - Variable name -- `value` - Variable value (string representation) - -## Advanced Analytics - -### Time-Series Analysis - -**Memory growth over time (using torch_trace):** -```sql -SELECT - step, - stage, - avg(allocated) as avg_memory_mb, - max(allocated) as peak_memory_mb -FROM python.torch_trace -WHERE step > (SELECT max(step) - 10 FROM python.torch_trace) -GROUP BY step, stage -ORDER BY step, stage; -``` - -**Rolling averages:** -```sql -SELECT - step, - module, - duration, - AVG(duration) OVER ( - PARTITION BY module - ORDER BY step, seq - ROWS BETWEEN 4 PRECEDING AND CURRENT ROW - ) as avg_duration_5_samples -FROM python.torch_trace -WHERE step > (SELECT max(step) - 5 FROM python.torch_trace); -``` - -### Performance Analysis - -**Top slowest operations:** -```sql -SELECT - module, - stage, - count(*) as execution_count, - avg(duration) as avg_duration, - max(duration) as max_duration, - stddev(duration) as duration_stddev -FROM python.torch_trace -WHERE step > (SELECT max(step) - 10 FROM python.torch_trace) - AND duration > 0 -GROUP BY module, stage -HAVING count(*) > 5 -ORDER BY avg_duration DESC -LIMIT 10; -``` - -**Execution patterns:** -```sql -SELECT - step, - stage, - count(*) as operations_per_step -FROM python.torch_trace -WHERE step > (SELECT max(step) - 5 FROM python.torch_trace) -GROUP BY step, stage -ORDER BY step DESC, operations_per_step DESC; -``` - -### Training Progress Analysis - -**Memory usage trends during training:** -```sql -SELECT - step, - avg(allocated) as avg_memory_allocated, - max(allocated) as peak_memory_allocated, - min(allocated) as min_memory_allocated -FROM python.torch_trace -WHERE step IS NOT NULL -GROUP BY step -ORDER BY step; -``` - -**Module execution time analysis:** -```sql -SELECT - module, - stage, - avg(duration) as avg_duration, - count(*) as execution_count -FROM python.torch_trace -WHERE module IS NOT NULL - AND duration > 0 -GROUP BY module, stage -ORDER BY avg_duration DESC; -``` - -## Data Filtering and Conditions - -### Time-based Filtering - -```sql --- Recent steps only -WHERE step > (SELECT max(step) - 10 FROM python.torch_trace) - --- Specific step range -WHERE step BETWEEN 5 AND 15 - --- Latest data only -WHERE step = (SELECT max(step) FROM python.torch_trace) -``` - -### Performance Filtering - -```sql --- Long-running operations -WHERE duration > 0.1 - --- Memory-intensive operations -WHERE allocated > 1000 -- MB - --- Specific execution stages -WHERE stage IN ('forward', 'backward') - --- Specific modules -WHERE module LIKE '%attention%' -``` - -## Aggregation Functions - -### Statistical Functions - -```sql -SELECT - module, - stage, - count(*) as total_executions, - avg(duration) as mean_duration, - percentile_cont(0.5) WITHIN GROUP (ORDER BY duration) as median_duration, - percentile_cont(0.95) WITHIN GROUP (ORDER BY duration) as p95_duration, - min(duration) as min_duration, - max(duration) as max_duration, - stddev(duration) as std_duration -FROM python.torch_trace -WHERE duration > 0 -GROUP BY module, stage; -``` - -### Window Functions - -```sql -SELECT - step, - allocated, - LAG(allocated) OVER (ORDER BY step, seq) as prev_memory, - LEAD(allocated) OVER (ORDER BY step, seq) as next_memory, - ROW_NUMBER() OVER (ORDER BY allocated DESC) as memory_rank -FROM python.torch_trace -WHERE step > (SELECT max(step) - 5 FROM python.torch_trace); -``` - -## Cross-Table Joins - -**Correlate torch traces with variable tracking:** -```sql -SELECT - t.step, - t.module, - t.duration, - v.name as variable_name, - v.value as variable_value -FROM python.torch_trace t -JOIN python.variables v ON t.step = v.step -WHERE t.step > (SELECT max(step) - 3 FROM python.torch_trace) - AND t.duration > 0.05; -``` - -## Configuration Tables - -### View Current Settings - -```sql -SELECT * FROM information_schema.df_settings -WHERE name LIKE 'probing.%'; -``` - -### System Configuration - -```sql -SELECT * FROM information_schema.df_settings -WHERE name LIKE 'server.%' OR name LIKE 'torch.