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dependencies - click, - datasets, - ftfy, - loguru, - numpy, - pillow, - pydantic, - pydantic_settings, - pyyaml, - respx, - rich, - setuptools, - setuptools-git-versioning, - transformers, - - # dev dependencies - pytest, - pydantic_settings, - - # types - types-click, - types-PyYAML, - types-requests, - types-toml, - ] diff --git a/404.html b/404.html new file mode 100644 index 00000000..08ca73f1 --- /dev/null +++ b/404.html @@ -0,0 +1,71 @@ + + + + + + Page Not Found | GuideLLM + + + +
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+ + diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md deleted file mode 100644 index 5708a3fc..00000000 --- a/CODE_OF_CONDUCT.md +++ /dev/null @@ -1,77 +0,0 @@ -# GuideLLM Code of Conduct - -## Our Pledge - -We as members, contributors, and leaders pledge to make participation in our community a harassment-free experience for everyone, regardless of age, body size, visible or invisible disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation. - -We pledge to act and interact in ways that contribute to an open, welcoming, diverse, inclusive, and healthy community. - -## Our Standards - -Examples of behavior that contributes to a positive environment for our community include: - -- Demonstrating empathy and kindness toward other people -- Being respectful of differing opinions, viewpoints, and experiences -- Giving and gracefully accepting constructive feedback -- Accepting responsibility and apologizing to those affected by our mistakes, and learning from the experience -- Focusing on what is best not just for us as individuals, but for the overall community - -Examples of unacceptable behavior include: - -- The use of sexualized language or imagery, and sexual attention or advances of any kind -- Trolling, insulting or derogatory comments, and personal or political attacks -- Public or private harassment -- Publishing others’ private information, such as a physical or email address, without their explicit permission -- Other conduct which could reasonably be considered inappropriate in a professional setting - -## Enforcement Responsibilities - -Community leaders are responsible for clarifying and enforcing our standards of acceptable behavior and will take appropriate and fair corrective action in response to any behavior that they deem inappropriate, threatening, offensive, or harmful. - -Community leaders have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, and will communicate reasons for moderation decisions when appropriate. - -## Scope - -This Code of Conduct applies within all community spaces, and also applies when an individual is officially representing the community in public spaces. Examples of representing our community include using an official e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. - -## Enforcement - -Instances of abusive, harassing, or otherwise unacceptable behavior may be reported to the community leaders responsible for enforcement through GitHub, Slack, or Email. All complaints will be reviewed and investigated promptly and fairly. - -All community leaders are obligated to respect the privacy and security of the reporter of any incident. - -## Enforcement Guidelines - -Community leaders will follow these Community Impact Guidelines in determining the consequences for any action they deem in violation of this Code of Conduct: - -### 1. Correction - -**Community Impact**: Use of inappropriate language or other behavior deemed unprofessional or unwelcome in the community. - -**Consequence**: A private, written warning from community leaders, providing clarity around the nature of the violation and an explanation of why the behavior was inappropriate. A public apology may be requested. - -### 2. Warning - -**Community Impact**: A violation through a single incident or series of actions. - -**Consequence**: A warning with consequences for continued behavior. No interaction with the people involved, including unsolicited interaction with those enforcing the Code of Conduct, for a specified period of time. This includes avoiding interactions in community spaces as well as external channels like social media. Violating these terms may lead to a temporary or permanent ban. - -### 3. Temporary Ban - -**Community Impact**: A serious violation of community standards, including sustained inappropriate behavior. - -**Consequence**: A temporary ban from any sort of interaction or public communication with the community for a specified period of time. No public or private interaction with the people involved, including unsolicited interaction with those enforcing the Code of Conduct, is allowed during this period. Violating these terms may lead to a permanent ban. - -### 4. Permanent Ban - -**Community Impact**: Demonstrating a pattern of violation of community standards, including sustained inappropriate behavior, harassment of an individual, or aggression toward or disparagement of classes of individuals. - -**Consequence**: A permanent ban from any sort of public interaction within the community. - -## Attribution - -This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 2.1, available at https://www.contributor-covenant.org/version/2/1/code_of_conduct.html. - -Community Impact Guidelines were inspired by [Mozilla’s code of conduct enforcement ladder](https://github.com/mozilla/diversity). - -For answers to common questions about this code of conduct, see the FAQ at https://www.contributor-covenant.org/faq. Translations are available at https://www.contributor-covenant.org/translations. diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md deleted file mode 100644 index 5d3369b0..00000000 --- a/CONTRIBUTING.md +++ /dev/null @@ -1,121 +0,0 @@ -# Contributing to GuideLLM - -Thank you for considering contributing to GuideLLM! We welcome contributions from the community to help improve and grow this project. This document outlines the process and guidelines for contributing. - -## How Can You Contribute? - -There are many ways to contribute to GuideLLM: - -- **Reporting Bugs**: If you encounter a bug, please let us know by creating an issue. -- **Suggesting Features**: Have an idea for a new feature? Open an issue to discuss it. -- **Improving Documentation**: Help us improve our documentation by submitting pull requests. -- **Writing Code**: Contribute code to fix bugs, add features, or improve performance. -- **Reviewing Pull Requests**: Provide feedback on open pull requests to help maintain code quality. - -## Getting Started - -### Prerequisites - -Before contributing, ensure you have the following installed: - -- Python 3.9 or higher -- pip (Python package manager) -- Tox -- Git - -### Setting Up the Repository - -You can either clone the repository directly or fork it if you plan to contribute changes back: - -#### Option 1: Cloning the Repository - -1. Clone the repository to your local machine: - - ```bash - git clone https://github.com/neuralmagic/guidellm.git - cd guidellm - ``` - -#### Option 2: Forking the Repository - -1. Fork the repository by clicking the "Fork" button on the repository's GitHub page. - -2. Clone your forked repository to your local machine: - - ```bash - git clone https://github.com//guidellm.git - cd guidellm - ``` - -For detailed instructions on setting up your development environment, please refer to the [DEVELOPING.md](https://github.com/neuralmagic/speculators/blob/main/DEVELOPING.md) file. It includes step-by-step guidance on: - -- Installing dependencies -- Running tests -- Using Tox for various tasks - -## Code Style and Guidelines - -We follow strict coding standards to ensure code quality and maintainability. Please adhere to the following guidelines: - -- **Code Style**: Use [Black](https://black.readthedocs.io/en/stable/) for code formatting and [Ruff](https://github.com/charliermarsh/ruff) for linting. -- **Type Checking**: Use [Mypy](http://mypy-lang.org/) for type checking. -- **Testing**: Write unit tests for new features and bug fixes. Use [pytest](https://docs.pytest.org/) for testing. -- **Documentation**: Update documentation for any changes to the codebase. - -To check code quality locally, use the following Tox environment: - -```bash -tox -e quality -``` - -To automatically fix style issues, use: - -```bash -tox -e style -``` - -To run type checks, use: - -```bash -tox -e types -``` - -## Submitting Changes - -1. **Create a Branch**: Create a new branch for your changes: - - ```bash - git checkout -b feature/your-feature-name - ``` - -2. **Make Changes**: Commit your changes with clear and descriptive commit messages. - -3. **Run Tests and Quality Checks**: Before submitting your changes, ensure all tests pass and code quality checks are satisfied: - - ```bash - tox - ``` - -4. **Push Changes**: Push your branch to your forked repository (if you forked): - - ```bash - git push origin feature/your-feature-name - ``` - -5. **Open a Pull Request**: Go to the original repository and open a pull request. Provide a clear description of your changes and link any related issues. - -## Reporting Issues - -If you encounter a bug or have a feature request, please open an issue on GitHub. Include as much detail as possible, such as: - -- Steps to reproduce the issue -- Expected and actual behavior -- Environment details (OS, Python version, etc.) - -## Community Standards - -We are committed to fostering a welcoming and inclusive community. Please read and adhere to our [Code of Conduct](https://github.com/neuralmagic/speculators/blob/main/CODE_OF_CONDUCT.md). - -## License - -By contributing to Speculators, you agree that your contributions will be licensed under the [Apache License 2.0](https://github.com/neuralmagic/speculators/blob/main/LICENSE). diff --git a/DEVELOPING.md b/DEVELOPING.md deleted file mode 100644 index d91dc93d..00000000 --- a/DEVELOPING.md +++ /dev/null @@ -1,187 +0,0 @@ -# Developing for Speculators - -Thank you for your interest in contributing to Speculators! This document provides detailed instructions for setting up your development environment, implementing changes, and adhering to the project's best practices. Your contributions help us grow and improve this project. - -## Setting Up Your Development Environment - -### Prerequisites - -Before you begin, ensure you have the following installed: - -- Python 3.9 or higher -- pip (Python package manager) -- Tox -- Git - -### Cloning the Repository - -1. Clone the repository to your local machine: - - ```bash - git clone https://github.com/neuralmagic/guidellm.git - cd guidellm - ``` - -2. (Optional) If you plan to contribute changes back, fork the repository and clone your fork instead: - - ```bash - git clone https://github.com//guidellm.git - cd guidellm - ``` - -### Installing Dependencies - -To install the required dependencies for the package and development, run: - -```bash -pip install -e ./[dev] -``` - -The `-e` flag installs the package in editable mode, allowing you to make changes to the code without reinstalling it. The `[dev]` part installs additional dependencies needed for development, such as testing and linting tools. - -## Implementing Changes - -### Writing Code - -1. **Create a Branch**: Create a new branch for your changes: - - ```bash - git checkout -b feature/your-feature-name - ``` - -2. **Make Changes**: Implement your changes in the appropriate files. Ensure that all public functions and classes have clear and concise docstrings. - -3. **Update Documentation**: Update or add documentation to reflect your changes. This includes updating README files, docstrings, and any relevant guides. - -## Running Quality, Style, and Type Checks - -We use Tox to simplify running various tasks in isolated environments. Tox standardizes environments to ensure consistency across local development, CI/CD pipelines, and releases. This guarantees that the code behaves the same regardless of where it is executed. - -Additionally, to ensure consistency and quality of the codebase, we use [ruff](https://github.com/astral-sh/ruff) for linting and styling, [isort](https://pycqa.github.io/isort/) for sorting imports, [mypy](https://github.com/python/mypy) for type checking, and [mdformat](https://github.com/hukkin/mdformat) for formatting Markdown files. - -### Code Quality and Style - -To check code quality, including linting and formatting: - -```bash -tox -e quality -``` - -To automatically fix style issues: - -```bash -tox -e style -``` - -### Type Checking - -To ensure type safety using Mypy: - -```bash -tox -e types -``` - -### Link Checking - -To ensure valid links added to the documentation / Markdown files: - -```bash -tox -e links -``` - -### Automating Quality Checks with Pre-Commit Hooks (Optional) - -We use [pre-commit](https://pre-commit.com/) to automate quality checks before commits. Pre-commit hooks run checks like linting, formatting, and type checking, ensuring that only high-quality code is committed. - -To install the pre-commit hooks, run: - -```bash -pre-commit install -``` - -This will set up the hooks to run automatically before each commit. To manually run the hooks on all files, use: - -```bash -pre-commit run --all-files -``` - -## Running Tests - -For testing, we use [pytest](https://docs.pytest.org/) as our testing framework. We have different test suites for unit tests, integration tests, and end-to-end tests. To run the tests, you can use Tox, which will automatically create isolated environments for each test suite. Tox will also ensure that the tests are run in a consistent environment, regardless of where they are executed. - -### Running All Tests - -To run all tests: - -```bash -tox -``` - -### Running Specific Tests - -- Unit tests (focused on individual components with mocking): - - ```bash - tox -e test-unit - ``` - -- Integration tests (focused on interactions between components ideally without mocking): - - ```bash - tox -e test-integration - ``` - -- End-to-end tests (focused on the entire system and user interfaces): - - ```bash - tox -e test-e2e - ``` - -### Running Tests with Coverage - -To ensure your changes are covered by tests, run: - -```bash -tox -e test-unit -- --cov=speculators --cov-report=html -``` - -Review the coverage report to confirm that your new code is adequately tested. - -## Opening a Pull Request - -1. **Push Changes**: Push your branch to your forked repository (if you forked): - - ```bash - git push origin feature/your-feature-name - ``` - -2. **Open a Pull Request**: Go to the original repository and open a pull request. Use the following template for your pull request description: - - ```markdown - # Title; ex: Add feature X to improve Y - - ## Summary: - - Short paragraph detailing the pull request changes and reasoning in addition to any relevant context. - - ## Details: - - - Detailed list of changes made in the pull request - - ## Test Plan: - - - Detailed list of steps to test the changes made in the pull request - - ## Related Issues - - - List of related issues or other pull requests; ex: "Fixes #1234" - ``` - -3. **Address Feedback**: Respond to any feedback from reviewers and make necessary changes. - -## Additional Resources - -- [CONTRIBUTING.md](https://github.com/neuralmagic/speculators/blob/main/CONTRIBUTING.md): Guidelines for contributing to the project. -- [CODE_OF_CONDUCT.md](https://github.com/neuralmagic/speculators/blob/main/CODE_OF_CONDUCT.md): Our expectations for community behavior. -- [tox.ini](https://github.com/neuralmagic/speculators/blob/main/tox.ini): Configuration for Tox environments. -- [.pre-commit-config.yaml](https://github.com/neuralmagic/speculators/blob/main/.pre-commit-config.yaml): Configuration for pre-commit hooks. diff --git a/LICENSE b/LICENSE deleted file mode 100644 index 261eeb9e..00000000 --- a/LICENSE +++ /dev/null @@ -1,201 +0,0 @@ - Apache License - Version 2.0, January 2004 - http://www.apache.org/licenses/ - - TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION - - 1. 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- - - GuideLLM Logo - -

- -

-Scale Efficiently: Evaluate and Optimize Your LLM Deployments for Real-World Inference -

- -[![GitHub Release](https://img.shields.io/github/release/neuralmagic/guidellm.svg?label=Version)](https://github.com/neuralmagic/guidellm/releases) [![Documentation](https://img.shields.io/badge/Documentation-8A2BE2?logo=read-the-docs&logoColor=%23ffffff&color=%231BC070)](https://github.com/neuralmagic/guidellm/tree/main/docs) [![License](https://img.shields.io/github/license/neuralmagic/guidellm.svg)](https://github.com/neuralmagic/guidellm/blob/main/LICENSE) [![PyPI Release](https://img.shields.io/pypi/v/guidellm.svg?label=PyPI%20Release)](https://pypi.python.org/pypi/guidellm) [![Python Versions](https://img.shields.io/badge/Python-3.9--3.13-orange)](https://pypi.python.org/pypi/guidellm) [![Nightly Build](https://img.shields.io/github/actions/workflow/status/neuralmagic/guidellm/nightly.yml?branch=main&label=Nightly%20Build)](https://github.com/neuralmagic/guidellm/actions/workflows/nightly.yml) - -## Overview - -

- - - GuideLLM User Flows - -

- -**GuideLLM** is a platform for evaluating and optimizing the deployment of large language models (LLMs). By simulating real-world inference workloads, GuideLLM enables users to assess the performance, resource requirements, and cost implications of deploying LLMs on various hardware configurations. This approach ensures efficient, scalable, and cost-effective LLM inference serving while maintaining high service quality. - -### Key Features - -- **Performance Evaluation:** Analyze LLM inference under different load scenarios to ensure your system meets your service level objectives (SLOs). -- **Resource Optimization:** Determine the most suitable hardware configurations for running your models effectively. -- **Cost Estimation:** Understand the financial impact of different deployment strategies and make informed decisions to minimize costs. -- **Scalability Testing:** Simulate scaling to handle large numbers of concurrent users without performance degradation. - -## Getting Started - -### Installation - -Before installing, ensure you have the following prerequisites: - -- OS: Linux or MacOS -- Python: 3.9 – 3.13 - -The latest GuideLLM release can be installed using pip: - -```bash -pip install guidellm -``` - -Or from source code using pip: - -```bash -pip install git+https://github.com/neuralmagic/guidellm.git -``` - -For detailed installation instructions and requirements, see the [Installation Guide](https://github.com/neuralmagic/guidellm/blob/main/docs/install.md). - -### Quick Start - -#### 1. Start an OpenAI Compatible Server (vLLM) - -GuideLLM requires an OpenAI-compatible server to run evaluations. [vLLM](https://github.com/vllm-project/vllm) is recommended for this purpose. After installing vLLM on your desired server (`pip install vllm`), start a vLLM server with a Llama 3.1 8B quantized model by running the following command: - -```bash -vllm serve "neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16" -``` - -For more information on starting a vLLM server, see the [vLLM Documentation](https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html). - -For information on starting other supported inference servers or platforms, see the [Supported Backends documentation](https://github.com/neuralmagic/guidellm/blob/main/docs/backends.md). - -#### 2. Run a GuideLLM Benchmark - -To run a GuideLLM benchmark, use the `guidellm benchmark` command with the target set to an OpenAI-compatible server. For this example, the target is set to 'http://localhost:8000', assuming that vLLM is active and running on the same server. Otherwise, update it to the appropriate location. By default, GuideLLM automatically determines the model available on the server and uses it. To target a different model, pass the desired name with the `--model` argument. Additionally, the `--rate-type` is set to `sweep`, which automatically runs a range of benchmarks to determine the minimum and maximum rates that the server and model can support. Each benchmark run under the sweep will run for 30 seconds, as set by the `--max-seconds` argument. Finally, `--data` is set to a synthetic dataset with 256 prompt tokens and 128 output tokens per request. For more arguments, supported scenarios, and configurations, jump to the [Configurations Section](#configurations) or run `guidellm benchmark --help`. - -Now, to start benchmarking, run the following command: - -```bash -guidellm benchmark \ - --target "http://localhost:8000" \ - --rate-type sweep \ - --max-seconds 30 \ - --data "prompt_tokens=256,output_tokens=128" -``` - -The above command will begin the evaluation and provide progress updates similar to the following: - -#### 3. Analyze the Results - -After the evaluation is completed, GuideLLM will summarize the results into three sections: - -1. Benchmarks Metadata: A summary of the benchmark run and the arguments used to create it, including the server, data, profile, and more. -2. Benchmarks Info: A high-level view of each benchmark and the requests that were run, including the type, duration, request statuses, and number of tokens. -3. Benchmarks Stats: A summary of the statistics for each benchmark run, including the request rate, concurrency, latency, and token-level metrics such as TTFT, ITL, and more. - -The sections will look similar to the following: Sample GuideLLM benchmark output - -For more details about the metrics and definitions, please refer to the [Metrics documentation](https://raw.githubusercontent.com/neuralmagic/guidellm/main/docs/metrics.md). - -#### 4. Explore the Results File - -By default, the full results, including complete statistics and request data, are saved to a file `benchmarks.json` in the current working directory. This file can be used for further analysis or reporting, and additionally can be reloaded into Python for further analysis using the `guidellm.benchmark.GenerativeBenchmarksReport` class. You can specify a different file name and extension with the `--output` argument. - -For more details about the supported output file types, please take a look at the [Outputs documentation](https://github.com/neuralmagic/guidellm/blob/main/docs/outputs.md). - -#### 5. Use the Results - -The results from GuideLLM are used to optimize your LLM deployment for performance, resource efficiency, and cost. By analyzing the performance metrics, you can identify bottlenecks, determine the optimal request rate, and select the most cost-effective hardware configuration for your deployment. - -For example, when deploying a chat application, we likely want to ensure that our time to first token (TTFT) and inter-token latency (ITL) are under certain thresholds to meet our service level objectives (SLOs) or service level agreements (SLAs). For example, setting TTFT to 200ms and ITL 25ms for the sample data provided in the example above, we can see that even though the server is capable of handling up to 13 requests per second, we would only be able to meet our SLOs for 99% of users at a request rate of 3.5 requests per second. If we relax our constraints on ITL to 50 ms, then we can meet the TTFT SLA for 99% of users at a request rate of approximately 10 requests per second. - -For further details on determining the optimal request rate and SLOs, refer to the [SLOs documentation](https://raw.githubusercontent.com/neuralmagic/guidellm/main/docs/service_level_objectives.md). - -### Configurations - -GuideLLM offers a range of configurations through both the benchmark CLI command and environment variables, which provide default values and more granular controls. The most common configurations are listed below. A complete list is easily accessible, though, by running `guidellm benchmark --help` or `guidellm config` respectively. - -#### Benchmark CLI - -The `guidellm benchmark` command is used to run benchmarks against a generative AI backend/server. The command accepts a variety of arguments to customize the benchmark run. The most common arguments include: - -- `--target`: Specifies the target path for the backend to run benchmarks against. For example, `http://localhost:8000`. This is required to define the server endpoint. - -- `--model`: Allows selecting a specific model from the server. If not provided, it defaults to the first model available on the server. Useful when multiple models are hosted on the same server. - -- `--processor`: Used only for synthetic data creation or when the token source configuration is set to local for calculating token metrics locally. It must match the model's processor or tokenizer to ensure compatibility and correctness. This supports either a HuggingFace model ID or a local path to a processor or tokenizer. - -- `--data`: Specifies the dataset to use. This can be a HuggingFace dataset ID, a local path to a dataset, or standard text files such as CSV, JSONL, and more. Additionally, synthetic data configurations can be provided using JSON or key-value strings. Synthetic data options include: - - - `prompt_tokens`: Average number of tokens for prompts. - - `output_tokens`: Average number of tokens for outputs. - - `TYPE_stdev`, `TYPE_min`, `TYPE_max`: Standard deviation, minimum, and maximum values for the specified type (e.g., `prompt_tokens`, `output_tokens`). If not provided, will use the provided tokens value only. - - `samples`: Number of samples to generate, defaults to 1000. - - `source`: Source text data for generation, defaults to a local copy of Pride and Prejudice. - -- `--data-args`: A JSON string used to specify the columns to source data from (e.g., `prompt_column`, `output_tokens_count_column`) and additional arguments to pass into the HuggingFace datasets constructor. - -- `--data-sampler`: Enables applying `random` shuffling or sampling to the dataset. If not set, no sampling is used. - -- `--rate-type`: Defines the type of benchmark to run (default sweep). Supported types include: - - - `synchronous`: Runs a single stream of requests one at a time. `--rate` must not be set for this mode. - - `throughput`: Runs all requests in parallel to measure the maximum throughput for the server (bounded by GUIDELLM\_\_MAX_CONCURRENCY config argument). `--rate` must not be set for this mode. - - `concurrent`: Runs a fixed number of streams of requests in parallel. `--rate` must be set to the desired concurrency level/number of streams. - - `constant`: Sends requests asynchronously at a constant rate set by `--rate`. - - `poisson`: Sends requests at a rate following a Poisson distribution with the mean set by `--rate`. - - `sweep`: Automatically determines the minimum and maximum rates the server can support by running synchronous and throughput benchmarks, and then runs a series of benchmarks equally spaced between the two rates. The number of benchmarks is set by `--rate` (default is 10). - -- `--max-seconds`: Sets the maximum duration (in seconds) for each benchmark run. If not specified, the benchmark will run until the dataset is exhausted or the `--max-requests` limit is reached. - -- `--max-requests`: Sets the maximum number of requests for each benchmark run. If not provided, the benchmark will run until `--max-seconds` is reached or the dataset is exhausted. - -- `--warmup-percent`: Specifies the percentage of the benchmark to treat as a warmup phase. Requests during this phase are excluded from the final results. - -- `--cooldown-percent`: Specifies the percentage of the benchmark to treat as a cooldown phase. Requests during this phase are excluded from the final results. - -- `--output-path`: Defines the path to save the benchmark results. Supports JSON, YAML, or CSV formats. If a directory is provided, the results will be saved as `benchmarks.json` in that directory. If not set, the results will be saved in the current working directory. - -## Resources - -### Documentation - -Our comprehensive documentation offers detailed guides and resources to help you maximize the benefits of GuideLLM. Whether just getting started or looking to dive deeper into advanced topics, you can find what you need in our [documentation](https://github.com/neuralmagic/guidellm/blob/main/docs). - -### Core Docs - -- [**Installation Guide**](https://github.com/neuralmagic/guidellm/blob/main/docs/install.md) - This guide provides step-by-step instructions for installing GuideLLM, including prerequisites and setup tips. -- [**Backends Guide**](https://github.com/neuralmagic/guidellm/blob/main/docs/backends.md) - A comprehensive overview of supported backends and how to set them up for use with GuideLLM. -- [**Data/Datasets Guide**](https://github.com/neuralmagic/guidellm/blob/main/docs/datasets.md) - Information on supported datasets, including how to use them for benchmarking. -- [**Metrics Guide**](https://github.com/neuralmagic/guidellm/blob/main/docs/metrics.md) - Detailed explanations of the metrics used in GuideLLM, including definitions and how to interpret them. -- [**Outputs Guide**](https://github.com/neuralmagic/guidellm/blob/main/docs/outputs.md) - Information on the different output formats supported by GuideLLM and how to use them. -- [**Architecture Overview**](https://github.com/neuralmagic/guidellm/blob/main/docs/architecture.md) - A detailed look at GuideLLM's design, components, and how they interact. - -### Supporting External Documentation - -- [**vLLM Documentation**](https://vllm.readthedocs.io/en/latest/) - Official vLLM documentation provides insights into installation, usage, and supported models. - -### Contribution Docs - -We appreciate contributions to the code, examples, integrations, documentation, bug reports, and feature requests! Your feedback and involvement are crucial in helping GuideLLM grow and improve. Below are some ways you can get involved: - -- [**DEVELOPING.md**](https://github.com/neuralmagic/guidellm/blob/main/DEVELOPING.md) - Development guide for setting up your environment and making contributions. -- [**CONTRIBUTING.md**](https://github.com/neuralmagic/guidellm/blob/main/CONTRIBUTING.md) - Guidelines for contributing to the project, including code standards, pull request processes, and more. -- [**CODE_OF_CONDUCT.md**](https://github.com/neuralmagic/guidellm/blob/main/CODE_OF_CONDUCT.md) - Our expectations for community behavior to ensure a welcoming and inclusive environment. - -### Releases - -Visit our [GitHub Releases page](https://github.com/neuralmagic/guidellm/releases) and review the release notes to stay updated with the latest releases. - -### License - -GuideLLM is licensed under the [Apache License 2.0](https://github.com/neuralmagic/guidellm/blob/main/LICENSE). - -### Cite - -If you find GuideLLM helpful in your research or projects, please consider citing it: - -```bibtex -@misc{guidellm2024, - title={GuideLLM: Scalable Inference and Optimization for Large Language Models}, - author={Neural Magic, Inc.}, - year={2024}, - howpublished={\url{https://github.com/neuralmagic/guidellm}}, -} -``` diff --git a/docs/architecture.md b/docs/architecture.md deleted file mode 100644 index 047648ec..00000000 --- a/docs/architecture.md +++ /dev/null @@ -1,97 +0,0 @@ -# GuideLLM Architecture - -GuideLLM is designed to evaluate and optimize large language model (LLM) deployments by simulating real-world inference workloads. The architecture is modular, enabling flexibility and scalability. Below is an overview of the core components and their interactions. - -``` -+------------------+ +------------------+ +------------------+ -| DatasetCreator | ---> | RequestLoader | ---> | Scheduler | -+------------------+ +------------------+ +------------------+ - / | \ - / | \ - / | \ - v v v - +------------------+ +------------------+ - | RequestsWorker | | RequestsWorker | - +------------------+ +------------------+ - | | - v v - +------------------+ +------------------+ - | Backend | | Backend | - +------------------+ +------------------+ - | | - v v - +---------------------------------------+ - | BenchmarkAggregator | - +---------------------------------------+ - | - v - +------------------+ - | Benchmarker | - +------------------+ -``` - -## Core Components - -### 1. **Backend** - -The `Backend` is an abstract interface for interacting with generative AI backends. It is responsible for processing requests and generating results. GuideLLM supports OpenAI-compatible HTTP servers, such as vLLM, as backends. - -- **Responsibilities:** - - Accept requests from the `RequestsWorker`. - - Generate responses for text or chat completions. - - Validate backend readiness and available models. - -### 2. **RequestLoader** - -The `RequestLoader` handles sourcing data from an iterable and generating requests for the backend. It ensures that data is properly formatted and ready for processing. - -- **Responsibilities:** - - Load data from datasets or synthetic sources. - - Generate requests in a format compatible with the backend. - -### 3. **DatasetCreator** - -The `DatasetCreator` is responsible for loading data sources and converting them into Hugging Face (HF) dataset items. These items can then be streamed by the `RequestLoader`. - -- **Responsibilities:** - - Load datasets from local files, Hugging Face datasets, or synthetic data. - - Convert data into a format compatible with the `RequestLoader`. - -### 4. **Scheduler** - -The `Scheduler` manages the scheduling of requests to the backend. It uses multiprocessing and multithreading with asyncio to minimize overheads and maximize throughput. - -- **Responsibilities:** - - Schedule requests to the backend. - - Manage queues for requests and results. - - Ensure efficient utilization of resources. - -### 5. **RequestsWorker** - -The `RequestsWorker` is a worker process that pulls requests from a queue, processes them using the backend, and sends the results back to the scheduler. - -- **Responsibilities:** - - Process requests from the scheduler. - - Interact with the backend to generate results. - - Return results to the scheduler. - -### 6. **Benchmarker** - -The `Benchmarker` wraps around multiple invocations of the `Scheduler`, one for each benchmark. It aggregates results using a `BenchmarkAggregator` and compiles them into a `Benchmark` once complete. - -- **Responsibilities:** - - Manage multiple benchmarks. - - Aggregate results from the scheduler. - - Compile results into a final benchmark report. - -### 7. **BenchmarkAggregator** - -The `BenchmarkAggregator` is responsible for storing and compiling results from the benchmarks. - -- **Responsibilities:** - - Aggregate results from multiple benchmarks. - - Compile results into a `Benchmark` object. - -## Component Interactions - -The following diagram illustrates the relationships between the core components: diff --git a/docs/assets/guidellm-icon-blue.png b/docs/assets/guidellm-icon-blue.png deleted file mode 100644 index db191639782b5e34bbab9aebda9d98627257dc3a..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 3577 zcmVXwP)CUS8ONWQckMV3lP$DRsa0_gpvESukwE2uT1N#gAQ1>BF0nbmg|I~J4PmRC+QO1v 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oKmsH{0wh2JBtQZrke - + - - Redirecting - - + + + GuideLLM - Neural Magic + - Redirecting to latest/... +