%'; -``` - -## Real-time Monitoring Queries - -### Dashboard Queries - -**Current training status:** -```sql -SELECT - 'Current Step' as metric, - max(step) as value, - '' as unit -FROM python.torch_trace -UNION ALL -SELECT - 'Peak Memory Usage', - max(allocated), - 'MB' -FROM python.torch_trace -WHERE step = (SELECT max(step) FROM python.torch_trace); -``` - -**Training progress:** -```sql -SELECT - step, - count(*) as total_operations, - avg(duration) as avg_duration, - max(allocated) as peak_memory_mb -FROM python.torch_trace -WHERE step > (SELECT max(step) - 5 FROM python.torch_trace) -GROUP BY step -ORDER BY step DESC -LIMIT 5; -``` - -## Export and Integration - -### Data Export - -Results can be exported for further analysis: - -```bash -# Export to JSON -probing $ENDPOINT query "SELECT * FROM python.torch_trace" > torch_traces.json - -# Time-series data for plotting -probing $ENDPOINT query " - SELECT step, stage, avg(duration), avg(allocated) - FROM python.torch_trace - WHERE step > (SELECT max(step) - 10 FROM python.torch_trace) - GROUP BY step, stage -" > training_metrics.json -``` - -### Integration with Other Tools - -The SQL interface makes it easy to integrate with monitoring and visualization tools: - -- Export data for Grafana dashboards -- Feed metrics into alerting systems -- Generate reports for analysis notebooks - -## Best Practices - -1. **Use step-based filtering** - Always include step constraints for better performance -2. **Limit result sets** - Use `LIMIT` clauses for large datasets -3. **Index-friendly queries** - Leverage step and module columns -4. **Aggregate appropriately** - Use `GROUP BY` for summary statistics -5. **Test queries incrementally** - Start simple and add complexity gradually - -## Common Query Patterns - -### Performance Regression Detection - -```sql -SELECT - module, - stage, - avg(duration) as current_avg, - LAG(avg(duration)) OVER (ORDER BY step) as prev_avg -FROM python.torch_trace -WHERE step > (SELECT max(step) - 10 FROM python.torch_trace) -GROUP BY module, stage, step -HAVING avg(duration) > LAG(avg(duration)) OVER (ORDER BY step) * 1.2; -``` - -### Memory Usage Growth Detection - -```sql -SELECT - step, - max(allocated) - min(allocated) as memory_growth_mb -FROM python.torch_trace -WHERE step > (SELECT max(step) - 5 FROM python.torch_trace) -GROUP BY step -HAVING max(allocated) - min(allocated) > 50 -ORDER BY step; -``` - -### Error Rate Analysis - -For custom external tables with error tracking: -```sql -SELECT - step, - count(*) FILTER (WHERE error_count > 0) * 100.0 / count(*) as error_rate_percent -FROM python.my_error_table -WHERE step > (SELECT max(step) - 20 FROM python.my_error_table) -GROUP BY step -ORDER BY step; -``` - -For more advanced usage patterns, see [Basic Usage](basic-usage.md) and [Memory Analysis](memory-analysis.md). diff --git a/docs/user-guide/troubleshooting.md b/docs/user-guide/troubleshooting.md deleted file mode 100644 index e69de29b..00000000 diff --git a/examples/ray_tracing_example.py b/examples/ray_tracing_example.py index 0eb5eb1f..4e5c64ba 100644 --- a/examples/ray_tracing_example.py +++ b/examples/ray_tracing_example.py @@ -72,9 +72,10 @@ def get_value(self): # Query tracing data using probing print("\nQuerying trace data...") try: - import probing from probing.ext.ray import get_ray_timeline, get_ray_timeline_chrome_format + import probing + # Query for Ray task spans task_spans = probing.query("SELECT * FROM TraceEvent WHERE name LIKE 'ray.task.%'") print(f"Found {len(task_spans)} task spans") diff --git a/ruff.toml b/ruff.toml new file mode 100644 index 00000000..c4985bea --- /dev/null +++ b/ruff.toml @@ -0,0 +1,6 @@ +[lint] +# Enable Pyflakes (`F`) and a subset of the pycodestyle (`E`) codes by default. +# Unlike Flake8, Ruff doesn't enable pycodestyle warnings (`W`) or +# McCabe complexity (`C901`) by default. +select = ["E4", "E7", "E9", "F"] +ignore = ["F401", "F841", "E402", "E721", "E722", "E731"] diff --git a/tests/repl/test_help_magic.py b/tests/repl/test_help_magic.py index 6cc4c42f..6203c1cd 100644 --- a/tests/repl/test_help_magic.py +++ b/tests/repl/test_help_magic.py @@ -7,7 +7,6 @@ sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "..", "python")) import pytest - from probing.repl import CodeExecutor diff --git a/tests/repl/test_query_magic.py b/tests/repl/test_query_magic.py index d0cb5f37..301908a1 100644 --- a/tests/repl/test_query_magic.py +++ b/tests/repl/test_query_magic.py @@ -12,9 +12,8 @@ if python_dir not in sys.path: sys.path.insert(0, python_dir) -from traitlets.config.configurable import Configurable - from probing.repl.query_magic import QueryMagic +from traitlets.config.configurable import Configurable @pytest.fixture