+

GuideLLM

+

A framework for efficient large language model evaluation and benchmarking

+
+ +
+

Welcome to GuideLLM

+

This is a placeholder for the GuideLLM project documentation site. The actual content will be added soon.

+ +

About GuideLLM

+

GuideLLM is a framework developed by Neural Magic for effective evaluation and benchmarking of large language models.

+ +

Resources

+ +
+ +
+

© 2025 Neural Magic | GuideLLM

+ +
diff --git a/pyproject.toml b/pyproject.toml deleted file mode 100644 index a78b1fc5..00000000 --- a/pyproject.toml +++ /dev/null @@ -1,241 +0,0 @@ -[build-system] -requires = ["setuptools >= 61.0", "setuptools-git-versioning>=2.0,<3"] -build-backend = "setuptools.build_meta" - - -[tool.setuptools.packages.find] -where = ["src"] -include = ["*"] - -[tool.setuptools.package-data] -"guidellm.data" = ["*.gz"] - - -# ************************************************ -# ********** Project Metadata ********** -# ************************************************ - -[project] -dynamic = ["version"] -name = "guidellm" -description = "Guidance platform for deploying and managing large language models." -readme = { file = "README.md", content-type = "text/markdown" } -requires-python = ">=3.9.0,<4.0" -license = "Apache-2.0" -license-files = ["LICENSE"] -authors = [ { name = "Red Hat" } ] -keywords = [ - "ai", - "benchmarking", - "deep-learning", - "deployment", - "evaluation", - "guidance", - "inference", - "language-models", - "large-language-model", - "llm", - "machine-learning", - "model-benchmark", - "model-evaluation", - "nlp", - "performance", - "vllm", -] -dependencies = [ - "click", - "datasets", - "ftfy>=6.0.0", - "httpx[http2]<1.0.0", - "loguru", - "numpy", - "pillow", - "protobuf", - "pydantic>=2.0.0", - "pydantic-settings>=2.0.0", - "pyyaml>=6.0.0", - "rich", - "transformers", -] - -[project.optional-dependencies] -dev = [ - # build - "build>=1.0.0", - "setuptools>=61.0", - "setuptools-git-versioning>=2.0,<3", - - # general and configurations - "pre-commit~=3.5.0", - "scipy~=1.10", - "sphinx~=7.1.2", - "tox~=4.16.0", - - # testing - "lorem~=0.1.1", - "pytest~=8.2.2", - "pytest-asyncio~=0.23.8", - "pytest-cov~=5.0.0", - "pytest-mock~=3.14.0", - "pytest-rerunfailures~=14.0", - "respx~=0.22.0", - - # code quality - "mypy~=1.15.0", - "ruff~=0.11.7", - - # docs quality - "mdformat~=0.7.17", - "mdformat-footnote~=0.1.1", - "mdformat-frontmatter~=2.0.8", - "mdformat-gfm~=0.3.6", - - # type-checking - "types-click~=7.1.8", - "types-PyYAML~=6.0.1", - "types-requests~=2.32.0", - "types-toml", - - # link checking - "mkdocs-linkcheck~=1.0.6", -] - -[project.urls] -homepage = "https://github.com/neuralmagic/guidellm" -source = "https://github.com/neuralmagic/guidellm" -issues = "https://github.com/neuralmagic/guidellm/issues" -docs = "https://github.com/neuralmagic/guidellm/tree/main/docs" - - -[project.entry-points.console_scripts] -guidellm = "guidellm.__main__:cli" - - -# ************************************************ -# ********** Code Quality Tools ********** -# ************************************************ - - -[tool.isort] -profile = "black" - - -[tool.mypy] -files = ["src/guidellm", "tests"] -python_version = '3.9' -warn_redundant_casts = true -warn_unused_ignores = false -show_error_codes = true -namespace_packages = true -exclude = ["venv", ".tox"] - -# Silence "type import errors" as our 3rd-party libs does not have types -# Check: https://mypy.readthedocs.io/en/latest/config_file.html#import-discovery -follow_imports = 'silent' - -[[tool.mypy.overrides]] -module = ["datasets.*", "transformers.*", "setuptools.*", "setuptools_git_versioning.*"] -ignore_missing_imports=true - - -[tool.ruff] -line-length = 88 -indent-width = 4 -exclude = ["build", "dist", "env", ".venv"] - -[tool.ruff.format] -quote-style = "double" -indent-style = "space" - -[tool.ruff.lint] -ignore = [ - "PLR0913", - "TC001", - "COM812", - "ISC001", - "TC002", - "PLW1514", # allow Path.open without encoding - "RET505", # allow `else` blocks - "RET506", # allow `else` blocks - "PD011", # ignore .values usage since ruff assumes it's a Pandas DataFrame -] -select = [ - # Rules reference: https://docs.astral.sh/ruff/rules/ - - # Code Style / Formatting - "E", # pycodestyle: checks adherence to PEP 8 conventions including spacing, indentation, and line length - "W", # pycodestyle: checks adherence to PEP 8 conventions including spacing, indentation, and line length - "A", # flake8-builtins: prevents shadowing of Python built-in names - "C", # Convention: ensures code adheres to specific style and formatting conventions - "COM", # flake8-commas: enforces the correct use of trailing commas - "ERA", # eradicate: detects commented-out code that should be removed - "I", # isort: ensures imports are sorted in a consistent manner - "ICN", # flake8-import-conventions: enforces import conventions for better readability - "N", # pep8-naming: enforces PEP 8 naming conventions for classes, functions, and variables - "NPY", # NumPy: enforces best practices for using the NumPy library - "PD", # pandas-vet: enforces best practices for using the pandas library - "PT", # flake8-pytest-style: enforces best practices and style conventions for pytest tests - "PTH", # flake8-use-pathlib: encourages the use of pathlib over os.path for file system operations - "Q", # flake8-quotes: enforces consistent use of single or double quotes - "TCH", # flake8-type-checking: enforces type checking practices and standards - "TID", # flake8-tidy-imports: enforces tidy and well-organized imports - "RUF022", # flake8-ruff: enforce sorting of __all__ in modules - - # Code Structure / Complexity - "C4", # flake8-comprehensions: improves readability and performance of list, set, and dict comprehensions - "C90", # mccabe: checks for overly complex code using cyclomatic complexity - "ISC", # flake8-implicit-str-concat: prevents implicit string concatenation - "PIE", # flake8-pie: identifies and corrects common code inefficiencies and mistakes - "R", # Refactor: suggests improvements to code structure and readability - "SIM", # flake8-simplify: simplifies complex expressions and improves code readability - - # Code Security / Bug Prevention - "ARG", # flake8-unused-arguments: detects unused function and method arguments - "ASYNC", # flake8-async: identifies incorrect or inefficient usage patterns in asynchronous code - "B", # flake8-bugbear: detects common programming mistakes and potential bugs - "BLE", # flake8-blind-except: prevents blind exceptions that catch all exceptions without handling - "E", # Error: detects and reports errors in the code - "F", # Pyflakes: detects unused imports, shadowed imports, undefined variables, and various formatting errors in string operations - "INP", # flake8-no-pep420: prevents implicit namespace packages by requiring __init__.py - "PGH", # pygrep-hooks: detects deprecated and dangerous code patterns - "PL", # Pylint: comprehensive source code analyzer for enforcing coding standards and detecting errors - "RSE", # flake8-raise: ensures exceptions are raised correctly - "S", # flake8-bandit: detects security issues and vulnerabilities in the code - "SLF", # flake8-self: prevents incorrect usage of the self argument in class methods - "T10", # flake8-debugger: detects the presence of debugging tools such as pdb - "T20", # flake8-print: detects print statements left in the code - "UP", # pyupgrade: automatically upgrades syntax for newer versions of Python - "W", # Warning: provides warnings about potential issues in the code - "YTT", # flake8-2020: identifies code that will break with future Python releases - - # Code Documentation - "FIX", # flake8-fixme: detects FIXMEs and other temporary comments that should be resolved -] - -[tool.ruff.lint.extend-per-file-ignores] -"tests/**/*.py" = [ - "S101", # asserts allowed in tests - "ARG", # Unused function args allowed in tests - "PLR2004", # Magic value used in comparison - "TCH002", # No import only type checking in tests - "SLF001", # enable private member access in tests - "S105", # allow hardcoded passwords in tests - "S311", # allow standard pseudo-random generators in tests - "PT011", # allow generic exceptions in tests - "N806", # allow uppercase variable names in tests - "PGH003", # allow general ignores in tests - "S106", # allow hardcoded passwords in tests - "PLR0915", # allow complext statements in tests -] - -[tool.ruff.lint.isort] -known-first-party = ["guidellm", "tests"] - - -[tool.pytest.ini_options] -addopts = '-s -vvv --cache-clear' -markers = [ - "smoke: quick tests to check basic functionality", - "sanity: detailed tests to ensure major functions work correctly", - "regression: tests to ensure that new changes do not break existing functionality" -] diff --git a/setup.py b/setup.py deleted file mode 100644 index 009cf362..00000000 --- a/setup.py +++ /dev/null @@ -1,126 +0,0 @@ -import os -import re -from pathlib import Path -from typing import Optional, Union - -from packaging.version import Version -from setuptools import setup -from setuptools_git_versioning import count_since, get_branch, get_sha, get_tags - -LAST_RELEASE_VERSION = Version("0.0.0") -TAG_VERSION_PATTERN = re.compile(r"^v(\d+\.\d+\.\d+)$") - - -def get_last_version_diff() -> tuple[Version, Optional[str], Optional[int]]: - """ - Get the last version, last tag, and the number of commits since the last tag. - If no tags are found, return the last release version and None for the tag/commits. - - :returns: A tuple containing the last version, last tag, and number of commits since - the last tag. - """ - tagged_versions = [ - (Version(match.group(1)), tag) - for tag in get_tags(root=Path(__file__).parent) - if (match := TAG_VERSION_PATTERN.match(tag)) - ] - tagged_versions.sort(key=lambda tv: tv[0]) - last_version, last_tag = ( - tagged_versions[-1] if tagged_versions else (LAST_RELEASE_VERSION, None) - ) - commits_since_last = ( - count_since(last_tag + "^{commit}", root=Path(__file__).parent) - if last_tag - else None - ) - - return last_version, last_tag, commits_since_last - - -def get_next_version( - build_type: str, build_iteration: Optional[Union[str, int]] -) -> tuple[Version, Optional[str], int]: - """ - Get the next version based on the build type and iteration. - - build_type == release: take the last version and add a post if build iteration - - build_type == candidate: increment to next minor, add 'rc' with build iteration - - build_type == nightly: increment to next minor, add 'a' with build iteration - - build_type == alpha: increment to next minor, add 'a' with build iteration - - build_type == dev: increment to next minor, add 'dev' with build iteration - - :param build_type: The type of build (release, candidate, nightly, alpha, dev). - :param build_iteration: The build iteration number. If None, defaults to the number - of commits since the last tag or 0 if no commits since the last tag. - :returns: A tuple containing the next version, the last tag the version is based - off of (if any), and the final build iteration used. - """ - version, tag, commits_since_last = get_last_version_diff() - - if not build_iteration and build_iteration != 0: - build_iteration = commits_since_last or 0 - elif isinstance(build_iteration, str): - build_iteration = int(build_iteration) - - if build_type == "release": - if commits_since_last: - # add post since we have commits since last tag - version = Version(f"{version.base_version}.post{build_iteration}") - return version, tag, build_iteration - - # not in release pathway, so need to increment to target next release version - version = Version(f"{version.major}.{version.minor + 1}.0") - - if build_type == "candidate": - # add 'rc' since we are in candidate pathway - version = Version(f"{version}.rc{build_iteration}") - elif build_type in ["nightly", "alpha"]: - # add 'a' since we are in nightly or alpha pathway - version = Version(f"{version}.a{build_iteration}") - else: - # assume 'dev' if not in any of the above pathways - version = Version(f"{version}.dev{build_iteration}") - - return version, tag, build_iteration - - -def write_version_files() -> tuple[Path, Path]: - """ - Write the version information to version.txt and version.py files. - version.txt contains the version string. - version.py contains the version plus additional metadata. - - :returns: A tuple containing the paths to the version.txt and version.py files. - """ - build_type = os.getenv("GUIDELLM_BUILD_TYPE", "dev").lower() - version, tag, build_iteration = get_next_version( - build_type=build_type, - build_iteration=os.getenv("GUIDELLM_BUILD_ITERATION"), - ) - module_path = Path(__file__).parent / "src" / "guidellm" - version_txt_path = module_path / "version.txt" - version_py_path = module_path / "version.py" - - with version_txt_path.open("w") as file: - file.write(str(version)) - - with version_py_path.open("w") as file: - file.writelines( - [ - f'version = "{version}"\n', - f'build_type = "{build_type}"\n', - f'build_iteration = "{build_iteration}"\n', - f'git_commit = "{get_sha()}"\n', - f'git_branch = "{get_branch()}"\n', - f'git_last_tag = "{tag}"\n', - ] - ) - - return version_txt_path, version_py_path - - -setup( - setuptools_git_versioning={ - "enabled": True, - "version_file": str(write_version_files()[0]), - } -) diff --git a/src/guidellm/__init__.py b/src/guidellm/__init__.py deleted file mode 100644 index 9333860e..00000000 --- a/src/guidellm/__init__.py +++ /dev/null @@ -1,46 +0,0 @@ -""" -Guidellm is a package that provides an easy and intuitive interface for -evaluating and benchmarking large language models (LLMs). -""" - -import contextlib -import logging -import os - -with ( - open(os.devnull, "w") as devnull, # noqa: PTH123 - contextlib.redirect_stderr(devnull), - contextlib.redirect_stdout(devnull), -): - from transformers.utils import logging as hf_logging # type: ignore[import] - - # Set the log level for the transformers library to ERROR - # to ignore None of PyTorch, TensorFlow found - os.environ["TOKENIZERS_PARALLELISM"] = "false" # Silence warnings for tokenizers - hf_logging.set_verbosity_error() - logging.getLogger("transformers").setLevel(logging.ERROR) - -from .config import ( - DatasetSettings, - Environment, - LoggingSettings, - OpenAISettings, - Settings, - print_config, - reload_settings, - settings, -) -from .logger import configure_logger, logger - -__all__ = [ - "DatasetSettings", - "Environment", - "LoggingSettings", - "OpenAISettings", - "Settings", - "configure_logger", - "logger", - "print_config", - "reload_settings", - "settings", -] diff --git a/src/guidellm/__main__.py b/src/guidellm/__main__.py deleted file mode 100644 index f38b11aa..00000000 --- a/src/guidellm/__main__.py +++ /dev/null @@ -1,294 +0,0 @@ -import asyncio -import json -from pathlib import Path -from typing import get_args - -import click - -from guidellm.backend import BackendType -from guidellm.benchmark import ProfileType, benchmark_generative_text -from guidellm.config import print_config -from guidellm.scheduler import StrategyType - -STRATEGY_PROFILE_CHOICES = set( - list(get_args(ProfileType)) + list(get_args(StrategyType)) -) - - -def parse_json(ctx, param, value): # noqa: ARG001 - if value is None: - return None - try: - return json.loads(value) - except json.JSONDecodeError as err: - raise click.BadParameter(f"{param.name} must be a valid JSON string.") from err - - -def parse_number_str(ctx, param, value): # noqa: ARG001 - if value is None: - return None - - values = value.split(",") if "," in value else [value] - - try: - return [int(val) if val.isdigit() else float(val) for val in values] - except ValueError as err: - raise click.BadParameter( - f"{param.name} must be a number or comma-separated list of numbers." - ) from err - - -@click.group() -def cli(): - pass - - -@cli.command( - help="Run a benchmark against a generative model using the specified arguments." -) -@click.option( - "--target", - required=True, - type=str, - help="The target path for the backend to run benchmarks against. For example, http://localhost:8000", -) -@click.option( - "--backend-type", - type=click.Choice(list(get_args(BackendType))), - help=( - "The type of backend to use to run requests against. Defaults to 'openai_http'." - f" Supported types: {', '.join(get_args(BackendType))}" - ), - default="openai_http", -) -@click.option( - "--backend-args", - callback=parse_json, - default=None, - help=( - "A JSON string containing any arguments to pass to the backend as a " - "dict with **kwargs." - ), -) -@click.option( - "--model", - default=None, - type=str, - help=( - "The ID of the model to benchmark within the backend. " - "If None provided (default), then it will use the first model available." - ), -) -@click.option( - "--processor", - default=None, - type=str, - help=( - "The processor or tokenizer to use to calculate token counts for statistics " - "and synthetic data generation. If None provided (default), will load " - "using the model arg, if needed." - ), -) -@click.option( - "--processor-args", - default=None, - callback=parse_json, - help=( - "A JSON string containing any arguments to pass to the processor constructor " - "as a dict with **kwargs." - ), -) -@click.option( - "--data", - required=True, - type=str, - help=( - "The HuggingFace dataset ID, a path to a HuggingFace dataset, " - "a path to a data file csv, json, jsonl, or txt, " - "or a synthetic data config as a json or key=value string." - ), -) -@click.option( - "--data-args", - callback=parse_json, - help=( - "A JSON string containing any arguments to pass to the dataset creation " - "as a dict with **kwargs." - ), -) -@click.option( - "--data-sampler", - default=None, - type=click.Choice(["random"]), - help=( - "The data sampler type to use. 'random' will add a random shuffle on the data. " - "Defaults to None" - ), -) -@click.option( - "--rate-type", - required=True, - type=click.Choice(STRATEGY_PROFILE_CHOICES), - help=( - "The type of benchmark to run. " - f"Supported types {', '.join(STRATEGY_PROFILE_CHOICES)}. " - ), -) -@click.option( - "--rate", - default=None, - callback=parse_number_str, - help=( - "The rates to run the benchmark at. " - "Can be a single number or a comma-separated list of numbers. " - "For rate-type=sweep, this is the number of benchmarks it runs in the sweep. " - "For rate-type=concurrent, this is the number of concurrent requests. " - "For rate-type=async,constant,poisson, this is the rate requests per second. " - "For rate-type=synchronous,throughput, this must not be set." - ), -) -@click.option( - "--max-seconds", - type=float, - help=( - "The maximum number of seconds each benchmark can run for. " - "If None, will run until max_requests or the data is exhausted." - ), -) -@click.option( - "--max-requests", - type=int, - help=( - "The maximum number of requests each benchmark can run for. " - "If None, will run until max_seconds or the data is exhausted." - ), -) -@click.option( - "--warmup-percent", - type=float, - default=None, - help=( - "The percent of the benchmark (based on max-seconds, max-requets, " - "or lenth of dataset) to run as a warmup and not include in the final results. " - "Defaults to None." - ), -) -@click.option( - "--cooldown-percent", - type=float, - help=( - "The percent of the benchmark (based on max-seconds, max-requets, or lenth " - "of dataset) to run as a cooldown and not include in the final results. " - "Defaults to None." - ), -) -@click.option( - "--disable-progress", - is_flag=True, - help="Set this flag to disable progress updates to the console", -) -@click.option( - "--display-scheduler-stats", - is_flag=True, - help="Set this flag to display stats for the processes running the benchmarks", -) -@click.option( - "--disable-console-outputs", - is_flag=True, - help="Set this flag to disable console output", -) -@click.option( - "--output-path", - type=click.Path(), - default=Path.cwd() / "benchmarks.json", - help=( - "The path to save the output to. If it is a directory, " - "it will save benchmarks.json under it. " - "Otherwise, json, yaml, or csv files are supported for output types " - "which will be read from the extension for the file path." - ), -) -@click.option( - "--output-extras", - callback=parse_json, - help="A JSON string of extra data to save with the output benchmarks", -) -@click.option( - "--output-sampling", - type=int, - help=( - "The number of samples to save in the output file. " - "If None (default), will save all samples." - ), - default=None, -) -@click.option( - "--random-seed", - default=42, - type=int, - help="The random seed to use for benchmarking to ensure reproducibility.", -) -def benchmark( - target, - backend_type, - backend_args, - model, - processor, - processor_args, - data, - data_args, - data_sampler, - rate_type, - rate, - max_seconds, - max_requests, - warmup_percent, - cooldown_percent, - disable_progress, - display_scheduler_stats, - disable_console_outputs, - output_path, - output_extras, - output_sampling, - random_seed, -): - asyncio.run( - benchmark_generative_text( - target=target, - backend_type=backend_type, - backend_args=backend_args, - model=model, - processor=processor, - processor_args=processor_args, - data=data, - data_args=data_args, - data_sampler=data_sampler, - rate_type=rate_type, - rate=rate, - max_seconds=max_seconds, - max_requests=max_requests, - warmup_percent=warmup_percent, - cooldown_percent=cooldown_percent, - show_progress=not disable_progress, - show_progress_scheduler_stats=display_scheduler_stats, - output_console=not disable_console_outputs, - output_path=output_path, - output_extras=output_extras, - output_sampling=output_sampling, - random_seed=random_seed, - ) - ) - - -@cli.command( - help=( - "Print out the available configuration settings that can be set " - "through environment variables." - ) -) -def config(): - print_config() - - -if __name__ == "__main__": - cli() diff --git a/src/guidellm/backend/__init__.py b/src/guidellm/backend/__init__.py deleted file mode 100644 index 315a28f0..00000000 --- a/src/guidellm/backend/__init__.py +++ /dev/null @@ -1,23 +0,0 @@ -from .backend import ( - Backend, - BackendType, -) -from .openai import CHAT_COMPLETIONS_PATH, TEXT_COMPLETIONS_PATH, OpenAIHTTPBackend -from .response import ( - RequestArgs, - ResponseSummary, - StreamingResponseType, - StreamingTextResponse, -) - -__all__ = [ - "CHAT_COMPLETIONS_PATH", - "TEXT_COMPLETIONS_PATH", - "Backend", - "BackendType", - "OpenAIHTTPBackend", - "RequestArgs", - "ResponseSummary", - "StreamingResponseType", - "StreamingTextResponse", -] diff --git a/src/guidellm/backend/backend.py b/src/guidellm/backend/backend.py deleted file mode 100644 index 12e30fa3..00000000 --- a/src/guidellm/backend/backend.py +++ /dev/null @@ -1,238 +0,0 @@ -from abc import ABC, abstractmethod -from collections.abc import AsyncGenerator -from pathlib import Path -from typing import Any, Literal, Optional, Union - -from loguru import logger -from PIL import Image - -from guidellm.backend.response import ResponseSummary, StreamingTextResponse - -__all__ = [ - "Backend", - "BackendType", -] - - -BackendType = Literal["openai_http"] - - -class Backend(ABC): - """ - Abstract base class for generative AI backends. - - This class provides a common interface for creating and interacting with different - generative AI backends. Subclasses should implement the abstract methods to - define specific backend behavior. - - :cvar _registry: A registration dictionary that maps BackendType to backend classes. - :param type_: The type of the backend. - """ - - _registry: dict[BackendType, "type[Backend]"] = {} - - @classmethod - def register(cls, backend_type: BackendType): - """ - A decorator to register a backend class in the backend registry. - - :param backend_type: The type of backend to register. - :type backend_type: BackendType - :return: The decorated backend class. - :rtype: Type[Backend] - """ - if backend_type in cls._registry: - raise ValueError(f"Backend type already registered: {backend_type}") - - if not issubclass(cls, Backend): - raise TypeError("Only subclasses of Backend can be registered") - - def inner_wrapper(wrapped_class: type["Backend"]): - cls._registry[backend_type] = wrapped_class - logger.info("Registered backend type: {}", backend_type) - return wrapped_class - - return inner_wrapper - - @classmethod - def create(cls, type_: BackendType, **kwargs) -> "Backend": - """ - Factory method to create a backend instance based on the backend type. - - :param type_: The type of backend to create. - :type type_: BackendType - :param kwargs: Additional arguments for backend initialization. - :return: An instance of a subclass of Backend. - :rtype: Backend - :raises ValueError: If the backend type is not registered. - """ - - logger.info("Creating backend of type {}", type_) - - if type_ not in cls._registry: - err = ValueError(f"Unsupported backend type: {type_}") - logger.error("{}", err) - raise err - - return Backend._registry[type_](**kwargs) - - def __init__(self, type_: BackendType): - self._type = type_ - - @property - def type_(self) -> BackendType: - """ - :return: The type of the backend. - """ - return self._type - - @property - @abstractmethod - def target(self) -> str: - """ - :return: The target location for the backend. - """ - ... - - @property - @abstractmethod - def model(self) -> Optional[str]: - """ - :return: The model used for the backend requests. - """ - ... - - @property - @abstractmethod - def info(self) -> dict[str, Any]: - """ - :return: The information about the backend. - """ - ... - - async def validate(self): - """ - Handle final setup and validate the backend is ready for use. - If not successful, raises the appropriate exception. - """ - logger.info("{} validating backend {}", self.__class__.__name__, self.type_) - await self.check_setup() - models = await self.available_models() - if not models: - raise ValueError("No models available for the backend") - - async for _ in self.text_completions( - prompt="Test connection", output_token_count=1 - ): # type: ignore[attr-defined] - pass - - @abstractmethod - async def check_setup(self): - """ - Check the setup for the backend. - If unsuccessful, raises the appropriate exception. - - :raises ValueError: If the setup check fails. - """ - ... - - @abstractmethod - async def prepare_multiprocessing(self): - """ - Prepare the backend for use in a multiprocessing environment. - This is useful for backends that have instance state that can not - be shared across processes and should be cleared out and re-initialized - for each new process. - """ - ... - - @abstractmethod - async def available_models(self) -> list[str]: - """ - Get the list of available models for the backend. - - :return: The list of available models. - :rtype: List[str] - """ - ... - - @abstractmethod - async def text_completions( - self, - prompt: Union[str, list[str]], - request_id: Optional[str] = None, - prompt_token_count: Optional[int] = None, - output_token_count: Optional[int] = None, - **kwargs, - ) -> AsyncGenerator[Union[StreamingTextResponse, ResponseSummary], None]: - """ - Generate text only completions for the given prompt. - Does not support multiple modalities, complicated chat interfaces, - or chat templates. Specifically, it requests with only the prompt. - - :param prompt: The prompt (or list of prompts) to generate a completion for. - If a list is supplied, these are concatenated and run through the model - for a single prompt. - :param request_id: The unique identifier for the request, if any. - Added to logging statements and the response for tracking purposes. - :param prompt_token_count: The number of tokens measured in the prompt, if any. - Returned in the response stats for later analysis, if applicable. - :param output_token_count: If supplied, the number of tokens to enforce - generation of for the output for this request. - :param kwargs: Additional keyword arguments to pass with the request. - :return: An async generator that yields a StreamingTextResponse for start, - a StreamingTextResponse for each received iteration, - and a ResponseSummary for the final response. - """ - ... - - @abstractmethod - async def chat_completions( - self, - content: Union[ - str, - list[Union[str, dict[str, Union[str, dict[str, str]]], Path, Image.Image]], - Any, - ], - request_id: Optional[str] = None, - prompt_token_count: Optional[int] = None, - output_token_count: Optional[int] = None, - raw_content: bool = False, - **kwargs, - ) -> AsyncGenerator[Union[StreamingTextResponse, ResponseSummary], None]: - """ - Generate chat completions for the given content. - Supports multiple modalities, complicated chat interfaces, and chat templates. - Specifically, it requests with the content, which can be any combination of - text, images, and audio provided the target model supports it, - and returns the output text. Additionally, any chat templates - for the model are applied within the backend. - - :param content: The content (or list of content) to generate a completion for. - This supports any combination of text, images, and audio (model dependent). - Supported text only request examples: - content="Sample prompt", content=["Sample prompt", "Second prompt"], - content=[{"type": "text", "value": "Sample prompt"}. - Supported text and image request examples: - content=["Describe the image", PIL.Image.open("image.jpg")], - content=["Describe the image", Path("image.jpg")], - content=["Describe the image", {"type": "image_url", - "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}]. - Supported text and audio request examples: - content=["Transcribe the audio", Path("audio.wav")], - content=["Transcribe the audio", {"type": "input_audio", - "input_audio": {"data": f"{base64_bytes}", "format": "wav}]. - Additionally, if raw_content=True then the content is passed directly to the - backend without any processing. - :param request_id: The unique identifier for the request, if any. - Added to logging statements and the response for tracking purposes. - :param prompt_token_count: The number of tokens measured in the prompt, if any. - Returned in the response stats for later analysis, if applicable. - :param output_token_count: If supplied, the number of tokens to enforce - generation of for the output for this request. - :param kwargs: Additional keyword arguments to pass with the request. - :return: An async generator that yields a StreamingTextResponse for start, - a StreamingTextResponse for each received iteration, - and a ResponseSummary for the final response. - """ - ... diff --git a/src/guidellm/backend/openai.py b/src/guidellm/backend/openai.py deleted file mode 100644 index 7ee4776c..00000000 --- a/src/guidellm/backend/openai.py +++ /dev/null @@ -1,585 +0,0 @@ -import base64 -import json -import time -from collections.abc import AsyncGenerator -from pathlib import Path -from typing import Any, Literal, Optional, Union - -import httpx -from loguru import logger -from PIL import Image - -from guidellm.backend.backend import Backend -from guidellm.backend.response import ( - RequestArgs, - ResponseSummary, - StreamingTextResponse, -) -from guidellm.config import settings - -__all__ = ["CHAT_COMPLETIONS_PATH", "TEXT_COMPLETIONS_PATH", "OpenAIHTTPBackend"] - - -TEXT_COMPLETIONS_PATH = "/v1/completions" -CHAT_COMPLETIONS_PATH = "/v1/chat/completions" - - -@Backend.register("openai_http") -class OpenAIHTTPBackend(Backend): - """ - A HTTP-based backend implementation for requests to an OpenAI compatible server. - For example, a vLLM server instance or requests to OpenAI's API. - - :param target: The target URL string for the OpenAI server. ex: http://0.0.0.0:8000 - :param model: The model to use for all requests on the target server. - If none is provided, the first available model will be used. - :param api_key: The API key to use for requests to the OpenAI server. - If provided, adds an Authorization header with the value - "Authorization: Bearer {api_key}". - If not provided, no Authorization header is added. - :param organization: The organization to use for requests to the OpenAI server. - For example, if set to "org_123", adds an OpenAI-Organization header with the - value "OpenAI-Organization: org_123". - If not provided, no OpenAI-Organization header is added. - :param project: The project to use for requests to the OpenAI server. - For example, if set to "project_123", adds an OpenAI-Project header with the - value "OpenAI-Project: project_123". - If not provided, no OpenAI-Project header is added. - :param timeout: The timeout to use for requests to the OpenAI server. - If not provided, the default timeout provided from settings is used. - :param http2: If True, uses HTTP/2 for requests to the OpenAI server. - Defaults to True. - :param max_output_tokens: The maximum number of tokens to request for completions. - If not provided, the default maximum tokens provided from settings is used. - """ - - def __init__( - self, - target: Optional[str] = None, - model: Optional[str] = None, - api_key: Optional[str] = None, - organization: Optional[str] = None, - project: Optional[str] = None, - timeout: Optional[float] = None, - http2: Optional[bool] = True, - max_output_tokens: Optional[int] = None, - ): - super().__init__(type_="openai_http") - self._target = target or settings.openai.base_url - - if not self._target: - raise ValueError("Target URL must be provided for OpenAI HTTP backend.") - - if self._target.endswith("/v1") or self._target.endswith("/v1/"): - # backwards compatability, strip v1 off - self._target = self._target[:-3] - - if self._target.endswith("/"): - self._target = self._target[:-1] - - self._model = model - - api_key = api_key or settings.openai.api_key - self.authorization = ( - f"Bearer {api_key}" if api_key else settings.openai.bearer_token - ) - - self.organization = organization or settings.openai.organization - self.project = project or settings.openai.project - self.timeout = timeout if timeout is not None else settings.request_timeout - self.http2 = http2 if http2 is not None else settings.request_http2 - self.max_output_tokens = ( - max_output_tokens - if max_output_tokens is not None - else settings.openai.max_output_tokens - ) - self._async_client: Optional[httpx.AsyncClient] = None - - @property - def target(self) -> str: - """ - :return: The target URL string for the OpenAI server. - """ - return self._target - - @property - def model(self) -> Optional[str]: - """ - :return: The model to use for all requests on the target server. - If validate hasn't been called yet and no model was passed in, - this will be None until validate is called to set the default. - """ - return self._model - - @property - def info(self) -> dict[str, Any]: - """ - :return: The information about the backend. - """ - return { - "max_output_tokens": self.max_output_tokens, - "timeout": self.timeout, - "http2": self.http2, - "authorization": bool(self.authorization), - "organization": self.organization, - "project": self.project, - "text_completions_path": TEXT_COMPLETIONS_PATH, - "chat_completions_path": CHAT_COMPLETIONS_PATH, - } - - async def check_setup(self): - """ - Check if the backend is setup correctly and can be used for requests. - Specifically, if a model is not provided, it grabs the first available model. - If no models are available, raises a ValueError. - If a model is provided and not available, raises a ValueError. - - :raises ValueError: If no models or the provided model is not available. - """ - models = await self.available_models() - if not models: - raise ValueError(f"No models available for target: {self.target}") - - if not self.model: - self._model = models[0] - elif self.model not in models: - raise ValueError( - f"Model {self.model} not found in available models:" - f"{models} for target: {self.target}" - ) - - async def prepare_multiprocessing(self): - """ - Prepare the backend for use in a multiprocessing environment. - Clears out the sync and async clients to ensure they are re-initialized - for each process. - """ - if self._async_client is not None: - await self._async_client.aclose() - self._async_client = None - - async def available_models(self) -> list[str]: - """ - Get the available models for the target server using the OpenAI models endpoint: - /v1/models - """ - target = f"{self.target}/v1/models" - headers = self._headers() - response = await self._get_async_client().get(target, headers=headers) - response.raise_for_status() - - models = [] - - for item in response.json()["data"]: - models.append(item["id"]) - - return models - - async def text_completions( # type: ignore[override] - self, - prompt: Union[str, list[str]], - request_id: Optional[str] = None, - prompt_token_count: Optional[int] = None, - output_token_count: Optional[int] = None, - **kwargs, - ) -> AsyncGenerator[Union[StreamingTextResponse, ResponseSummary], None]: - """ - Generate text completions for the given prompt using the OpenAI - completions endpoint: /v1/completions. - - :param prompt: The prompt (or list of prompts) to generate a completion for. - If a list is supplied, these are concatenated and run through the model - for a single prompt. - :param request_id: The unique identifier for the request, if any. - Added to logging statements and the response for tracking purposes. - :param prompt_token_count: The number of tokens measured in the prompt, if any. - Returned in the response stats for later analysis, if applicable. - :param output_token_count: If supplied, the number of tokens to enforce - generation of for the output for this request. - :param kwargs: Additional keyword arguments to pass with the request. - :return: An async generator that yields a StreamingTextResponse for start, - a StreamingTextResponse for each received iteration, - and a ResponseSummary for the final response. - """ - logger.debug("{} invocation with args: {}", self.__class__.__name__, locals()) - - if isinstance(prompt, list): - raise ValueError( - "List prompts (batching) is currently not supported for " - f"text_completions OpenAI pathways. Received: {prompt}" - ) - - headers = self._headers() - payload = self._completions_payload( - orig_kwargs=kwargs, - max_output_tokens=output_token_count, - prompt=prompt, - ) - - try: - async for resp in self._iterative_completions_request( - type_="text_completions", - request_id=request_id, - request_prompt_tokens=prompt_token_count, - request_output_tokens=output_token_count, - headers=headers, - payload=payload, - ): - yield resp - except Exception as ex: - logger.error( - "{} request with headers: {} and payload: {} failed: {}", - self.__class__.__name__, - headers, - payload, - ex, - ) - raise ex - - async def chat_completions( # type: ignore[override] - self, - content: Union[ - str, - list[Union[str, dict[str, Union[str, dict[str, str]]], Path, Image.Image]], - Any, - ], - request_id: Optional[str] = None, - prompt_token_count: Optional[int] = None, - output_token_count: Optional[int] = None, - raw_content: bool = False, - **kwargs, - ) -> AsyncGenerator[Union[StreamingTextResponse, ResponseSummary], None]: - """ - Generate chat completions for the given content using the OpenAI - chat completions endpoint: /v1/chat/completions. - - :param content: The content (or list of content) to generate a completion for. - This supports any combination of text, images, and audio (model dependent). - Supported text only request examples: - content="Sample prompt", content=["Sample prompt", "Second prompt"], - content=[{"type": "text", "value": "Sample prompt"}. - Supported text and image request examples: - content=["Describe the image", PIL.Image.open("image.jpg")], - content=["Describe the image", Path("image.jpg")], - content=["Describe the image", {"type": "image_url", - "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}]. - Supported text and audio request examples: - content=["Transcribe the audio", Path("audio.wav")], - content=["Transcribe the audio", {"type": "input_audio", - "input_audio": {"data": f"{base64_bytes}", "format": "wav}]. - Additionally, if raw_content=True then the content is passed directly to the - backend without any processing. - :param request_id: The unique identifier for the request, if any. - Added to logging statements and the response for tracking purposes. - :param prompt_token_count: The number of tokens measured in the prompt, if any. - Returned in the response stats for later analysis, if applicable. - :param output_token_count: If supplied, the number of tokens to enforce - generation of for the output for this request. - :param kwargs: Additional keyword arguments to pass with the request. - :return: An async generator that yields a StreamingTextResponse for start, - a StreamingTextResponse for each received iteration, - and a ResponseSummary for the final response. - """ - logger.debug("{} invocation with args: {}", self.__class__.__name__, locals()) - headers = self._headers() - messages = ( - content if raw_content else self._create_chat_messages(content=content) - ) - payload = self._completions_payload( - orig_kwargs=kwargs, - max_output_tokens=output_token_count, - messages=messages, - ) - - try: - async for resp in self._iterative_completions_request( - type_="chat_completions", - request_id=request_id, - request_prompt_tokens=prompt_token_count, - request_output_tokens=output_token_count, - headers=headers, - payload=payload, - ): - yield resp - except Exception as ex: - logger.error( - "{} request with headers: {} and payload: {} failed: {}", - self.__class__.__name__, - headers, - payload, - ex, - ) - raise ex - - def _get_async_client(self) -> httpx.AsyncClient: - """ - Get the async HTTP client for making requests. - If the client has not been created yet, it will create one. - - :return: The async HTTP client. - """ - if self._async_client is None: - client = httpx.AsyncClient(http2=self.http2, timeout=self.timeout) - self._async_client = client - else: - client = self._async_client - - return client - - def _headers(self) -> dict[str, str]: - headers = { - "Content-Type": "application/json", - } - - if self.authorization: - headers["Authorization"] = self.authorization - - if self.organization: - headers["OpenAI-Organization"] = self.organization - - if self.project: - headers["OpenAI-Project"] = self.project - - return headers - - def _completions_payload( - self, orig_kwargs: Optional[dict], max_output_tokens: Optional[int], **kwargs - ) -> dict: - payload = orig_kwargs or {} - payload.update(kwargs) - payload["model"] = self.model - payload["stream"] = True - payload["stream_options"] = { - "include_usage": True, - } - - if max_output_tokens or self.max_output_tokens: - logger.debug( - "{} adding payload args for setting output_token_count: {}", - self.__class__.__name__, - max_output_tokens or self.max_output_tokens, - ) - payload["max_tokens"] = max_output_tokens or self.max_output_tokens - payload["max_completion_tokens"] = payload["max_tokens"] - - if max_output_tokens: - # only set stop and ignore_eos if max_output_tokens set at request level - # otherwise the instance value is just the max to enforce we stay below - payload["stop"] = None - payload["ignore_eos"] = True - - return payload - - @staticmethod - def _create_chat_messages( - content: Union[ - str, - list[Union[str, dict[str, Union[str, dict[str, str]]], Path, Image.Image]], - Any, - ], - ) -> list[dict]: - if isinstance(content, str): - return [ - { - "role": "user", - "content": content, - } - ] - - if isinstance(content, list): - resolved_content = [] - - for item in content: - if isinstance(item, dict): - resolved_content.append(item) - elif isinstance(item, str): - resolved_content.append({"type": "text", "text": item}) - elif isinstance(item, Image.Image) or ( - isinstance(item, Path) and item.suffix.lower() in [".jpg", ".jpeg"] - ): - image = item if isinstance(item, Image.Image) else Image.open(item) - encoded = base64.b64encode(image.tobytes()).decode("utf-8") - resolved_content.append( - { - "type": "image", - "image": { - "url": f"data:image/jpeg;base64,{encoded}", - }, - } - ) - elif isinstance(item, Path) and item.suffix.lower() in [".wav"]: - encoded = base64.b64encode(item.read_bytes()).decode("utf-8") - resolved_content.append( - { - "type": "input_audio", - "input_audio": { - "data": f"{encoded}", - "format": "wav", - }, - } - ) - else: - raise ValueError( - f"Unsupported content item type: {item} in list: {content}" - ) - - return [ - { - "role": "user", - "content": resolved_content, - } - ] - - raise ValueError(f"Unsupported content type: {content}") - - async def _iterative_completions_request( - self, - type_: Literal["text_completions", "chat_completions"], - request_id: Optional[str], - request_prompt_tokens: Optional[int], - request_output_tokens: Optional[int], - headers: dict, - payload: dict, - ) -> AsyncGenerator[Union[StreamingTextResponse, ResponseSummary], None]: - if type_ == "text_completions": - target = f"{self.target}{TEXT_COMPLETIONS_PATH}" - elif type_ == "chat_completions": - target = f"{self.target}{CHAT_COMPLETIONS_PATH}" - else: - raise ValueError(f"Unsupported type: {type_}") - - logger.info( - "{} making request: {} to target: {} using http2: {} for " - "timeout: {} with headers: {} and payload: {}", - self.__class__.__name__, - request_id, - target, - self.http2, - self.timeout, - headers, - payload, - ) - - response_value = "" - response_prompt_count: Optional[int] = None - response_output_count: Optional[int] = None - iter_count = 0 - start_time = time.time() - iter_time = start_time - first_iter_time: Optional[float] = None - last_iter_time: Optional[float] = None - - yield StreamingTextResponse( - type_="start", - value="", - start_time=start_time, - first_iter_time=None, - iter_count=iter_count, - delta="", - time=start_time, - request_id=request_id, - ) - - # reset start time after yielding start response to ensure accurate timing - start_time = time.time() - - async with self._get_async_client().stream( - "POST", target, headers=headers, json=payload - ) as stream: - stream.raise_for_status() - - async for line in stream.aiter_lines(): - iter_time = time.time() - logger.debug( - "{} request: {} recieved iter response line: {}", - self.__class__.__name__, - request_id, - line, - ) - - if not line or not line.strip().startswith("data:"): - continue - - if line.strip() == "data: [DONE]": - break - - data = json.loads(line.strip()[len("data: ") :]) - if delta := self._extract_completions_delta_content(type_, data): - if first_iter_time is None: - first_iter_time = iter_time - last_iter_time = iter_time - - iter_count += 1 - response_value += delta - - yield StreamingTextResponse( - type_="iter", - value=response_value, - iter_count=iter_count, - start_time=start_time, - first_iter_time=first_iter_time, - delta=delta, - time=iter_time, - request_id=request_id, - ) - - if usage := self._extract_completions_usage(data): - response_prompt_count = usage["prompt"] - response_output_count = usage["output"] - - logger.info( - "{} request: {} with headers: {} and payload: {} completed with: {}", - self.__class__.__name__, - request_id, - headers, - payload, - response_value, - ) - - yield ResponseSummary( - value=response_value, - request_args=RequestArgs( - target=target, - headers=headers, - payload=payload, - timeout=self.timeout, - http2=self.http2, - ), - start_time=start_time, - end_time=iter_time, - first_iter_time=first_iter_time, - last_iter_time=last_iter_time, - iterations=iter_count, - request_prompt_tokens=request_prompt_tokens, - request_output_tokens=request_output_tokens, - response_prompt_tokens=response_prompt_count, - response_output_tokens=response_output_count, - request_id=request_id, - ) - - @staticmethod - def _extract_completions_delta_content( - type_: Literal["text_completions", "chat_completions"], data: dict - ) -> Optional[str]: - if "choices" not in data or not data["choices"]: - return None - - if type_ == "text_completions": - return data["choices"][0]["text"] - - if type_ == "chat_completions": - return data["choices"][0]["delta"]["content"] - - raise ValueError(f"Unsupported type: {type_}") - - @staticmethod - def _extract_completions_usage( - data: dict, - ) -> Optional[dict[Literal["prompt", "output"], int]]: - if "usage" not in data or not data["usage"]: - return None - - return { - "prompt": data["usage"]["prompt_tokens"], - "output": data["usage"]["completion_tokens"], - } diff --git a/src/guidellm/backend/response.py b/src/guidellm/backend/response.py deleted file mode 100644 index 4875ade5..00000000 --- a/src/guidellm/backend/response.py +++ /dev/null @@ -1,132 +0,0 @@ -from typing import Any, Literal, Optional - -from pydantic import computed_field - -from guidellm.config import settings -from guidellm.objects.pydantic import StandardBaseModel - -__all__ = [ - "RequestArgs", - "ResponseSummary", - "StreamingResponseType", - "StreamingTextResponse", -] - - -StreamingResponseType = Literal["start", "iter"] - - -class StreamingTextResponse(StandardBaseModel): - """ - A model representing the response content for a streaming text request. - - :param type_: The type of the response; either 'start' or 'iter'. - :param value: The value of the response up to this iteration. - :param start_time: The time.time() the request started. - :param iter_count: The iteration count for the response. For 'start' this is 0 - and for the first 'iter' it is 1. - :param delta: The text delta added to the response for this stream iteration. - :param time: If 'start', the time.time() the request started. - If 'iter', the time.time() the iteration was received. - :param request_id: The unique identifier for the request, if any. - """ - - type_: StreamingResponseType - value: str - start_time: float - first_iter_time: Optional[float] - iter_count: int - delta: str - time: float - request_id: Optional[str] = None - - -class RequestArgs(StandardBaseModel): - """ - A model representing the arguments for a request to a backend. - Biases towards an HTTP request, but can be used for other types of backends. - - :param target: The target URL or function for the request. - :param headers: The headers, if any, included in the request such as authorization. - :param payload: The payload / arguments for the request including the prompt / - content and other configurations. - :param timeout: The timeout for the request in seconds, if any. - :param http2: Whether HTTP/2 was used for the request, if applicable. - """ - - target: str - headers: dict[str, str] - payload: dict[str, Any] - timeout: Optional[float] = None - http2: Optional[bool] = None - - -class ResponseSummary(StandardBaseModel): - """ - A model representing a summary of a backend request. - Always returned as the final iteration of a streaming request. - - :param value: The final value returned from the request. - :param request_args: The arguments used to make the request. - :param iterations: The number of iterations in the request. - :param start_time: The time the request started. - :param end_time: The time the request ended. - :param first_iter_time: The time the first iteration was received. - :param last_iter_time: The time the last iteration was received. - :param request_prompt_tokens: The number of tokens measured in the prompt - for the request, if any. - :param request_output_tokens: The number of tokens enforced for the output - for the request, if any. - :param response_prompt_tokens: The number of tokens measured in the prompt - for the response, if any. - :param response_output_tokens: The number of tokens measured in the output - for the response, if any. - :param request_id: The unique identifier for the request, if any. - :param error: The error message, if any, returned from making the request. - """ - - value: str - request_args: RequestArgs - iterations: int = 0 - start_time: float - end_time: float - first_iter_time: Optional[float] - last_iter_time: Optional[float] - request_prompt_tokens: Optional[int] = None - request_output_tokens: Optional[int] = None - response_prompt_tokens: Optional[int] = None - response_output_tokens: Optional[int] = None - request_id: Optional[str] = None - error: Optional[str] = None - - @computed_field # type: ignore[misc] - @property - def prompt_tokens(self) -> Optional[int]: - """ - The number of tokens measured in the prompt based on preferences - for trusting the input or response. - - :return: The number of tokens in the prompt, if any. - """ - if settings.preferred_prompt_tokens_source == "request": - return self.request_prompt_tokens or self.response_prompt_tokens - - return self.response_prompt_tokens or self.request_prompt_tokens - - @computed_field # type: ignore[misc] - @property - def output_tokens(self) -> Optional[int]: - """ - The number of tokens measured in the output based on preferences - for trusting the input or response. - - :return: The number of tokens in the output, if any. - """ - if self.error is not None: - # error occurred, can't trust request tokens were all generated - return self.response_prompt_tokens - - if settings.preferred_output_tokens_source == "request": - return self.request_output_tokens or self.response_output_tokens - - return self.response_output_tokens or self.request_output_tokens diff --git a/src/guidellm/benchmark/__init__.py b/src/guidellm/benchmark/__init__.py deleted file mode 100644 index f5130711..00000000 --- a/src/guidellm/benchmark/__init__.py +++ /dev/null @@ -1,66 +0,0 @@ -from .aggregator import AggregatorT, BenchmarkAggregator, GenerativeBenchmarkAggregator -from .benchmark import ( - Benchmark, - BenchmarkArgs, - BenchmarkMetrics, - BenchmarkRunStats, - BenchmarkT, - GenerativeBenchmark, - GenerativeMetrics, - GenerativeTextErrorStats, - GenerativeTextResponseStats, - StatusBreakdown, -) -from .benchmarker import Benchmarker, BenchmarkerResult, GenerativeBenchmarker -from .entrypoints import benchmark_generative_text -from .output import GenerativeBenchmarksConsole, GenerativeBenchmarksReport -from .profile import ( - AsyncProfile, - ConcurrentProfile, - Profile, - ProfileType, - SweepProfile, - SynchronousProfile, - ThroughputProfile, - create_profile, -) -from .progress import ( - BenchmarkerProgressDisplay, - BenchmarkerTaskProgressState, - GenerativeTextBenchmarkerProgressDisplay, - GenerativeTextBenchmarkerTaskProgressState, -) - -__all__ = [ - "AggregatorT", - "AsyncProfile", - "Benchmark", - "BenchmarkAggregator", - "BenchmarkArgs", - "BenchmarkMetrics", - "BenchmarkRunStats", - "BenchmarkT", - "Benchmarker", - "BenchmarkerProgressDisplay", - "BenchmarkerResult", - "BenchmarkerTaskProgressState", - "ConcurrentProfile", - "GenerativeBenchmark", - "GenerativeBenchmarkAggregator", - "GenerativeBenchmarker", - "GenerativeBenchmarksConsole", - "GenerativeBenchmarksReport", - "GenerativeMetrics", - "GenerativeTextBenchmarkerProgressDisplay", - "GenerativeTextBenchmarkerTaskProgressState", - "GenerativeTextErrorStats", - "GenerativeTextResponseStats", - "Profile", - "ProfileType", - "StatusBreakdown", - "SweepProfile", - "SynchronousProfile", - "ThroughputProfile", - "benchmark_generative_text", - "create_profile", -] diff --git a/src/guidellm/benchmark/aggregator.py b/src/guidellm/benchmark/aggregator.py deleted file mode 100644 index 9943f169..00000000 --- a/src/guidellm/benchmark/aggregator.py +++ /dev/null @@ -1,760 +0,0 @@ -import time -from abc import ABC, abstractmethod -from pathlib import Path -from typing import ( - Any, - Generic, - Literal, - Optional, - TypeVar, - Union, -) - -from pydantic import Field - -from guidellm.backend import ResponseSummary -from guidellm.benchmark.benchmark import ( - BenchmarkArgs, - BenchmarkRunStats, - BenchmarkT, - GenerativeBenchmark, - GenerativeTextErrorStats, - GenerativeTextResponseStats, -) -from guidellm.config import settings -from guidellm.objects import ( - RunningStats, - StandardBaseModel, - StatusBreakdown, - TimeRunningStats, -) -from guidellm.request import ( - GenerationRequest, - GenerativeRequestLoaderDescription, - RequestLoaderDescription, -) -from guidellm.scheduler import ( - GenerativeRequestsWorkerDescription, - RequestT, - ResponseT, - SchedulerRequestResult, - WorkerDescription, -) -from guidellm.utils import check_load_processor - -__all__ = [ - "AggregatorT", - "BenchmarkAggregator", - "GenerativeBenchmarkAggregator", -] - - -class SchedulerRunningStats(StandardBaseModel): - """ - The metrics for the scheduler stored as running statistics for easy calculations - of rates, averages, totals, etc. - """ - - created_requests: RunningStats = Field( - description=( - "The running statistics for the number of requests created for this " - "benchmark run. This includes all requests created, regardless of " - "their status." - ), - default_factory=RunningStats, - ) - queued_requests: RunningStats = Field( - description=( - "The running statistics for the number of requests pending in queue " - "for this benchmark run. This includes requests that are waiting to " - "be scheduled." - ), - default_factory=RunningStats, - ) - scheduled_requests: RunningStats = Field( - description=( - "The running statistics for the number of requests scheduled (actively " - "running but waiting for the desired start time) for this benchmark run." - ), - default_factory=RunningStats, - ) - processing_requests: RunningStats = Field( - description=( - "The running statistics for the number of requests actively being " - "processed by the worker for this benchmark run." - ), - default_factory=RunningStats, - ) - completed_requests: RunningStats = Field( - description=( - "The running statistics for the number of requests completed for this " - "benchmark run. This includes requests within the warmup and cooldown " - "period, if any, along with the final results." - ), - default_factory=RunningStats, - ) - - -class RequestsRunningStats(StandardBaseModel): - """ - The metrics for requests that have succeeded, been canceled, or errored stored - as running statistics for easy calculations of rates, averages, totals, etc. - """ - - totals: StatusBreakdown[RunningStats, RunningStats, RunningStats, RunningStats] = ( - Field( - description=( - "The running statistics for the total number of requests that " - "completed within the benchmark run." - ), - default_factory=lambda: StatusBreakdown( - successful=RunningStats(), - errored=RunningStats(), - incomplete=RunningStats(), - total=RunningStats(), - ), - ) - ) - queued_time: TimeRunningStats = Field( - description=( - "The running statistics for the time spent in queue for all requests that " - "completed within the benchmark run. This is the time from when the " - "request was created to when it was dequeued by the worker." - ), - default_factory=TimeRunningStats, - ) - scheduled_time_delay: TimeRunningStats = Field( - description=( - "The running statistics for the time spent from when a request was " - "dequeued by the worker to when it was actually scheduled by the worker" - "for all requests that completed within the benchmark run. " - "This should be as close to 0 as possible, any additional time is " - "overheads from the system or the worker." - ), - default_factory=TimeRunningStats, - ) - scheduled_time_sleep: TimeRunningStats = Field( - description=( - "The running statistics for the time for each request spent sleeping til " - "the desired start time was reached for all requests that completed within " - "the benchmark run. This is the time from when the request was scheduled " - "to when the desired start time was reached. " - ), - default_factory=TimeRunningStats, - ) - worker_start_delay: TimeRunningStats = Field( - description=( - "The running statistics for the time delay between when the request was " - "scheduled and when the worker actually started processing subtracting any " - "sleep time for all requests that completed within the benchmark run. " - "This should be as close to 0 as possible, any additional time is " - "overheads from the system or the worker." - ), - default_factory=TimeRunningStats, - ) - worker_time: TimeRunningStats = Field( - description=( - "The running statistics for the time spent processing all requests that " - "completed within the benchmark run. This is the time from when the " - "request was started to when it was completed." - ), - default_factory=TimeRunningStats, - ) - worker_start_time_targeted_delay: TimeRunningStats = Field( - description=( - "The running statistics for the delay between the targeted start time and " - "the actual start time for requests that completed within the benchmark " - "run. This represents delays from the best case desired start time. " - "For async strategies, this represents delays from the ideal system. " - "For sync strategies, since those are doubled in queue, this should be " - "as close to the time for a request to be processed as possible." - ), - default_factory=TimeRunningStats, - ) - request_start_time_delay: TimeRunningStats = Field( - description=( - "The running statistics for the delay between the actual request being " - "made and the time the worker started on the request for all requests " - "that completed within the benchmark run. This time should be as close to " - "0 as possible, any additional time is overhead from the system or " - "the worker." - ), - default_factory=TimeRunningStats, - ) - request_start_time_targeted_delay: TimeRunningStats = Field( - description=( - "The running statistics for the delay between the targeted start time and " - "the actual start time for all requests that completed within the " - "benchmark run. This represents delays from the best case desired start " - "time. For async strategies, this represents delays from the ideal system. " - "For sync strategies, since those are duplicated in queue, this should be " - "as close to the time for a request to be processed." - ), - default_factory=TimeRunningStats, - ) - request_time_delay: TimeRunningStats = Field( - description=( - "The running statistics for the delay in time between the total request " - "time and the worker time. This should be as close to 0 as possible, any " - "additional time is overhead from the system or the worker. " - ), - default_factory=TimeRunningStats, - ) - request_time: TimeRunningStats = Field( - description=( - "The running statistics for the time spent processing all requests that " - "completed within the benchmark run. This is the time from when the " - "request was created to when it was completed." - ), - default_factory=TimeRunningStats, - ) - - -class BenchmarkAggregator( - ABC, StandardBaseModel, Generic[BenchmarkT, RequestT, ResponseT] -): - """ - A pydantic base class representing the base class for aggregating benchmark results. - The purpose is to receive and process results from a Benchmarker as it iterates - through a Scheduler for an individual benchmark run. - As results are added, lightweight statistics are updated and stored for immediate - progress and informational updates to the caller. - Once the benchmark run is complete, the `compile` method is called to finalize - the benchmark and return a Benchmark object with all the results and statistics - fully calculated. - """ - - type_: Literal["benchmark_aggregator"] = "benchmark_aggregator" - run_id: str = Field( - description=( - "The unique identifier for the encompasing benchmark run that this " - "benchmark was a part of." - ) - ) - args: BenchmarkArgs = Field( - description=( - "The arguments used to create the benchmark run that this benchmark was " - "a part of." - ) - ) - worker_description: Union[ - GenerativeRequestsWorkerDescription, WorkerDescription - ] = Field( - description=( - "The description and specifics for the worker used to resolve requests " - "for this benchmark." - ), - discriminator="type_", - ) - request_loader_description: Union[ - GenerativeRequestLoaderDescription, RequestLoaderDescription - ] = Field( - description=( - "The description and specifics for the request loader used to create " - "requests for this benchmark." - ), - discriminator="type_", - ) - extras: dict[str, Any] = Field( - description=( - "Any additional information or metadata that was passed for this benchmark." - ) - ) - in_warmup: bool = Field( - description=( - "A flag to indicate if the benchmark is currently in the warmup phase." - ), - default=False, - exclude=True, - ) - in_cooldown: bool = Field( - description=( - "A flag to indicate if the benchmark is currently in the cooldown phase." - ), - default=False, - exclude=True, - ) - scheduler_stats: SchedulerRunningStats = Field( - description=( - "The running statistics for the scheduler for this benchmark run. " - "This includes all requests created, regardless of their status." - ), - default_factory=SchedulerRunningStats, - ) - requests_stats: RequestsRunningStats = Field( - description=( - "The running statistics for the requests for this benchmark run. " - "This includes all requests created, regardless of their status." - ), - default_factory=RequestsRunningStats, - ) - results: StatusBreakdown[ - list[SchedulerRequestResult[RequestT, ResponseT]], - list[SchedulerRequestResult[RequestT, ResponseT]], - list[SchedulerRequestResult[RequestT, ResponseT]], - None, - ] = Field( - description=( - "The completed requests for this benchmark run broken down by status" - "and excluding warmup and cooldown requests." - ), - default_factory=lambda: StatusBreakdown( # type: ignore[arg-type] - successful=[], - errored=[], - incomplete=[], - total=None, - ), - ) - - def add_result( - self, - result: SchedulerRequestResult[RequestT, ResponseT], - ) -> bool: - """ - Add a result to the aggregator. This will update the internal statistics - and add the result to the list of results if it is not within the warmup or - cooldown period. - - :param result: The result to add to the aggregator. - :return: True if the result was added, False if it was added because it - did not fit within the warmup or cooldown period, was not requested, - or is not finished - """ - # Add scheduler statistics - self.scheduler_stats.created_requests += max( - 0, result.run_info.created_requests - ) - self.scheduler_stats.queued_requests += max(0, result.run_info.queued_requests) - self.scheduler_stats.scheduled_requests += max( - 0, result.run_info.scheduled_requests - ) - self.scheduler_stats.processing_requests += max( - 0, result.run_info.processing_requests - ) - self.scheduler_stats.completed_requests += max( - 0, result.run_info.completed_requests - ) - - if result.type_ != "request_complete" or ( - result.request_info.canceled and not result.request_info.requested - ): - # If the result is not completed yet, don't add to the results - # If the result was canceled and not started, ignore it - return False - - # Add request statistics - self.requests_stats.totals.total += 1 - if result.request_info.canceled: - self.requests_stats.totals.incomplete += 1 - elif result.request_info.errored: - self.requests_stats.totals.errored += 1 - elif result.request_info.completed: - self.requests_stats.totals.successful += 1 - else: - raise ValueError( - "Unexpected state: request_info must be either " - "completed, canceled, or errored. " - f"Got {result.request_info}" - ) - - self.requests_stats.queued_time.update( - result.request_info.dequeued_time - result.request_info.queued_time - ) - self.requests_stats.scheduled_time_delay.update( - result.request_info.scheduled_time - result.request_info.dequeued_time - ) - sleep_time = max( - 0.0, - result.request_info.targeted_start_time - - result.request_info.scheduled_time, - ) - self.requests_stats.scheduled_time_sleep.update(sleep_time) - time_to_worker_start = ( - result.request_info.worker_start - result.request_info.scheduled_time - ) - self.requests_stats.worker_start_delay.update(time_to_worker_start - sleep_time) - self.requests_stats.worker_time.update( - result.request_info.worker_end - result.request_info.worker_start - ) - self.requests_stats.worker_start_time_targeted_delay.update( - result.request_info.worker_start - result.request_info.targeted_start_time - ) - self.requests_stats.request_start_time_delay.update( - result.request_info.worker_start - result.request_info.targeted_start_time - ) - self.requests_stats.request_start_time_targeted_delay.update( - result.request_info.worker_start - result.request_info.targeted_start_time - ) - self.requests_stats.request_time_delay.update( - (result.request_info.worker_end - result.request_info.worker_start) - - (result.request_info.worker_end - result.request_info.worker_start) - ) - self.requests_stats.request_time.update( - result.request_info.worker_end - result.request_info.worker_start - ) - - # Add result to the list of results provided we are not in warmup or cooldown - total_completed = self.requests_stats.totals.total.total - global_start_time = self.requests_stats.totals.total.start_time - - in_warmup_number = ( - self.args.warmup_number and total_completed <= self.args.warmup_number - ) - in_warmup_duration = ( - self.args.warmup_duration - and result.request_info.worker_start - <= (global_start_time - self.args.warmup_duration) - ) - - if in_warmup_number or in_warmup_duration: - self.in_warmup = True - return True - - self.in_warmup = False - in_cooldown_number = ( - self.args.cooldown_number - and self.args.max_number - and total_completed > self.args.max_number - self.args.cooldown_number - ) - in_cooldown_duration = ( - self.args.cooldown_duration - and self.args.max_duration - and result.request_info.worker_start - > global_start_time + self.args.max_duration - self.args.cooldown_duration - ) - - if in_cooldown_number or in_cooldown_duration: - self.in_cooldown = True - return True - - self.in_cooldown = False - - if result.request_info.canceled: - self.results.incomplete.append(result) - elif result.request_info.errored: - self.results.errored.append(result) - elif result.request_info.completed: - self.results.successful.append(result) - else: - raise ValueError( - "Unexpected state: request_info must be either " - "completed, canceled, or errored. " - f"Got {result.request_info}" - ) - - return True - - @abstractmethod - def compile(self) -> BenchmarkT: - """ - Compile the benchmark results and statistics into a Benchmark object. - This is required to be implemented by subclasses to finalize the benchmark - and return the compiled object. - """ - ... - - -AggregatorT = TypeVar("AggregatorT", bound=BenchmarkAggregator) - - -class GenerativeRequestsRunningStats(RequestsRunningStats): - """ - The metrics for generative requests that have succeeded, been canceled, or errored - stored as running statistics for easy calculations of rates, averages, totals, etc. - """ - - time_to_first_token: TimeRunningStats = Field( - description=( - "The running statistics for the time from the start of the request to the " - "first token being generated for all requests that completed within the " - "benchmark run." - ), - default_factory=TimeRunningStats, - ) - inter_token_latency: TimeRunningStats = Field( - description=( - "The running statistics for the time between each token being generated " - "for all requests that completed within the benchmark run." - ), - default_factory=TimeRunningStats, - ) - prompt_tokens: RunningStats = Field( - description=( - "The running statistics for the token count for the prompt for all " - "requests that completed, if available in the response." - ), - default_factory=RunningStats, - ) - output_tokens: RunningStats = Field( - description=( - "The running statistics for the token count for the output for all " - "requests that completed, if available in the response." - ), - default_factory=RunningStats, - ) - total_tokens: RunningStats = Field( - description=( - "The running statistics for the total token count for all requests that " - "completed, if available in the response." - ), - default_factory=RunningStats, - ) - - -class GenerativeBenchmarkAggregator( - BenchmarkAggregator[GenerativeBenchmark, GenerationRequest, ResponseSummary] -): - type_: Literal["generative_benchmark_aggregator"] = ( - "generative_benchmark_aggregator" # type: ignore[assignment] - ) - processor: Optional[Union[str, Path, Any]] = Field( - description=( - "The tokenizer to use for calculating token counts when none are " - "avaiable that match the preferred source." - ) - ) - processor_args: Optional[dict[str, Any]] = Field( - description=( - "Additional arguments to pass to the tokenizer if it requires " - "any specific configuration for loading or processing." - ), - ) - worker_description: GenerativeRequestsWorkerDescription = Field( - description=( - "The description and specifics for the worker used to resolve requests " - "for this benchmark." - ), - discriminator="type_", - ) - request_loader_description: GenerativeRequestLoaderDescription = Field( - description=( - "The description and specifics for the request loader used to create " - "requests for this benchmark." - ), - discriminator="type_", - ) - requests_stats: GenerativeRequestsRunningStats = Field( - description=( - "The running statistics for the requests for this benchmark run. " - "This includes all requests created, regardless of their status." - ), - default_factory=GenerativeRequestsRunningStats, - ) - - def add_result( - self, result: SchedulerRequestResult[GenerationRequest, ResponseSummary] - ) -> bool: - """ - Add a result to the aggregator. This will update the internal statistics - and add the result to the list of results if it is not within the warmup or - cooldown period. - - :param result: The result to add to the aggregator. - """ - if not super().add_result(result): - return False - - if result.request is None: - raise ValueError("Request is None, cannot add result.") - - if result.response is None: - raise ValueError("Response is None, cannot add result.") - - self.requests_stats.request_start_time_delay.update( - result.response.start_time - result.request_info.worker_start - ) - self.requests_stats.request_start_time_targeted_delay.update( - result.response.start_time - result.request_info.targeted_start_time - ) - self.requests_stats.request_time_delay.update( - (result.response.start_time - result.request_info.worker_start) - + result.request_info.worker_end - - result.response.end_time - ) - self.requests_stats.request_time.update( - result.response.end_time - result.response.start_time - ) - if result.response.first_iter_time: - self.requests_stats.time_to_first_token.update( - result.response.first_iter_time - result.response.start_time - ) - if result.response.last_iter_time and result.response.first_iter_time: - self.requests_stats.inter_token_latency.update( - result.response.last_iter_time - result.response.first_iter_time, - count=(result.response.output_tokens or 1) - 1, - ) - self.requests_stats.prompt_tokens += result.response.request_prompt_tokens or 0 - self.requests_stats.output_tokens += result.response.request_output_tokens or 0 - total_tokens = (result.response.request_prompt_tokens or 0) + ( - result.response.request_output_tokens or 0 - ) - self.requests_stats.total_tokens += total_tokens - - return True - - def compile(self) -> GenerativeBenchmark: - """ - Compile the benchmark results and statistics into a GenerativeBenchmark object. - This is required to be implemented by subclasses to finalize the benchmark - and return the compiled object. - """ - successful, incomplete, errored = self._compile_results() - - return GenerativeBenchmark.from_stats( - run_id=self.run_id, - successful=successful, - incomplete=incomplete, - errored=errored, - args=self.args, - run_stats=BenchmarkRunStats( - start_time=self.requests_stats.totals.total.start_time, - end_time=time.time(), - requests_made=StatusBreakdown( - successful=int(self.requests_stats.totals.successful.total), - errored=int(self.requests_stats.totals.errored.total), - incomplete=int(self.requests_stats.totals.incomplete.total), - total=int(self.requests_stats.totals.total.total), - ), - queued_time_avg=self.requests_stats.queued_time.mean, - scheduled_time_delay_avg=self.requests_stats.scheduled_time_delay.mean, - scheduled_time_sleep_avg=self.requests_stats.scheduled_time_sleep.mean, - worker_start_delay_avg=self.requests_stats.worker_start_delay.mean, - worker_time_avg=self.requests_stats.worker_time.mean, - worker_start_time_targeted_delay_avg=self.requests_stats.worker_start_time_targeted_delay.mean, - request_start_time_delay_avg=self.requests_stats.request_start_time_delay.mean, - request_start_time_targeted_delay_avg=self.requests_stats.request_start_time_targeted_delay.mean, - request_time_delay_avg=self.requests_stats.request_time_delay.mean, - request_time_avg=self.requests_stats.request_time.mean, - ), - worker=self.worker_description, - requests_loader=self.request_loader_description, - extras=self.extras, - ) - - def _compile_results( - self, - ) -> tuple[ - list[GenerativeTextResponseStats], - list[GenerativeTextErrorStats], - list[GenerativeTextErrorStats], - ]: - successful: list[GenerativeTextResponseStats] = [ - GenerativeTextResponseStats( - request_id=result.request.request_id, - request_type=result.request.request_type, - scheduler_info=result.request_info, - prompt=str(result.request.content), - prompt_tokens=self._compile_tokens_count( - value=str(result.request.content), - requests_tokens=result.response.request_prompt_tokens, - response_tokens=result.response.response_prompt_tokens, - preferred_tokens_source=settings.preferred_prompt_tokens_source, - errored=False, - ), - output=result.response.value, - output_tokens=self._compile_tokens_count( - value=result.response.value, - requests_tokens=result.response.request_output_tokens, - response_tokens=result.response.response_output_tokens, - preferred_tokens_source=settings.preferred_output_tokens_source, - errored=False, - ), - start_time=result.response.start_time, - end_time=result.response.end_time, - first_token_time=result.response.first_iter_time or -1.0, - last_token_time=result.response.last_iter_time or -1.0, - ) - for result in self.results.successful - if result.request and result.response - ] - incomplete: list[GenerativeTextErrorStats] = [ - GenerativeTextErrorStats( - error=result.response.error or "", - request_id=result.request.request_id, - request_type=result.request.request_type, - scheduler_info=result.request_info, - prompt=str(result.request.content), - prompt_tokens=self._compile_tokens_count( - value=str(result.request.content), - requests_tokens=result.response.request_prompt_tokens, - response_tokens=result.response.response_prompt_tokens, - preferred_tokens_source=settings.preferred_prompt_tokens_source, - errored=True, - ), - output=result.response.value, - output_tokens=self._compile_tokens_count( - value=result.response.value, - requests_tokens=result.response.request_output_tokens, - response_tokens=result.response.response_output_tokens, - preferred_tokens_source=settings.preferred_output_tokens_source, - errored=True, - ), - start_time=result.response.start_time, - end_time=result.response.end_time, - first_token_time=result.response.first_iter_time, - last_token_time=result.response.last_iter_time, - ) - for result in self.results.incomplete - if result.request and result.response - ] - error: list[GenerativeTextErrorStats] = [ - GenerativeTextErrorStats( - error=result.response.error or "", - request_id=result.request.request_id, - request_type=result.request.request_type, - scheduler_info=result.request_info, - prompt=str(result.request.content), - prompt_tokens=self._compile_tokens_count( - value=str(result.request.content), - requests_tokens=result.response.request_prompt_tokens, - response_tokens=result.response.response_prompt_tokens, - preferred_tokens_source=settings.preferred_prompt_tokens_source, - errored=True, - ), - output=result.response.value, - output_tokens=self._compile_tokens_count( - value=result.response.value, - requests_tokens=result.response.request_output_tokens, - response_tokens=result.response.response_output_tokens, - preferred_tokens_source=settings.preferred_output_tokens_source, - errored=True, - ), - start_time=result.response.start_time, - end_time=result.response.end_time, - first_token_time=result.response.first_iter_time, - last_token_time=result.response.last_iter_time, - ) - for result in self.results.errored - if result.request and result.response - ] - - return successful, incomplete, error - - def _compile_tokens_count( - self, - value: str, - requests_tokens: Optional[int], - response_tokens: Optional[int], - preferred_tokens_source: Optional[Literal["request", "response", "local"]], - errored: bool, - ) -> int: - if not errored and preferred_tokens_source == "response" and response_tokens: - return response_tokens or 0 - - if not errored and preferred_tokens_source == "request" and requests_tokens: - return requests_tokens or 0 - - if preferred_tokens_source in {"response", "request"} and ( - self.processor is None or errored or response_tokens or requests_tokens - ): - # we had a preferred tokens source that isn't local and we either - # have the data to return something or we don't have the ability - # to calculate locally - return response_tokens or requests_tokens or 0 - - self.processor = check_load_processor( - self.processor, - processor_args=self.processor_args, - error_msg="Processor/Tokenizer is required for calculating token counts.", - ) - return len(self.processor.tokenize(value)) diff --git a/src/guidellm/benchmark/benchmark.py b/src/guidellm/benchmark/benchmark.py deleted file mode 100644 index 4e2e09a3..00000000 --- a/src/guidellm/benchmark/benchmark.py +++ /dev/null @@ -1,838 +0,0 @@ -import random -import uuid -from typing import Any, Literal, Optional, TypeVar, Union - -from pydantic import Field, computed_field - -from guidellm.benchmark.profile import ( - AsyncProfile, - ConcurrentProfile, - Profile, - SweepProfile, - SynchronousProfile, - ThroughputProfile, -) -from guidellm.objects import ( - StandardBaseModel, - StatusBreakdown, - StatusDistributionSummary, -) -from guidellm.request import ( - GenerativeRequestLoaderDescription, - RequestLoaderDescription, -) -from guidellm.scheduler import ( - AsyncConstantStrategy, - AsyncPoissonStrategy, - ConcurrentStrategy, - GenerativeRequestsWorkerDescription, - SchedulerRequestInfo, - SchedulingStrategy, - SynchronousStrategy, - ThroughputStrategy, - WorkerDescription, -) - -__all__ = [ - "Benchmark", - "BenchmarkArgs", - "BenchmarkMetrics", - "BenchmarkRunStats", - "BenchmarkT", - "GenerativeBenchmark", - "GenerativeMetrics", - "GenerativeTextErrorStats", - "GenerativeTextResponseStats", - "StatusBreakdown", -] - - -class BenchmarkArgs(StandardBaseModel): - """ - A serializable model representing the arguments used to specify a benchmark run - and how data was collected for it. - """ - - profile: Union[ - AsyncProfile, - SweepProfile, - ConcurrentProfile, - ThroughputProfile, - SynchronousProfile, - Profile, - ] = Field( - description=( - "The profile used for the entire benchmark run that the strategy for " - "this benchmark was pulled from." - ), - discriminator="type_", - ) - strategy_index: int = Field( - description=( - "The index of the strategy in the profile that was used for this benchmark." - ) - ) - strategy: Union[ - ConcurrentStrategy, - SchedulingStrategy, - ThroughputStrategy, - SynchronousStrategy, - AsyncPoissonStrategy, - AsyncConstantStrategy, - SchedulingStrategy, - ] = Field( - description="The scheduling strategy used to run this benchmark. ", - discriminator="type_", - ) - max_number: Optional[int] = Field( - description="The maximum number of requests to run for this benchmark, if any." - ) - max_duration: Optional[float] = Field( - description="The maximum duration in seconds to run this benchmark, if any." - ) - warmup_number: Optional[int] = Field( - description=( - "The number of requests to run for the warmup phase of this benchmark, " - "if any. These are requests that were not included in the final results." - ) - ) - warmup_duration: Optional[float] = Field( - description=( - "The duration in seconds to run for the warmup phase of this benchmark, " - "if any. These are requests that were not included in the final results." - ) - ) - cooldown_number: Optional[int] = Field( - description=( - "The number of requests to run for the cooldown phase of this benchmark, " - "if any. These are requests that were not included in the final results." - ) - ) - cooldown_duration: Optional[float] = Field( - description=( - "The duration in seconds to run for the cooldown phase of this benchmark, " - "if any. These are requests that were not included in the final results." - ) - ) - - -class BenchmarkRunStats(StandardBaseModel): - """ - A serializable model representing the run process statistics for the - entire benchmark run across all requests including warmup and cooldown. - """ - - start_time: float = Field( - description="The start time of the benchmark run.", - ) - end_time: float = Field( - description="The end time of the benchmark run.", - ) - requests_made: StatusBreakdown[int, int, int, int] = Field( - description=( - "The number of requests made for the benchmark run broken down by " - "status including successful, incomplete, errored, and the sum of all three" - ) - ) - queued_time_avg: float = Field( - description=( - "The average time spent in the queue for each request in the benchmark " - "run until it was dequeued by a worker." - ) - ) - scheduled_time_delay_avg: float = Field( - description=( - "The average time delay between when a request was dequeued and when it " - "was scheduled to be processed by a worker in the benchmark run. " - "This should be as close to 0 as possible, any additional time is " - "overheads from the system or the worker." - ) - ) - scheduled_time_sleep_avg: float = Field( - description=( - "The average time spent sleeping til the desired start time was reached " - "after being scheduled by the worker in the benchmark run." - ) - ) - worker_start_delay_avg: float = Field( - description=( - "The average time delay between when a request was scheduled and when " - "the worker started processing it in the benchmark run. " - "This should be as close to 0 as possible, any additional time is " - "overheads from the system or the worker." - ) - ) - worker_time_avg: float = Field( - description=( - "The average time taken by the worker to process each request in the " - "benchmark run. This includes the time to generate the response and " - "any additional processing time." - ) - ) - worker_start_time_targeted_delay_avg: float = Field( - description=( - "The average time delay between when a request was targeted to start " - "and when the worker actually started processing it in the benchmark " - "run. For async strategies, this represents delays from the ideal " - "system. For sync strategies, since those are doubled in queue, " - "this should be as close to the time for a request to be processed " - "as possible. Any additional time is overhead from the system or " - "the worker." - ) - ) - request_start_time_delay_avg: float = Field( - description=( - "The average time delay between the actual request being made " - "and the time the worker started on the request for all requests " - "that completed within the benchmark run. This time should be as close " - "to 0 as possible, any additional time is overhead from the system or " - "the worker." - ) - ) - request_start_time_targeted_delay_avg: float = Field( - description=( - "The average time delay between when the targeted start time and " - "the actual start time for each request in the benchmark run. " - "For async strategies, this represents delays from the ideal " - "system. For sync strategies, this should be as close to the " - "time for a request to be processed as possible. Any additional " - "time is overhead from the system or the worker." - ) - ) - request_time_delay_avg: float = Field( - description=( - "The average time delay between the total request time and the " - "worker time. This should be as close to 0 as possible, any additional " - "time is overhead from the system or the worker. " - ) - ) - request_time_avg: float = Field( - description=( - "The average time spent processing all requests in the benchmark run. " - "This is the time from when the actual request was started to when " - "it was completed." - ) - ) - - -class BenchmarkMetrics(StandardBaseModel): - """ - A serializable model representing the metrics for a benchmark run. - """ - - requests_per_second: StatusDistributionSummary = Field( - description="The distribution of requests per second for the benchmark.", - ) - request_concurrency: StatusDistributionSummary = Field( - description="The distribution of requests concurrency for the benchmark.", - ) - - -class Benchmark(StandardBaseModel): - """ - The base serializable model representing a benchmark run and its results. - Specific benchmarker implementations should extend this model to include - additional information or metadata as needed. - - Note, requests_per_second and request_concurrency are kept at this level - and are expected to be populated by the subclass implementation to ensure - the logic for Profiles can include more complicated logic for determining - what rates and concurrency values to use for subsequent strategies. - """ - - type_: Literal["benchmark"] = "benchmark" - id_: str = Field( - default_factory=lambda: str(uuid.uuid4()), - description="The unique identifier for the benchmark.", - ) - run_id: str = Field( - description=( - "The unique identifier for the encompasing benchmark run that this " - "benchmark was a part of." - ) - ) - args: BenchmarkArgs = Field( - description=( - "The arguments used to specify how to run the benchmark and collect data." - ) - ) - run_stats: BenchmarkRunStats = Field( - description=( - "The process statistics for the entire benchmark run across all requests." - ) - ) - worker: Union[WorkerDescription] = Field( - description=( - "The description and specifics for the worker used to resolve requests " - "for this benchmark." - ), - ) - request_loader: Union[RequestLoaderDescription] = Field( - description=( - "The description and specifics for the request loader used to create " - "requests for this benchmark." - ), - ) - extras: dict[str, Any] = Field( - description=( - "Any additional information or metadata that was passed for this benchmark." - ) - ) - metrics: BenchmarkMetrics = Field( - description=( - "The metrics for the benchmark run represented as a distribution of " - "various per-request statistics." - ), - ) - - -BenchmarkT = TypeVar("BenchmarkT", bound=Benchmark) - - -class GenerativeTextResponseStats(StandardBaseModel): - """ - A serializable model representing the request values, response values, and - statistics for a generative text response. - """ - - type_: Literal["generative_text_response"] = "generative_text_response" - request_id: Optional[str] = Field( - description="The unique identifier for the request.", - ) - request_type: Literal["text_completions", "chat_completions"] = Field( - description="The type of request made to the generative backend." - ) - scheduler_info: SchedulerRequestInfo = Field( - description=( - "The info about the request from the scheduler about how it was run." - ), - ) - prompt: str = Field( - description="The text prompt used for the generative request.", - ) - output: str = Field( - description="The generated text output from the generative request.", - ) - prompt_tokens: int = Field( - description="The number of tokens in the prompt text.", - ) - output_tokens: int = Field( - description="The number of tokens in the generated output text.", - ) - start_time: float = Field( - description="The time the request started.", - ) - end_time: float = Field( - description="The time the request ended.", - ) - first_token_time: float = Field( - description="The time the first token was received.", - ) - last_token_time: float = Field( - description="The time the last token was received.", - ) - - @computed_field # type: ignore[misc] - @property - def request_latency(self) -> float: - """ - :return: The duration of the request in seconds from the start to the end. - """ - return self.end_time - self.start_time - - @computed_field # type: ignore[misc] - @property - def time_to_first_token_ms(self) -> float: - """ - :return: The time in milliseconds from the start of the request to the first - token received. - """ - return 1000 * (self.first_token_time - self.start_time) - - @computed_field # type: ignore[misc] - @property - def time_per_output_token_ms(self) -> float: - """ - :return: The average time in milliseconds per output token generated. - This includes the time to generate the first token and all other tokens. - """ - if self.output_tokens == 0: - return 0.0 - - return ( - 1000 * (self.last_token_time - self.first_token_time) / self.output_tokens - ) - - @computed_field # type: ignore[misc] - @property - def inter_token_latency_ms(self) -> float: - """ - :return: The average time in milliseconds between generating tokens in the - output text. Note, does not include the time to generate the first token. - """ - if self.output_tokens <= 1: - return 0.0 - - return ( - 1000 - * (self.last_token_time - self.first_token_time) - / (self.output_tokens - 1) - ) - - @computed_field # type: ignore[misc] - @property - def tokens_per_second(self) -> float: - """ - :return: The average number of tokens generated per second in the prompt and - output text. - """ - if (latency := self.request_latency) == 0.0: - return 0.0 - - return (self.prompt_tokens + self.output_tokens) / latency - - @computed_field # type: ignore[misc] - @property - def output_tokens_per_second(self) -> float: - """ - :return: The average number of output tokens generated per second. - """ - if (latency := self.request_latency) == 0.0: - return 0.0 - - return self.output_tokens / latency - - -class GenerativeTextErrorStats(GenerativeTextResponseStats): - """ - A serializable model representing the request values, response values, and - statistics for a generative text response that errored. - Extends and overrides the GenerativeTextResponseStats model to include the - error message and optional properties given the error occurred. - """ - - type_: Literal["generative_text_error"] = "generative_text_error" # type: ignore[assignment] - error: str = Field( - description=( - "The error message for the error that occurred while making the request." - ) - ) - output: Optional[str] = Field( # type: ignore[assignment] - default=None, - description=( - "The generated text output from the generative request, if any, " - "before the error occurred." - ), - ) - first_token_time: Optional[float] = Field( # type: ignore[assignment] - default=None, - description=( - "The time the first token was received, if any, before the error occurred." - ), - ) - last_token_time: Optional[float] = Field( # type: ignore[assignment] - default=None, - description=( - "The time the last token was received, if any, before the error occurred." - ), - ) - - @computed_field # type: ignore[misc] - @property - def time_to_first_token_ms(self) -> Optional[float]: # type: ignore[override] - """ - :return: The time in milliseconds from the start of the request to the first - token received. None if the first token was not received. - """ - if self.first_token_time is None: - return None - - return super().time_to_first_token_ms - - @computed_field # type: ignore[misc] - @property - def time_per_output_token_ms(self) -> Optional[float]: # type: ignore[override] - """ - :return: The average time in milliseconds per output token generated. - This includes the time to generate the first token and all other tokens. - None if the output_tokens is None or 0. - """ - if ( - self.output_tokens is None - or self.output_tokens == 0 - or self.first_token_time is None - or self.last_token_time is None - ): - return None - - return super().time_per_output_token_ms - - @computed_field # type: ignore[misc] - @property - def inter_token_latency_ms(self) -> Optional[float]: # type: ignore[override] - """ - :return: The average time in milliseconds between generating tokens in the - output text. Note, does not include the time to generate the first token. - None if there were no output_tokens or the first token was not received. - """ - if ( - self.output_tokens is None - or self.first_token_time is None - or self.last_token_time is None - ): - return None - - return super().inter_token_latency_ms - - @computed_field # type: ignore[misc] - @property - def output_tokens_per_second(self) -> Optional[float]: # type: ignore[override] - """ - :return: The average number of tokens generated per second in the output text. - Note, does not include the time to generate the first token. None if there - were no output_tokens or the first token was not received. - """ - if self.inter_token_latency_ms is None: - return None - - return super().output_tokens_per_second - - -class GenerativeMetrics(BenchmarkMetrics): - """ - A serializable model representing the metrics for a generative benchmark run. - """ - - request_latency: StatusDistributionSummary = Field( - description="The distribution of latencies for the completed requests.", - ) - prompt_token_count: StatusDistributionSummary = Field( - description=( - "The distribution of token counts in the prompts for completed, " - "errored, and all requests." - ) - ) - output_token_count: StatusDistributionSummary = Field( - description=( - "The distribution of token counts in the outputs for completed, " - "errored, and all requests." - ) - ) - time_to_first_token_ms: StatusDistributionSummary = Field( - description=( - "The distribution of latencies to receiving the first token in " - "milliseconds for completed, errored, and all requests." - ), - ) - time_per_output_token_ms: StatusDistributionSummary = Field( - description=( - "The distribution of latencies per output token in milliseconds for " - "completed, errored, and all requests. " - "This includes the time to generate the first token and all other tokens." - ), - ) - inter_token_latency_ms: StatusDistributionSummary = Field( - description=( - "The distribution of latencies between tokens in milliseconds for " - "completed, errored, and all requests." - ), - ) - output_tokens_per_second: StatusDistributionSummary = Field( - description=( - "The distribution of output tokens per second for completed, " - "errored, and all requests." - ), - ) - tokens_per_second: StatusDistributionSummary = Field( - description=( - "The distribution of tokens per second, including prompt and output tokens " - "for completed, errored, and all requests." - ), - ) - - -class GenerativeBenchmark(Benchmark): - """ - A serializable model representing a benchmark run and its results for generative - requests and responses. Includes the completed and errored requests, the start - and end times for the benchmark, and the statistics for the requests and responses. - """ - - type_: Literal["generative_benchmark"] = "generative_benchmark" # type: ignore[assignment] - start_time: float = Field( - description="The start time of the first request for the benchmark.", - ) - end_time: float = Field( - description="The end time of the last request for the benchmark.", - ) - - @computed_field # type: ignore[misc] - @property - def duration(self) -> float: - """ - :return: The duration of the benchmark in seconds from the start of the - first request to the end of the last request. - """ - return self.end_time - self.start_time - - worker: GenerativeRequestsWorkerDescription = Field( - description=( - "The description and specifics for the worker used to resolve requests " - "for this benchmark." - ), - ) - request_loader: GenerativeRequestLoaderDescription = Field( - description=( - "The description and specifics for the request loader used to create " - "requests for this benchmark." - ), - ) - metrics: GenerativeMetrics = Field( - description=( - "The metrics for the benchmark run represented as a distribution of " - "various per-request statistics." - ), - ) - # Output is ordered so keep the requests at the end for better readability in files - request_totals: StatusBreakdown[int, int, int, int] = Field( - description=( - "The number of requests made for the benchmark broken down by status " - "including successful, incomplete, errored, and the sum of all three" - ) - ) - request_samples: Optional[StatusBreakdown[int, int, int, None]] = Field( - description=( - "The number of requests that were randomly sampled for " - "the benchmark. None if no sampling was applied." - ), - default=None, - ) - requests: StatusBreakdown[ - list[GenerativeTextResponseStats], - list[GenerativeTextErrorStats], - list[GenerativeTextErrorStats], - None, - ] = Field( - description=( - "The breakdown of requests for the benchmark run including successful, " - "incomplete, and errored requests." - ), - ) - - def set_sample_size(self, sample_size: Optional[int]) -> "GenerativeBenchmark": - """ - Set the sample size for the benchmark. This will randomly sample the - requests for each status type to the given sample size or the maximum - number of requests for that status type, whichever is smaller. - This is applied to requests.successful, requests.errored, and - requests.incomplete. - If None, no sampling is applied and the state is kept. - - :param sample_size: The number of requests to sample for each status type. - :return: The benchmark with the sampled requests. - :raises ValueError: If the sample size is invalid. - """ - - if sample_size is not None: - if sample_size < 0 or not isinstance(sample_size, int): - raise ValueError( - f"Sample size must be non-negative integer, given {sample_size}" - ) - - sample_size = min(sample_size, len(self.requests.successful)) - error_sample_size = min(sample_size, len(self.requests.errored)) - incomplete_sample_size = min(sample_size, len(self.requests.incomplete)) - - self.requests.successful = random.sample( - self.requests.successful, sample_size - ) - self.requests.errored = random.sample( - self.requests.errored, error_sample_size - ) - self.requests.incomplete = random.sample( - self.requests.incomplete, incomplete_sample_size - ) - self.request_samples = StatusBreakdown( - successful=len(self.requests.successful), - incomplete=len(self.requests.incomplete), - errored=len(self.requests.errored), - ) - - return self - - @staticmethod - def from_stats( - run_id: str, - successful: list[GenerativeTextResponseStats], - incomplete: list[GenerativeTextErrorStats], - errored: list[GenerativeTextErrorStats], - args: BenchmarkArgs, - run_stats: BenchmarkRunStats, - worker: GenerativeRequestsWorkerDescription, - requests_loader: GenerativeRequestLoaderDescription, - extras: Optional[dict[str, Any]], - ) -> "GenerativeBenchmark": - """ - Create a GenerativeBenchmark instance from the given statistics and metadata. - Given the completed and errored requests, the benchmark will fill in the - remaining statistics for the various metrics required for a benchmark. - This is the preferred method for creating a GenerativeBenchmark instance - to ensure all statistics are properly calculated and populated. - - :param run_id: The unique identifier for the benchmark run. - :param completed: The list of completed requests. - :param errored: The list of errored requests. - :param args: The arguments used to specify how to run the benchmark - and collect data. - :param run_stats: The process statistics for the entire benchmark run across - all requests. - :param worker: The description and specifics for the worker used to resolve - requests. - :param requests_loader: The description and specifics for the request loader - used to create requests. - :param extras: Any additional information or metadata that was passed for - this benchmark. - :return: A GenerativeBenchmark instance with the given statistics and metadata - populated and calculated - """ - total = successful + incomplete + errored - total_types: list[Literal["successful", "incomplete", "error"]] = [ - *["successful"] * len(successful), # type: ignore[list-item] - *["incomplete"] * len(incomplete), # type: ignore[list-item] - *["error"] * len(errored), # type: ignore[list-item] - ] - start_time = min(req.start_time for req in total) - end_time = max(req.end_time for req in total) - - total_with_prompt, total_types_with_prompt = ( - zip(*filtered) - if ( - filtered := list( - filter(lambda val: bool(val[0].prompt), zip(total, total_types)) - ) - ) - else ([], []) - ) - total_with_output_first, total_types_with_output_first = ( - zip(*filtered) - if ( - filtered := list( - filter( - lambda val: bool(val[0].output_tokens > 0), - zip(total, total_types), - ) - ) - ) - else ([], []) - ) - total_with_output_multi, total_types_with_output_multi = ( - zip(*filtered) - if ( - filtered := list( - filter( - lambda val: bool(val[0].output_tokens > 1), - zip(total, total_types), - ) - ) - ) - else ([], []) - ) - - return GenerativeBenchmark( - run_id=run_id, - args=args, - run_stats=run_stats, - extras=extras or {}, - start_time=start_time, - end_time=end_time, - worker=worker, - request_loader=requests_loader, - metrics=GenerativeMetrics( - requests_per_second=StatusDistributionSummary.from_request_times( - request_types=total_types, - requests=[(req.start_time, req.end_time) for req in total], - distribution_type="rate", - ), - request_concurrency=StatusDistributionSummary.from_request_times( - request_types=total_types, - requests=[(req.start_time, req.end_time) for req in total], - distribution_type="concurrency", - ), - request_latency=StatusDistributionSummary.from_values( - value_types=total_types, - values=[req.request_latency for req in total], - ), - prompt_token_count=StatusDistributionSummary.from_values( - value_types=list(total_types_with_prompt), - values=[req.prompt_tokens for req in total_with_prompt], - ), - output_token_count=StatusDistributionSummary.from_values( - value_types=list(total_types_with_output_first), - values=[req.output_tokens for req in total_with_output_first], - ), - time_to_first_token_ms=StatusDistributionSummary.from_values( - value_types=list(total_types_with_output_first), - values=[ - req.time_to_first_token_ms or 0 - for req in total_with_output_first - ], - ), - time_per_output_token_ms=StatusDistributionSummary.from_values( - value_types=list(total_types_with_output_first), - values=[ - req.time_per_output_token_ms or 0 - for req in total_with_output_first - ], - weights=[req.output_tokens for req in total_with_output_first], - ), - inter_token_latency_ms=StatusDistributionSummary.from_values( - value_types=list(total_types_with_output_multi), - values=[ - req.inter_token_latency_ms or 0 - for req in total_with_output_multi - ], - weights=[req.output_tokens - 1 for req in total_with_output_multi], - ), - output_tokens_per_second=StatusDistributionSummary.from_iterable_request_times( - request_types=list(total_types_with_output_first), - requests=[ - (req.start_time, req.end_time) - for req in total_with_output_first - ], - first_iter_times=[ - req.first_token_time or req.start_time - for req in total_with_output_first - ], - iter_counts=[req.output_tokens for req in total_with_output_first], - ), - tokens_per_second=StatusDistributionSummary.from_iterable_request_times( - request_types=list(total_types_with_output_first), - requests=[ - (req.start_time, req.end_time) - for req in total_with_output_first - ], - first_iter_times=[ - req.first_token_time or req.start_time - for req in total_with_output_first - ], - iter_counts=[ - req.prompt_tokens + req.output_tokens - for req in total_with_output_first - ], - first_iter_counts=[ - req.prompt_tokens for req in total_with_output_first - ], - ), - ), - request_totals=StatusBreakdown( - successful=len(successful), - incomplete=len(incomplete), - errored=len(errored), - total=len(total), - ), - requests=StatusBreakdown( - successful=successful, - incomplete=incomplete, - errored=errored, - ), - ) diff --git a/src/guidellm/benchmark/benchmarker.py b/src/guidellm/benchmark/benchmarker.py deleted file mode 100644 index 11b6d245..00000000 --- a/src/guidellm/benchmark/benchmarker.py +++ /dev/null @@ -1,334 +0,0 @@ -import time -import uuid -from abc import ABC, abstractmethod -from collections.abc import AsyncGenerator, Iterable -from pathlib import Path -from typing import ( - Any, - Generic, - Literal, - Optional, - Union, -) - -from pydantic import Field -from transformers import PreTrainedTokenizerBase # type: ignore # noqa: PGH003 - -from guidellm.backend import Backend, ResponseSummary -from guidellm.benchmark.aggregator import ( - AggregatorT, - BenchmarkT, - GenerativeBenchmarkAggregator, -) -from guidellm.benchmark.benchmark import BenchmarkArgs, GenerativeBenchmark -from guidellm.benchmark.profile import Profile -from guidellm.objects import StandardBaseModel -from guidellm.request import ( - GenerationRequest, - GenerativeRequestLoaderDescription, - RequestLoaderDescription, -) -from guidellm.scheduler import ( - GenerativeRequestsWorker, - RequestsWorker, - RequestT, - ResponseT, - Scheduler, - SchedulerRequestResult, - SchedulingStrategy, -) - -__all__ = ["Benchmarker", "BenchmarkerResult", "GenerativeBenchmarker"] - - -class BenchmarkerResult( - StandardBaseModel, Generic[AggregatorT, BenchmarkT, RequestT, ResponseT] -): - type_: Literal[ - "run_start", - "run_complete", - "scheduler_start", - "scheduler_update", - "scheduler_complete", - "benchmark_compiled", - ] - start_time: float - end_number: int - profile: Profile - current_index: int - current_strategy: Optional[SchedulingStrategy] = None - current_aggregator: Optional[AggregatorT] = None - current_benchmark: Optional[BenchmarkT] = None - current_result: Optional[SchedulerRequestResult[RequestT, ResponseT]] = None - - -class BenchmarkerStrategyLimits(StandardBaseModel): - requests_loader_size: Optional[int] = Field( - description="Size of the request loader.", - ) - max_number_per_strategy: Optional[int] = Field( - description="Maximum number of requests to process per strategy.", - ge=0, - ) - max_duration_per_strategy: Optional[float] = Field( - description="Maximum duration (in seconds) to process requests per strategy.", - ge=0, - ) - warmup_percent_per_strategy: Optional[float] = Field( - description="Percentage of requests to use for warmup.", - ge=0, - le=1, - ) - cooldown_percent_per_strategy: Optional[float] = Field( - description="Percentage of requests to use for cooldown.", - ge=0, - le=1, - ) - - @property - def max_number(self) -> Optional[int]: - if self.max_number_per_strategy is not None: - return self.max_number_per_strategy - - if self.requests_loader_size is not None: - return self.requests_loader_size - - return None - - @property - def max_duration(self) -> Optional[float]: - return self.max_duration_per_strategy - - @property - def warmup_number(self) -> Optional[int]: - if self.warmup_percent_per_strategy is None or self.max_number is None: - return None - - return int(self.warmup_percent_per_strategy * self.max_number) - - @property - def warmup_duration(self) -> Optional[float]: - if self.warmup_percent_per_strategy is None or self.max_duration is None: - return None - - return self.warmup_percent_per_strategy * self.max_duration - - @property - def cooldown_number(self) -> Optional[int]: - if self.cooldown_percent_per_strategy is None or self.max_number is None: - return None - - return int(self.cooldown_percent_per_strategy * self.max_number) - - @property - def cooldown_duration(self) -> Optional[float]: - if self.cooldown_percent_per_strategy is None or self.max_duration is None: - return None - - return self.cooldown_percent_per_strategy * self.max_duration - - -class Benchmarker(Generic[AggregatorT, BenchmarkT, RequestT, ResponseT], ABC): - def __init__( - self, - worker: RequestsWorker[RequestT, ResponseT], - request_loader: Iterable[RequestT], - requests_loader_description: RequestLoaderDescription, - benchmark_save_extras: Optional[dict[str, Any]] = None, - ): - self.worker = worker - self.scheduler: Scheduler[RequestT, ResponseT] = Scheduler( - worker=worker, request_loader=request_loader - ) - self.requests_loader_description = requests_loader_description - self.benchmark_save_extras = benchmark_save_extras - - async def run( - self, - profile: Profile, - max_number_per_strategy: Optional[int], - max_duration_per_strategy: Optional[float], - warmup_percent_per_strategy: Optional[float], - cooldown_percent_per_strategy: Optional[float], - ) -> AsyncGenerator[ - BenchmarkerResult[AggregatorT, BenchmarkT, RequestT, ResponseT], None - ]: - try: - requests_loader_size = len(self.scheduler.request_loader) # type: ignore[arg-type] - except Exception: # noqa: BLE001 - requests_loader_size = None - - strategy_limits = BenchmarkerStrategyLimits( - requests_loader_size=requests_loader_size, - max_number_per_strategy=max_number_per_strategy, - max_duration_per_strategy=max_duration_per_strategy, - warmup_percent_per_strategy=warmup_percent_per_strategy, - cooldown_percent_per_strategy=cooldown_percent_per_strategy, - ) - start_time = time.time() - end_number = len(profile.strategy_types) - current_index = -1 - run_id = str(uuid.uuid4()) - - yield BenchmarkerResult( - type_="run_start", - start_time=start_time, - end_number=end_number, - profile=profile, - current_index=current_index, - current_strategy=None, - current_aggregator=None, - current_benchmark=None, - current_result=None, - ) - - while scheduling_strategy := profile.next_strategy(): - current_index += 1 - aggregator = self.create_benchmark_aggregator( - run_id=run_id, - profile=profile, - strategy_index=current_index, - strategy=scheduling_strategy, - limits=strategy_limits, - ) - - async for result in self.scheduler.run( - scheduling_strategy=scheduling_strategy, - max_number=max_number_per_strategy, - max_duration=max_duration_per_strategy, - ): - if result.type_ == "run_start": - yield BenchmarkerResult( - type_="scheduler_start", - start_time=start_time, - end_number=end_number, - profile=profile, - current_index=current_index, - current_strategy=scheduling_strategy, - current_aggregator=aggregator, - current_benchmark=None, - current_result=None, - ) - elif result.type_ == "run_complete": - yield BenchmarkerResult( - type_="scheduler_complete", - start_time=start_time, - end_number=end_number, - profile=profile, - current_index=current_index, - current_strategy=scheduling_strategy, - current_aggregator=aggregator, - current_benchmark=None, - current_result=None, - ) - elif isinstance(result, SchedulerRequestResult): - aggregator.add_result(result) - - yield BenchmarkerResult( - type_="scheduler_update", - start_time=start_time, - end_number=end_number, - profile=profile, - current_index=current_index, - current_strategy=scheduling_strategy, - current_aggregator=aggregator, - current_benchmark=None, - current_result=result, - ) - else: - raise ValueError(f"Unexpected result type: {type(result)}") - - benchmark: BenchmarkT = aggregator.compile() - profile.completed_strategy( - average_rate=benchmark.metrics.requests_per_second.successful.mean, - average_concurrency=benchmark.metrics.request_concurrency.successful.mean, - ) - - yield BenchmarkerResult( - type_="benchmark_compiled", - start_time=start_time, - end_number=end_number, - profile=profile, - current_index=current_index, - current_strategy=scheduling_strategy, - current_aggregator=None, - current_benchmark=benchmark, - current_result=None, - ) - - yield BenchmarkerResult( - type_="run_complete", - start_time=start_time, - end_number=end_number, - profile=profile, - current_index=current_index, - current_strategy=None, - current_aggregator=None, - current_benchmark=None, - current_result=None, - ) - - @abstractmethod - def create_benchmark_aggregator( - self, - run_id: str, - profile: Profile, - strategy_index: int, - strategy: SchedulingStrategy, - limits: BenchmarkerStrategyLimits, - ) -> AggregatorT: ... - - -class GenerativeBenchmarker( - Benchmarker[ - GenerativeBenchmarkAggregator, - GenerativeBenchmark, - GenerationRequest, - ResponseSummary, - ], -): - def __init__( - self, - backend: Backend, - request_loader: Iterable[GenerationRequest], - request_loader_description: GenerativeRequestLoaderDescription, - benchmark_save_extras: Optional[dict[str, Any]] = None, - processor: Optional[Union[str, Path, PreTrainedTokenizerBase]] = None, - processor_args: Optional[dict[str, Any]] = None, - ): - super().__init__( - worker=GenerativeRequestsWorker(backend), - request_loader=request_loader, - requests_loader_description=request_loader_description, - benchmark_save_extras=benchmark_save_extras, - ) - self.processor = processor - self.processor_args = processor_args - - def create_benchmark_aggregator( - self, - run_id: str, - profile: Profile, - strategy_index: int, - strategy: SchedulingStrategy, - limits: BenchmarkerStrategyLimits, - ) -> GenerativeBenchmarkAggregator: - return GenerativeBenchmarkAggregator( - run_id=run_id, - args=BenchmarkArgs( - profile=profile, - strategy_index=strategy_index, - strategy=strategy, - max_number=limits.max_number, - max_duration=limits.max_duration, - warmup_number=limits.warmup_number, - warmup_duration=limits.warmup_duration, - cooldown_number=limits.cooldown_number, - cooldown_duration=limits.cooldown_duration, - ), - worker_description=self.worker.description, # type: ignore[arg-type] - request_loader_description=self.requests_loader_description, # type: ignore[arg-type] - extras=self.benchmark_save_extras or {}, - processor=self.processor, - processor_args=self.processor_args, - ) diff --git a/src/guidellm/benchmark/entrypoints.py b/src/guidellm/benchmark/entrypoints.py deleted file mode 100644 index f252cf27..00000000 --- a/src/guidellm/benchmark/entrypoints.py +++ /dev/null @@ -1,141 +0,0 @@ -from collections.abc import Iterable -from pathlib import Path -from typing import Any, Literal, Optional, Union - -from datasets import Dataset, DatasetDict, IterableDataset, IterableDatasetDict -from transformers import ( # type: ignore[import] - PreTrainedTokenizerBase, -) - -from guidellm.backend import Backend, BackendType -from guidellm.benchmark.benchmarker import GenerativeBenchmarker -from guidellm.benchmark.output import ( - GenerativeBenchmarksConsole, - GenerativeBenchmarksReport, -) -from guidellm.benchmark.profile import ProfileType, create_profile -from guidellm.benchmark.progress import GenerativeTextBenchmarkerProgressDisplay -from guidellm.request import GenerativeRequestLoader -from guidellm.scheduler import StrategyType - - -async def benchmark_generative_text( - target: str, - backend_type: BackendType, - backend_args: Optional[dict[str, Any]], - model: Optional[str], - processor: Optional[Optional[Union[str, Path, PreTrainedTokenizerBase]]], - processor_args: Optional[dict[str, Any]], - data: Union[ - str, - Path, - Iterable[Union[str, dict[str, Any]]], - Dataset, - DatasetDict, - IterableDataset, - IterableDatasetDict, - ], - data_args: Optional[dict[str, Any]], - data_sampler: Optional[Literal["random"]], - rate_type: Union[StrategyType, ProfileType], - rate: Optional[Union[int, float, list[Union[int, float]]]], - max_seconds: Optional[float], - max_requests: Optional[int], - warmup_percent: Optional[float], - cooldown_percent: Optional[float], - show_progress: bool, - show_progress_scheduler_stats: bool, - output_console: bool, - output_path: Optional[Union[str, Path]], - output_extras: Optional[dict[str, Any]], - output_sampling: Optional[int], - random_seed: int, -) -> tuple[GenerativeBenchmarksReport, Optional[Path]]: - console = GenerativeBenchmarksConsole(enabled=show_progress) - console.print_line("Creating backend...") - backend = Backend.create( - backend_type, target=target, model=model, **(backend_args or {}) - ) - await backend.validate() - console.print_line( - f"Backend {backend_type} connected to {target} for model {backend.model}." - ) - - if processor is None: - processor = backend.model - - console.print_line("Creating request loader...") - request_loader = GenerativeRequestLoader( - data=data, - data_args=data_args, - processor=processor, - processor_args=processor_args, - shuffle=data_sampler == "random", - iter_type=( - "finite" # assume a finite dataset is our limit - if max_requests is None and max_seconds is None - else "infinite" # default to infinite so we don't run out of data - ), - random_seed=random_seed, - ) - unique_requests = request_loader.num_unique_items(raise_err=False) - console.print_line( - f"Created loader with {unique_requests} unique requests from {data}.\n\n" - if unique_requests > 0 - else f"Created loader with unknown number unique requests from {data}.\n\n" - ) - - profile = create_profile(rate_type=rate_type, rate=rate) - benchmarker = GenerativeBenchmarker( - backend=backend, - request_loader=request_loader, - request_loader_description=request_loader.description, - benchmark_save_extras=output_extras, - processor=processor, - processor_args=processor_args, - ) - progress = ( - GenerativeTextBenchmarkerProgressDisplay( - display_scheduler_stats=show_progress_scheduler_stats - ) - if show_progress - else None - ) - report = GenerativeBenchmarksReport() - - async for result in benchmarker.run( - profile=profile, - max_number_per_strategy=max_requests, - max_duration_per_strategy=max_seconds, - warmup_percent_per_strategy=warmup_percent, - cooldown_percent_per_strategy=cooldown_percent, - ): - if progress: - progress.update(result) - - if result.type_ == "benchmark_compiled": - if result.current_benchmark is None: - raise ValueError("Current benchmark is None") - report.benchmarks.append( - result.current_benchmark.set_sample_size(output_sampling) - ) - - if output_console: - orig_enabled = console.enabled - console.enabled = True - console.benchmarks = report.benchmarks - console.print_benchmarks_metadata() - console.print_benchmarks_info() - console.print_benchmarks_stats() - console.enabled = orig_enabled - - if output_path: - console.print_line("\nSaving benchmarks report...") - saved_path = report.save_file(output_path) - console.print_line(f"Benchmarks report saved to {saved_path}") - else: - saved_path = None - - console.print_line("\nBenchmarking complete.") - - return report, saved_path diff --git a/src/guidellm/benchmark/output.py b/src/guidellm/benchmark/output.py deleted file mode 100644 index 4847160d..00000000 --- a/src/guidellm/benchmark/output.py +++ /dev/null @@ -1,946 +0,0 @@ -import csv -import json -import math -from collections import OrderedDict -from datetime import datetime -from pathlib import Path -from typing import Any, Literal, Optional, Union - -import yaml -from pydantic import Field -from rich.console import Console -from rich.padding import Padding -from rich.text import Text - -from guidellm.benchmark.benchmark import GenerativeBenchmark, GenerativeMetrics -from guidellm.benchmark.profile import ( - AsyncProfile, - ConcurrentProfile, - SweepProfile, - ThroughputProfile, -) -from guidellm.config import settings -from guidellm.objects import ( - DistributionSummary, - StandardBaseModel, - StatusDistributionSummary, -) -from guidellm.scheduler import strategy_display_str -from guidellm.utils import Colors, split_text_list_by_length - -__all__ = [ - "GenerativeBenchmarksConsole", - "GenerativeBenchmarksReport", -] - - -class GenerativeBenchmarksReport(StandardBaseModel): - """ - A pydantic model representing a completed benchmark report. - Contains a list of benchmarks along with convenience methods for finalizing - and saving the report. - """ - - @staticmethod - def load_file(path: Union[str, Path]) -> "GenerativeBenchmarksReport": - """ - Load a report from a file. The file type is determined by the file extension. - If the file is a directory, it expects a file named benchmarks.json under the - directory. - - :param path: The path to load the report from. - :return: The loaded report. - """ - path, type_ = GenerativeBenchmarksReport._file_setup(path) - - if type_ == "json": - with path.open("r") as file: - model_dict = json.load(file) - - return GenerativeBenchmarksReport.model_validate(model_dict) - - if type_ == "yaml": - with path.open("r") as file: - model_dict = yaml.safe_load(file) - - return GenerativeBenchmarksReport.model_validate(model_dict) - - if type_ == "csv": - raise ValueError(f"CSV file type is not supported for loading: {path}.") - - raise ValueError(f"Unsupported file type: {type_} for {path}.") - - benchmarks: list[GenerativeBenchmark] = Field( - description="The list of completed benchmarks contained within the report.", - default_factory=list, - ) - - def set_sample_size( - self, sample_size: Optional[int] - ) -> "GenerativeBenchmarksReport": - """ - Set the sample size for each benchmark in the report. In doing this, it will - reduce the contained requests of each benchmark to the sample size. - If sample size is None, it will return the report as is. - - :param sample_size: The sample size to set for each benchmark. - If None, the report will be returned as is. - :return: The report with the sample size set for each benchmark. - """ - - if sample_size is not None: - for benchmark in self.benchmarks: - benchmark.set_sample_size(sample_size) - - return self - - def save_file(self, path: Union[str, Path]) -> Path: - """ - Save the report to a file. The file type is determined by the file extension. - If the file is a directory, it will save the report to a file named - benchmarks.json under the directory. - - :param path: The path to save the report to. - :return: The path to the saved report. - """ - path, type_ = GenerativeBenchmarksReport._file_setup(path) - - if type_ == "json": - return self.save_json(path) - - if type_ == "yaml": - return self.save_yaml(path) - - if type_ == "csv": - return self.save_csv(path) - - raise ValueError(f"Unsupported file type: {type_} for {path}.") - - def save_json(self, path: Union[str, Path]) -> Path: - """ - Save the report to a JSON file containing all of the report data which is - reloadable using the pydantic model. If the file is a directory, it will save - the report to a file named benchmarks.json under the directory. - - :param path: The path to save the report to. - :return: The path to the saved report. - """ - path, type_ = GenerativeBenchmarksReport._file_setup(path, "json") - - if type_ != "json": - raise ValueError( - f"Unsupported file type for saving a JSON: {type_} for {path}." - ) - - model_dict = self.model_dump() - model_json = json.dumps(model_dict) - - with path.open("w") as file: - file.write(model_json) - - return path - - def save_yaml(self, path: Union[str, Path]) -> Path: - """ - Save the report to a YAML file containing all of the report data which is - reloadable using the pydantic model. If the file is a directory, it will save - the report to a file named benchmarks.yaml under the directory. - - :param path: The path to save the report to. - :return: The path to the saved report. - """ - - path, type_ = GenerativeBenchmarksReport._file_setup(path, "yaml") - - if type_ != "yaml": - raise ValueError( - f"Unsupported file type for saving a YAML: {type_} for {path}." - ) - - model_dict = self.model_dump() - model_yaml = yaml.dump(model_dict) - - with path.open("w") as file: - file.write(model_yaml) - - return path - - def save_csv(self, path: Union[str, Path]) -> Path: - """ - Save the report to a CSV file containing the summarized statistics and values - for each report. Note, this data is not reloadable using the pydantic model. - If the file is a directory, it will save the report to a file named - benchmarks.csv under the directory. - - :param path: The path to save the report to. - :return: The path to the saved report. - """ - path, type_ = GenerativeBenchmarksReport._file_setup(path, "csv") - - if type_ != "csv": - raise ValueError( - f"Unsupported file type for saving a CSV: {type_} for {path}." - ) - - with path.open("w", newline="") as file: - writer = csv.writer(file) - headers: list[str] = [] - rows: list[list[Union[str, float, list[float]]]] = [] - - for benchmark in self.benchmarks: - benchmark_headers: list[str] = [] - benchmark_values: list[Union[str, float, list[float]]] = [] - - desc_headers, desc_values = self._benchmark_desc_headers_and_values( - benchmark - ) - benchmark_headers += desc_headers - benchmark_values += desc_values - - for status in StatusDistributionSummary.model_fields: - status_headers, status_values = ( - self._benchmark_status_headers_and_values(benchmark, status) - ) - benchmark_headers += status_headers - benchmark_values += status_values - - benchmark_extra_headers, benchmark_extra_values = ( - self._benchmark_extras_headers_and_values(benchmark) - ) - benchmark_headers += benchmark_extra_headers - benchmark_values += benchmark_extra_values - - if not headers: - headers = benchmark_headers - rows.append(benchmark_values) - - writer.writerow(headers) - for row in rows: - writer.writerow(row) - - return path - - @staticmethod - def _file_setup( - path: Union[str, Path], - default_file_type: Literal["json", "yaml", "csv"] = "json", - ) -> tuple[Path, Literal["json", "yaml", "csv"]]: - path = Path(path) if not isinstance(path, Path) else path - - if path.is_dir(): - path = path / f"benchmarks.{default_file_type}" - - path.parent.mkdir(parents=True, exist_ok=True) - path_suffix = path.suffix.lower() - - if path_suffix == ".json": - return path, "json" - - if path_suffix in [".yaml", ".yml"]: - return path, "yaml" - - if path_suffix in [".csv"]: - return path, "csv" - - raise ValueError(f"Unsupported file extension: {path_suffix} for {path}.") - - @staticmethod - def _benchmark_desc_headers_and_values( - benchmark: GenerativeBenchmark, - ) -> tuple[list[str], list[Union[str, float]]]: - headers = [ - "Type", - "Run Id", - "Id", - "Name", - "Start Time", - "End Time", - "Duration", - ] - values: list[Union[str, float]] = [ - benchmark.type_, - benchmark.run_id, - benchmark.id_, - strategy_display_str(benchmark.args.strategy), - datetime.fromtimestamp(benchmark.start_time).strftime("%Y-%m-%d %H:%M:%S"), - datetime.fromtimestamp(benchmark.end_time).strftime("%Y-%m-%d %H:%M:%S"), - benchmark.duration, - ] - - if len(headers) != len(values): - raise ValueError("Headers and values length mismatch.") - - return headers, values - - @staticmethod - def _benchmark_extras_headers_and_values( - benchmark: GenerativeBenchmark, - ) -> tuple[list[str], list[str]]: - headers = ["Args", "Worker", "Request Loader", "Extras"] - values: list[str] = [ - json.dumps(benchmark.args.model_dump()), - json.dumps(benchmark.worker.model_dump()), - json.dumps(benchmark.request_loader.model_dump()), - json.dumps(benchmark.extras), - ] - - if len(headers) != len(values): - raise ValueError("Headers and values length mismatch.") - - return headers, values - - @staticmethod - def _benchmark_status_headers_and_values( - benchmark: GenerativeBenchmark, status: str - ) -> tuple[list[str], list[Union[float, list[float]]]]: - headers = [ - f"{status.capitalize()} Requests", - ] - values = [ - getattr(benchmark.request_totals, status), - ] - - for metric in GenerativeMetrics.model_fields: - metric_headers, metric_values = ( - GenerativeBenchmarksReport._benchmark_status_metrics_stats( - benchmark, status, metric - ) - ) - headers += metric_headers - values += metric_values - - if len(headers) != len(values): - raise ValueError("Headers and values length mismatch.") - - return headers, values - - @staticmethod - def _benchmark_status_metrics_stats( - benchmark: GenerativeBenchmark, - status: str, - metric: str, - ) -> tuple[list[str], list[Union[float, list[float]]]]: - status_display = status.capitalize() - metric_display = metric.replace("_", " ").capitalize() - status_dist_summary: StatusDistributionSummary = getattr( - benchmark.metrics, metric - ) - dist_summary: DistributionSummary = getattr(status_dist_summary, status) - headers = [ - f"{status_display} {metric_display} mean", - f"{status_display} {metric_display} median", - f"{status_display} {metric_display} std dev", - ( - f"{status_display} {metric_display} " - "[min, 0.1, 1, 5, 10, 25, 75, 90, 95, 99, max]" - ), - ] - values: list[Union[float, list[float]]] = [ - dist_summary.mean, - dist_summary.median, - dist_summary.std_dev, - [ - dist_summary.min, - dist_summary.percentiles.p001, - dist_summary.percentiles.p01, - dist_summary.percentiles.p05, - dist_summary.percentiles.p10, - dist_summary.percentiles.p25, - dist_summary.percentiles.p75, - dist_summary.percentiles.p90, - dist_summary.percentiles.p95, - dist_summary.percentiles.p99, - dist_summary.max, - ], - ] - - if len(headers) != len(values): - raise ValueError("Headers and values length mismatch.") - - return headers, values - - -class GenerativeBenchmarksConsole: - """ - A class for outputting progress and benchmark results to the console. - Utilizes the rich library for formatting, enabling colored and styled output. - """ - - def __init__(self, enabled: bool = True): - """ - :param enabled: Whether to enable console output. Defaults to True. - If False, all console output will be suppressed. - """ - self.enabled = enabled - self.benchmarks: Optional[list[GenerativeBenchmark]] = None - self.console = Console() - - @property - def benchmarks_profile_str(self) -> str: - """ - :return: A string representation of the profile used for the benchmarks. - """ - profile = self.benchmarks[0].args.profile if self.benchmarks else None - - if profile is None: - return "None" - - profile_args = OrderedDict( - { - "type": profile.type_, - "strategies": profile.strategy_types, - } - ) - - if isinstance(profile, ConcurrentProfile): - profile_args["streams"] = str(profile.streams) - elif isinstance(profile, ThroughputProfile): - profile_args["max_concurrency"] = str(profile.max_concurrency) - elif isinstance(profile, AsyncProfile): - profile_args["max_concurrency"] = str(profile.max_concurrency) - profile_args["rate"] = str(profile.rate) - profile_args["initial_burst"] = str(profile.initial_burst) - elif isinstance(profile, SweepProfile): - profile_args["sweep_size"] = str(profile.sweep_size) - - return ", ".join(f"{key}={value}" for key, value in profile_args.items()) - - @property - def benchmarks_args_str(self) -> str: - """ - :return: A string representation of the arguments used for the benchmarks. - """ - args = self.benchmarks[0].args if self.benchmarks else None - - if args is None: - return "None" - - args_dict = OrderedDict( - { - "max_number": args.max_number, - "max_duration": args.max_duration, - "warmup_number": args.warmup_number, - "warmup_duration": args.warmup_duration, - "cooldown_number": args.cooldown_number, - "cooldown_duration": args.cooldown_duration, - } - ) - - return ", ".join(f"{key}={value}" for key, value in args_dict.items()) - - @property - def benchmarks_worker_desc_str(self) -> str: - """ - :return: A string representation of the worker used for the benchmarks. - """ - return str(self.benchmarks[0].worker) if self.benchmarks else "None" - - @property - def benchmarks_request_loader_desc_str(self) -> str: - """ - :return: A string representation of the request loader used for the benchmarks. - """ - return str(self.benchmarks[0].request_loader) if self.benchmarks else "None" - - @property - def benchmarks_extras_str(self) -> str: - """ - :return: A string representation of the extras used for the benchmarks. - """ - extras = self.benchmarks[0].extras if self.benchmarks else None - - if not extras: - return "None" - - return ", ".join(f"{key}={value}" for key, value in extras.items()) - - def print_section_header(self, title: str, indent: int = 0, new_lines: int = 2): - """ - Print out a styled section header to the console. - The title is underlined, bolded, and colored with the INFO color. - - :param title: The title of the section. - :param indent: The number of spaces to indent the title. - Defaults to 0. - :param new_lines: The number of new lines to print before the title. - Defaults to 2. - """ - self.print_line( - value=f"{title}:", - style=f"bold underline {Colors.INFO}", - indent=indent, - new_lines=new_lines, - ) - - def print_labeled_line( - self, label: str, value: str, indent: int = 4, new_lines: int = 0 - ): - """ - Print out a styled, labeled line (label: value) to the console. - The label is bolded and colored with the INFO color, - and the value is italicized. - - :param label: The label of the line. - :param value: The value of the line. - :param indent: The number of spaces to indent the line. - Defaults to 4. - :param new_lines: The number of new lines to print before the line. - Defaults to 0. - """ - self.print_line( - value=[label + ":", value], - style=["bold " + Colors.INFO, "italic"], - new_lines=new_lines, - indent=indent, - ) - - def print_line( - self, - value: Union[str, list[str]], - style: Union[str, list[str]] = "", - indent: int = 0, - new_lines: int = 0, - ): - """ - Print out a a value to the console as a line with optional indentation. - - :param value: The value to print. - :param style: The style to apply to the value. - Defaults to none. - :param indent: The number of spaces to indent the line. - Defaults to 0. - :param new_lines: The number of new lines to print before the value. - Defaults to 0. - """ - if not self.enabled: - return - - text = Text() - - for _ in range(new_lines): - text.append("\n") - - if not isinstance(value, list): - value = [value] - - if not isinstance(style, list): - style = [style for _ in range(len(value))] - - if len(value) != len(style): - raise ValueError( - f"Value and style length mismatch. Value length: {len(value)}, " - f"Style length: {len(style)}." - ) - - for val, sty in zip(value, style): - text.append(val, style=sty) - - self.console.print(Padding.indent(text, indent)) - - def print_table( - self, - headers: list[str], - rows: list[list[Any]], - title: str, - sections: Optional[dict[str, tuple[int, int]]] = None, - max_char_per_col: int = 2**10, - indent: int = 0, - new_lines: int = 2, - ): - """ - Print a table to the console with the given headers and rows. - - :param headers: The headers of the table. - :param rows: The rows of the table. - :param title: The title of the table. - :param sections: The sections of the table grouping columns together. - This is a mapping of the section display name to a tuple of the start and - end column indices. If None, no sections are added (default). - :param max_char_per_col: The maximum number of characters per column. - :param indent: The number of spaces to indent the table. - Defaults to 0. - :param new_lines: The number of new lines to print before the table. - Defaults to 0. - """ - - if rows and any(len(row) != len(headers) for row in rows): - raise ValueError( - f"Headers and rows length mismatch. Headers length: {len(headers)}, " - f"Row length: {len(rows[0]) if rows else 'N/A'}." - ) - - max_characters_per_column = self.calculate_max_chars_per_column( - headers, rows, sections, max_char_per_col - ) - - self.print_section_header(title, indent=indent, new_lines=new_lines) - self.print_table_divider( - max_characters_per_column, include_separators=False, indent=indent - ) - if sections: - self.print_table_sections( - sections, max_characters_per_column, indent=indent - ) - self.print_table_row( - split_text_list_by_length(headers, max_characters_per_column), - style=f"bold {Colors.INFO}", - indent=indent, - ) - self.print_table_divider( - max_characters_per_column, include_separators=True, indent=indent - ) - for row in rows: - self.print_table_row( - split_text_list_by_length(row, max_characters_per_column), - style="italic", - indent=indent, - ) - self.print_table_divider( - max_characters_per_column, include_separators=False, indent=indent - ) - - def calculate_max_chars_per_column( - self, - headers: list[str], - rows: list[list[Any]], - sections: Optional[dict[str, tuple[int, int]]], - max_char_per_col: int, - ) -> list[int]: - """ - Calculate the maximum number of characters per column in the table. - This is done by checking the length of the headers, rows, and optional sections - to ensure all columns are accounted for and spaced correctly. - - :param headers: The headers of the table. - :param rows: The rows of the table. - :param sections: The sections of the table grouping columns together. - This is a mapping of the section display name to a tuple of the start and - end column indices. If None, no sections are added (default). - :param max_char_per_col: The maximum number of characters per column. - :return: A list of the maximum number of characters per column. - """ - max_characters_per_column = [] - for ind in range(len(headers)): - max_characters_per_column.append(min(len(headers[ind]), max_char_per_col)) - - for row in rows: - max_characters_per_column[ind] = max( - max_characters_per_column[ind], len(str(row[ind])) - ) - - if not sections: - return max_characters_per_column - - for section in sections: - start_col, end_col = sections[section] - min_section_len = len(section) + ( - end_col - start_col - ) # ensure we have enough space for separators - chars_in_columns = sum( - max_characters_per_column[start_col : end_col + 1] - ) + 2 * (end_col - start_col) - if min_section_len > chars_in_columns: - add_chars_per_col = math.ceil( - (min_section_len - chars_in_columns) / (end_col - start_col + 1) - ) - for col in range(start_col, end_col + 1): - max_characters_per_column[col] += add_chars_per_col - - return max_characters_per_column - - def print_table_divider( - self, max_chars_per_column: list[int], include_separators: bool, indent: int = 0 - ): - """ - Print a divider line for the table (top and bottom of table with '=' characters) - - :param max_chars_per_column: The maximum number of characters per column. - :param include_separators: Whether to include separators between columns. - :param indent: The number of spaces to indent the line. - Defaults to 0. - """ - if include_separators: - columns = [ - settings.table_headers_border_char * max_chars - + settings.table_column_separator_char - + settings.table_headers_border_char - for max_chars in max_chars_per_column - ] - else: - columns = [ - settings.table_border_char * (max_chars + 2) - for max_chars in max_chars_per_column - ] - - columns[-1] = columns[-1][:-2] - self.print_line(value=columns, style=Colors.INFO, indent=indent) - - def print_table_sections( - self, - sections: dict[str, tuple[int, int]], - max_chars_per_column: list[int], - indent: int = 0, - ): - """ - Print the sections of the table with corresponding separators to the columns - the sections are mapped to to ensure it is compliant with a CSV format. - For example, a section named "Metadata" with columns 0-3 will print this: - Metadata ,,,, - Where the spaces plus the separators at the end will span the columns 0-3. - All columns must be accounted for in the sections. - - :param sections: The sections of the table. - :param max_chars_per_column: The maximum number of characters per column. - :param indent: The number of spaces to indent the line. - Defaults to 0. - """ - section_tuples = [(start, end, name) for name, (start, end) in sections.items()] - section_tuples.sort(key=lambda x: x[0]) - - if any(start > end for start, end, _ in section_tuples): - raise ValueError(f"Invalid section ranges: {section_tuples}") - - if ( - any( - section_tuples[ind][1] + 1 != section_tuples[ind + 1][0] - for ind in range(len(section_tuples) - 1) - ) - or section_tuples[0][0] != 0 - or section_tuples[-1][1] != len(max_chars_per_column) - 1 - ): - raise ValueError(f"Invalid section ranges: {section_tuples}") - - line_values = [] - line_styles = [] - for section, (start_col, end_col) in sections.items(): - section_length = sum(max_chars_per_column[start_col : end_col + 1]) + 2 * ( - end_col - start_col + 1 - ) - num_separators = end_col - start_col - line_values.append(section) - line_styles.append("bold " + Colors.INFO) - line_values.append( - " " * (section_length - len(section) - num_separators - 2) - ) - line_styles.append("") - line_values.append(settings.table_column_separator_char * num_separators) - line_styles.append("") - line_values.append(settings.table_column_separator_char + " ") - line_styles.append(Colors.INFO) - line_values = line_values[:-1] - line_styles = line_styles[:-1] - self.print_line(value=line_values, style=line_styles, indent=indent) - - def print_table_row( - self, column_lines: list[list[str]], style: str, indent: int = 0 - ): - """ - Print a single row of a table to the console. - - :param column_lines: The lines of text to print for each column. - :param indent: The number of spaces to indent the line. - Defaults to 0. - """ - for row in range(len(column_lines[0])): - print_line = [] - print_styles = [] - for column in range(len(column_lines)): - print_line.extend( - [ - column_lines[column][row], - settings.table_column_separator_char, - " ", - ] - ) - print_styles.extend([style, Colors.INFO, ""]) - print_line = print_line[:-2] - print_styles = print_styles[:-2] - self.print_line(value=print_line, style=print_styles, indent=indent) - - def print_benchmarks_metadata(self): - """ - Print out the metadata of the benchmarks to the console including the run id, - duration, profile, args, worker, request loader, and extras. - """ - - if not self.benchmarks: - raise ValueError( - "No benchmarks to print metadata for. Please set benchmarks first." - ) - - start_time = self.benchmarks[0].run_stats.start_time - end_time = self.benchmarks[-1].run_stats.end_time - duration = end_time - start_time - - self.print_section_header(title="Benchmarks Metadata") - self.print_labeled_line( - label="Run id", - value=str(self.benchmarks[0].run_id), - ) - self.print_labeled_line( - label="Duration", - value=f"{duration:.1f} seconds", - ) - self.print_labeled_line( - label="Profile", - value=self.benchmarks_profile_str, - ) - self.print_labeled_line( - label="Args", - value=self.benchmarks_args_str, - ) - self.print_labeled_line( - label="Worker", - value=self.benchmarks_worker_desc_str, - ) - self.print_labeled_line( - label="Request Loader", - value=self.benchmarks_request_loader_desc_str, - ) - self.print_labeled_line( - label="Extras", - value=self.benchmarks_extras_str, - ) - - def print_benchmarks_info(self): - """ - Print out the benchmark information to the console including the start time, - end time, duration, request totals, and token totals for each benchmark. - """ - if not self.benchmarks: - raise ValueError( - "No benchmarks to print info for. Please set benchmarks first." - ) - - sections = { - "Metadata": (0, 3), - "Requests Made": (4, 6), - "Prompt Tok/Req": (7, 9), - "Output Tok/Req": (10, 12), - "Prompt Tok Total": (13, 15), - "Output Tok Total": (16, 18), - } - headers = [ - "Benchmark", - "Start Time", - "End Time", - "Duration (s)", - "Comp", - "Inc", - "Err", - "Comp", - "Inc", - "Err", - "Comp", - "Inc", - "Err", - "Comp", - "Inc", - "Err", - "Comp", - "Inc", - "Err", - ] - rows = [] - - for benchmark in self.benchmarks: - rows.append( - [ - strategy_display_str(benchmark.args.strategy), - f"{datetime.fromtimestamp(benchmark.start_time).strftime('%H:%M:%S')}", - f"{datetime.fromtimestamp(benchmark.end_time).strftime('%H:%M:%S')}", - f"{(benchmark.end_time - benchmark.start_time):.1f}", - f"{benchmark.request_totals.successful:.0f}", - f"{benchmark.request_totals.incomplete:.0f}", - f"{benchmark.request_totals.errored:.0f}", - f"{benchmark.metrics.prompt_token_count.successful.mean:.1f}", - f"{benchmark.metrics.prompt_token_count.incomplete.mean:.1f}", - f"{benchmark.metrics.prompt_token_count.errored.mean:.1f}", - f"{benchmark.metrics.output_token_count.successful.mean:.1f}", - f"{benchmark.metrics.output_token_count.incomplete.mean:.1f}", - f"{benchmark.metrics.output_token_count.errored.mean:.1f}", - f"{benchmark.metrics.prompt_token_count.successful.total_sum:.0f}", - f"{benchmark.metrics.prompt_token_count.incomplete.total_sum:.0f}", - f"{benchmark.metrics.prompt_token_count.errored.total_sum:.0f}", - f"{benchmark.metrics.output_token_count.successful.total_sum:.0f}", - f"{benchmark.metrics.output_token_count.incomplete.total_sum:.0f}", - f"{benchmark.metrics.output_token_count.errored.total_sum:.0f}", - ] - ) - - self.print_table( - headers=headers, rows=rows, title="Benchmarks Info", sections=sections - ) - - def print_benchmarks_stats(self): - """ - Print out the benchmark statistics to the console including the requests per - second, request concurrency, output tokens per second, total tokens per second, - request latency, time to first token, inter token latency, and time per output - token for each benchmark. - """ - if not self.benchmarks: - raise ValueError( - "No benchmarks to print stats for. Please set benchmarks first." - ) - - sections = { - "Metadata": (0, 0), - "Request Stats": (1, 2), - "Out Tok/sec": (3, 3), - "Tot Tok/sec": (4, 4), - "Req Latency (sec)": (5, 7), - "TTFT (ms)": (8, 10), - "ITL (ms)": (11, 13), - "TPOT (ms)": (14, 16), - } - headers = [ - "Benchmark", - "Per Second", - "Concurrency", - "mean", - "mean", - "mean", - "median", - "p99", - "mean", - "median", - "p99", - "mean", - "median", - "p99", - "mean", - "median", - "p99", - ] - rows = [] - - for benchmark in self.benchmarks: - rows.append( - [ - strategy_display_str(benchmark.args.strategy), - f"{benchmark.metrics.requests_per_second.successful.mean:.2f}", - f"{benchmark.metrics.request_concurrency.successful.mean:.2f}", - f"{benchmark.metrics.output_tokens_per_second.successful.mean:.1f}", - f"{benchmark.metrics.tokens_per_second.successful.mean:.1f}", - f"{benchmark.metrics.request_latency.successful.mean:.2f}", - f"{benchmark.metrics.request_latency.successful.median:.2f}", - f"{benchmark.metrics.request_latency.successful.percentiles.p99:.2f}", - f"{benchmark.metrics.time_to_first_token_ms.successful.mean:.1f}", - f"{benchmark.metrics.time_to_first_token_ms.successful.median:.1f}", - f"{benchmark.metrics.time_to_first_token_ms.successful.percentiles.p99:.1f}", - f"{benchmark.metrics.inter_token_latency_ms.successful.mean:.1f}", - f"{benchmark.metrics.inter_token_latency_ms.successful.median:.1f}", - f"{benchmark.metrics.inter_token_latency_ms.successful.percentiles.p99:.1f}", - f"{benchmark.metrics.time_per_output_token_ms.successful.mean:.1f}", - f"{benchmark.metrics.time_per_output_token_ms.successful.median:.1f}", - f"{benchmark.metrics.time_per_output_token_ms.successful.percentiles.p99:.1f}", - ] - ) - - self.print_table( - headers=headers, - rows=rows, - title="Benchmarks Stats", - sections=sections, - ) diff --git a/src/guidellm/benchmark/profile.py b/src/guidellm/benchmark/profile.py deleted file mode 100644 index 642cb7a8..00000000 --- a/src/guidellm/benchmark/profile.py +++ /dev/null @@ -1,409 +0,0 @@ -from collections.abc import Sequence -from typing import Literal, Optional, Union - -import numpy as np -from pydantic import Field, computed_field - -from guidellm.config import settings -from guidellm.objects import StandardBaseModel -from guidellm.scheduler import ( - AsyncConstantStrategy, - AsyncPoissonStrategy, - ConcurrentStrategy, - SchedulingStrategy, - StrategyType, - SynchronousStrategy, - ThroughputStrategy, -) - -__all__ = [ - "AsyncProfile", - "ConcurrentProfile", - "Profile", - "ProfileType", - "SweepProfile", - "SynchronousProfile", - "ThroughputProfile", - "create_profile", -] - -ProfileType = Literal["synchronous", "concurrent", "throughput", "async", "sweep"] - - -class Profile(StandardBaseModel): - type_: Literal["profile"] = Field( - description="The type of benchmarking profile to use.", - ) - completed_strategies: int = Field( - default=0, - description="The number of scheduling strategies generated so far.", - ) - measured_rates: list[float] = Field( - default_factory=list, - description=("The average rates measured for the strategies that have run."), - ) - measured_concurrencies: list[float] = Field( - default_factory=list, - description=( - "The average concurrency measured for the strategies that have run." - ), - ) - - def completed_strategy(self, average_rate: float, average_concurrency: float): - self.measured_rates.append(average_rate) - self.measured_concurrencies.append(average_concurrency) - self.completed_strategies += 1 - - @computed_field # type: ignore[misc] - @property - def strategy_types(self) -> list[StrategyType]: - return [] - - def next_strategy(self) -> Optional[SchedulingStrategy]: - return None - - -class SynchronousProfile(Profile): - type_: Literal["synchronous"] = "synchronous" # type: ignore[assignment] - - @property - def strategy_types(self) -> list[StrategyType]: - return [self.type_] - - def next_strategy(self) -> Optional[SchedulingStrategy]: - if self.completed_strategies >= 1: - return None - - return SynchronousStrategy() - - @staticmethod - def from_standard_args( - rate_type: Union[StrategyType, ProfileType], - rate: Optional[Union[float, Sequence[float]]], - **kwargs, - ) -> "SynchronousProfile": - if rate_type != "synchronous": - raise ValueError("Rate type must be 'synchronous' for synchronous profile.") - - if rate is not None: - raise ValueError( - "Rate does not apply to synchronous profile, it must be set to None." - ) - - if kwargs: - raise ValueError( - "No additional arguments are allowed for synchronous profile." - ) - - return SynchronousProfile() - - -class ConcurrentProfile(Profile): - type_: Literal["concurrent"] = "concurrent" # type: ignore[assignment] - streams: Union[int, Sequence[int]] = Field( - description="The number of concurrent streams to use.", - ) - - @property - def strategy_types(self) -> list[StrategyType]: - num_strategies = len(self.streams) if isinstance(self.streams, Sequence) else 1 - - return [self.type_] * num_strategies - - def next_strategy(self) -> Optional[SchedulingStrategy]: - streams = self.streams if isinstance(self.streams, Sequence) else [self.streams] - - if self.completed_strategies >= len(streams): - return None - - return ConcurrentStrategy( - streams=streams[self.completed_strategies], - ) - - @staticmethod - def from_standard_args( - rate_type: Union[StrategyType, ProfileType], - rate: Optional[Union[float, Sequence[float]]], - **kwargs, - ) -> "ConcurrentProfile": - if rate_type != "concurrent": - raise ValueError("Rate type must be 'concurrent' for concurrent profile.") - - if not rate: - raise ValueError("Rate (streams) must be provided for concurrent profile.") - - if not isinstance(rate, Sequence): - rate = [rate] - - if not all(stream.is_integer() and stream > 0 for stream in rate): - raise ValueError( - f"All rate values (streams) must be positive integers, received {rate}" - ) - - if kwargs: - raise ValueError( - "No additional arguments are allowed for concurrent profile." - ) - - return ConcurrentProfile(streams=[int(rat) for rat in rate]) - - -class ThroughputProfile(Profile): - type_: Literal["throughput"] = "throughput" # type: ignore[assignment] - max_concurrency: Optional[int] = Field( - default=None, - description="The maximum number of concurrent requests that can be scheduled.", - ) - - @property - def strategy_types(self) -> list[StrategyType]: - return [self.type_] - - def next_strategy(self) -> Optional[SchedulingStrategy]: - if self.completed_strategies >= 1: - return None - - return ThroughputStrategy( - max_concurrency=self.max_concurrency, - ) - - @staticmethod - def from_standard_args( - rate_type: Union[StrategyType, ProfileType], - rate: Optional[Union[float, Sequence[float]]], - **kwargs, - ) -> "ThroughputProfile": - if rate_type != "throughput": - raise ValueError("Rate type must be 'throughput' for throughput profile.") - - if rate is not None: - raise ValueError( - "Rate does not apply to throughput profile, it must be set to None." - ) - - return ThroughputProfile(**kwargs) - - -class AsyncProfile(ThroughputProfile): - type_: Literal["async"] = "async" # type: ignore[assignment] - strategy_type: Literal["constant", "poisson"] = Field( - description="The type of asynchronous strategy to use.", - ) - rate: Union[float, Sequence[float]] = Field( - description="The rate of requests per second to use.", - ) - initial_burst: bool = Field( - default=True, - description=( - "True to send an initial burst of requests (math.floor(self.rate)) " - "to reach target rate. False to not send an initial burst." - ), - ) - random_seed: int = Field( - default=42, - description=( - "The random seed to use for the asynchronous strategy. " - "This is used to generate random numbers for the Poisson strategy." - ), - ) - - @property - def strategy_types(self) -> list[StrategyType]: - num_strategies = len(self.rate) if isinstance(self.rate, Sequence) else 1 - - return [self.strategy_type] * num_strategies - - def next_strategy(self) -> Optional[SchedulingStrategy]: - rate = self.rate if isinstance(self.rate, Sequence) else [self.rate] - - if self.completed_strategies >= len(rate): - return None - - if self.strategy_type == "constant": - return AsyncConstantStrategy( - rate=rate[self.completed_strategies], - initial_burst=self.initial_burst, - max_concurrency=self.max_concurrency, - ) - elif self.strategy_type == "poisson": - return AsyncPoissonStrategy( - rate=rate[self.completed_strategies], - initial_burst=self.initial_burst, - max_concurrency=self.max_concurrency, - random_seed=self.random_seed, - ) - else: - raise ValueError(f"Invalid strategy type: {self.strategy_type}") - - @staticmethod - def from_standard_args( # type: ignore[override] - rate_type: Union[StrategyType, ProfileType], - rate: Optional[Union[float, Sequence[float]]], - random_seed: int, - **kwargs, - ) -> "AsyncProfile": - if rate_type not in ("async", "constant", "poisson"): - raise ValueError( - "Rate type must be in ('async', 'constant', 'poisson') " - f"for async profile. Received: {rate_type}" - ) - - if not rate: - raise ValueError("Rate must be provided for async profile.") - - if not isinstance(rate, Sequence): - rate = [rate] - - if not all(isinstance(r, (float, int)) and r > 0 for r in rate): - raise ValueError( - f"All rate values must be positive numbers, received {rate}" - ) - - if rate_type == "async": - rate_type = "constant" # default to constant if not specified - - return AsyncProfile( - strategy_type=rate_type, # type: ignore[arg-type] - rate=rate, - random_seed=random_seed, - **kwargs, - ) - - -class SweepProfile(AsyncProfile): - type_: Literal["sweep"] = "sweep" # type: ignore[assignment] - sweep_size: int = Field( - description="The number of strategies to generate for the sweep.", - ) - rate: float = -1 - rate_type: Literal["constant", "poisson"] = "constant" - - @property - def strategy_types(self) -> list[StrategyType]: - return ( - ["synchronous"] + ["throughput"] + [self.rate_type] * (self.sweep_size - 2) # type: ignore[return-value] - ) - - def next_strategy(self) -> Optional[SchedulingStrategy]: - if self.completed_strategies >= self.sweep_size: - return None - - if self.completed_strategies == 0: - return SynchronousStrategy() - - if self.completed_strategies == 1: - return ThroughputStrategy( - max_concurrency=self.max_concurrency, - ) - - min_rate = self.measured_rates[0] - max_rate = self.measured_rates[1] - rates = np.linspace(min_rate, max_rate, self.sweep_size - 1)[1:] - - if self.rate_type == "constant": - return AsyncConstantStrategy( - rate=rates[self.completed_strategies - 2], - initial_burst=self.initial_burst, - max_concurrency=self.max_concurrency, - ) - elif self.rate_type == "poisson": - return AsyncPoissonStrategy( - rate=rates[self.completed_strategies - 2], - initial_burst=self.initial_burst, - max_concurrency=self.max_concurrency, - ) - else: - raise ValueError(f"Invalid strategy type: {self.rate_type}") - - @staticmethod - def from_standard_args( # type: ignore[override] - rate_type: Union[StrategyType, ProfileType], - rate: Optional[Union[float, Sequence[float]]], - random_seed: int, - **kwargs, - ) -> "SweepProfile": - if rate_type != "sweep": - raise ValueError("Rate type must be 'sweep' for sweep profile.") - - if "sweep_size" in kwargs: - raise ValueError("Sweep size must not be provided, use rate instead.") - - if isinstance(rate, Sequence): - if len(rate) != 1: - raise ValueError( - "Rate must be a single value for sweep profile, received " - f"{len(rate)} values." - ) - rate = rate[0] - - if not rate: - rate = settings.default_sweep_number - - if not rate: - raise ValueError( - "Rate (sweep_size) must be provided for concurrent profile." - ) - - if ( - not isinstance(rate, (int, float)) - or (isinstance(rate, float) and not rate.is_integer()) - or rate <= 1 - ): - raise ValueError( - f"Rate (sweep_size) must be a positive integer > 1, received {rate} " - f"with type {type(rate)}" - ) - - if not kwargs: - kwargs = {} - - if "strategy_type" not in kwargs: - kwargs["strategy_type"] = "constant" - - return SweepProfile(sweep_size=int(rate), random_seed=random_seed, **kwargs) - - -def create_profile( - rate_type: Union[StrategyType, ProfileType], - rate: Optional[Union[float, Sequence[float]]], - random_seed: int = 42, - **kwargs, -) -> "Profile": - if rate_type == "synchronous": - return SynchronousProfile.from_standard_args( - rate_type=rate_type, - rate=rate, - **kwargs, - ) - - if rate_type == "concurrent": - return ConcurrentProfile.from_standard_args( - rate_type=rate_type, - rate=rate, - **kwargs, - ) - - if rate_type == "throughput": - return ThroughputProfile.from_standard_args( - rate_type=rate_type, - rate=rate, - **kwargs, - ) - - if rate_type in ("async", "constant", "poisson"): - return AsyncProfile.from_standard_args( - rate_type=rate_type, - rate=rate, - random_seed=random_seed, - **kwargs, - ) - - if rate_type == "sweep": - return SweepProfile.from_standard_args( - rate_type=rate_type, - rate=rate, - random_seed=random_seed, - **kwargs, - ) - - raise ValueError(f"Invalid profile type: {rate_type}") diff --git a/src/guidellm/benchmark/progress.py b/src/guidellm/benchmark/progress.py deleted file mode 100644 index d6f437e1..00000000 --- a/src/guidellm/benchmark/progress.py +++ /dev/null @@ -1,720 +0,0 @@ -import math -import time -from dataclasses import dataclass -from datetime import datetime -from typing import Generic, Optional, TypeVar, Union - -from rich.console import Group -from rich.live import Live -from rich.panel import Panel -from rich.progress import ( - BarColumn, - Progress, - ProgressColumn, - SpinnerColumn, - TaskID, - TaskProgressColumn, - TextColumn, - TimeElapsedColumn, - TimeRemainingColumn, -) - -from guidellm.benchmark.aggregator import ( - BenchmarkAggregator, - GenerativeBenchmarkAggregator, -) -from guidellm.benchmark.benchmark import Benchmark, GenerativeBenchmark -from guidellm.benchmark.benchmarker import BenchmarkerResult -from guidellm.scheduler import ( - SchedulingStrategy, - StrategyType, - strategy_display_str, -) -from guidellm.utils import Colors - -__all__ = [ - "BenchmarkerProgressDisplay", - "BenchmarkerTaskProgressState", - "GenerativeTextBenchmarkerProgressDisplay", - "GenerativeTextBenchmarkerTaskProgressState", -] - - -@dataclass -class BenchmarkerTaskProgressState: - display_scheduler_stats: bool - - task_id: TaskID - strategy: Union[StrategyType, SchedulingStrategy] - started: bool = False - compiling: bool = False - ended: bool = False - - start_time: Optional[float] = None - max_number: Optional[float] = None - max_duration: Optional[float] = None - in_warmup: bool = False - in_cooldown: bool = False - - requests_rate: float = 0 - request_latency: float = 0 - requests_processing: float = 0 - requests_successful: float = 0 - requests_incomplete: float = 0 - requests_errored: float = 0 - - worker_overheads_time_ms: float = 0.0 - backend_overheads_time_ms: float = 0.0 - requests_sleep_time_ms: float = 0.0 - requests_targeted_start_time_delay_ms: float = 0.0 - - @property - def description(self) -> str: - return strategy_display_str(self.strategy) - - @property - def total(self) -> Optional[float]: - if self.max_number is None and self.max_duration is None: - return None - - return 1000 - - @property - def completed(self) -> int: - if self.ended: - return 1000 - - if self.max_number is None and self.max_duration is None: - return 0 - - number = self.requests_successful + self.requests_errored - number_percent = ( - number / float(self.max_number) * 1000 if self.max_number else -math.inf - ) - duration_percent = ( - (time.time() - self.start_time) / self.max_duration * 1000 - if self.max_duration and self.start_time - else -math.inf - ) - - return min(int(max(number_percent, duration_percent)), 1000) - - @property - def fields(self) -> dict[str, str]: - fields = { - "start_time": self.formatted_start_time, - "progress_status": self.formatted_progress_status, - "requests_summary": self.formatted_requests_summary, - } - - if self.display_scheduler_stats: - fields["scheduler_stats"] = self.formatted_scheduler_stats - - return fields - - @property - def formatted_start_time(self) -> str: - if self.start_time is None: - return "--:--:--" - - return datetime.fromtimestamp(self.start_time).strftime("%H:%M:%S") - - @property - def formatted_progress_status(self) -> str: - if self.ended: - status = "complete" - color = Colors.SUCCESS - elif self.compiling: - status = "compiling" - color = Colors.PROGRESS - elif self.started and self.in_warmup: - status = "warmup" - color = Colors.PROGRESS - elif self.started and self.in_cooldown: - status = "cooldown" - color = Colors.PROGRESS - elif self.started: - status = "running" - color = Colors.PROGRESS - else: - status = "pending" - color = Colors.INFO - - return f"[{color}]{status.ljust(8)}[/{color}]" - - @property - def formatted_requests_summary(self) -> str: - if not self.started: - return " " - - return ( - f"[{Colors.INFO}]Req:[/{Colors.INFO}] " - + BenchmarkerTaskProgressState.format_progress_display( - value=self.requests_rate, - label="req/s", - total_characters=12, - digits_places=4, - decimal_places=1, - ) - + ", " - + BenchmarkerTaskProgressState.format_progress_display( - value=self.request_latency, - label="Lat", - units="s", - total_characters=12, - digits_places=4, - decimal_places=2, - ) - + ", " - + BenchmarkerTaskProgressState.format_progress_display( - value=self.requests_processing, - label="Conc", - total_characters=12, - digits_places=4, - decimal_places=1, - ) - + ", " - + BenchmarkerTaskProgressState.format_progress_display( - value=self.requests_successful, - label="Comp", - total_characters=12, - digits_places=5, - decimal_places=0, - ) - + ", " - + BenchmarkerTaskProgressState.format_progress_display( - value=self.requests_incomplete, - label="Inc", - total_characters=12, - digits_places=5, - decimal_places=0, - ) - + ", " - + BenchmarkerTaskProgressState.format_progress_display( - value=self.requests_errored, - label="Err", - total_characters=12, - digits_places=5, - decimal_places=0, - ) - ) - - @property - def formatted_scheduler_stats(self) -> str: - if not self.started: - return " " - - return ( - f"[{Colors.INFO}]Sys:[/{Colors.INFO}] " - + BenchmarkerTaskProgressState.format_progress_display( - value=self.worker_overheads_time_ms, - label="Work OH", - units="ms", - total_characters=18, - digits_places=3, - decimal_places=1, - ) - + ", " - + BenchmarkerTaskProgressState.format_progress_display( - value=self.backend_overheads_time_ms, - label="Back OH", - units="ms", - total_characters=18, - digits_places=3, - decimal_places=1, - ) - + ", " - + BenchmarkerTaskProgressState.format_progress_display( - value=self.requests_sleep_time_ms, - label="Req Sleep", - units="ms", - total_characters=18, - digits_places=5, - decimal_places=0, - ) - + ", " - + BenchmarkerTaskProgressState.format_progress_display( - value=self.requests_targeted_start_time_delay_ms, - label="Start Del", - units="ms", - total_characters=18, - digits_places=5, - decimal_places=0, - ) - ) - - @staticmethod - def format_progress_display( - value: float, - label: str, - units: str = "", - total_characters: Optional[int] = None, - digits_places: Optional[int] = None, - decimal_places: Optional[int] = None, - ) -> str: - if decimal_places is None and digits_places is None: - formatted_number = f"{value}:.0f" - elif digits_places is None: - formatted_number = f"{value:.{decimal_places}f}" - elif decimal_places is None: - formatted_number = f"{value:>{digits_places}f}" - else: - formatted_number = f"{value:>{digits_places}.{decimal_places}f}" - - result = f"{formatted_number}{units} [{Colors.INFO}]{label}[/{Colors.INFO}]" - - if total_characters is not None: - total_characters += len(Colors.INFO) * 2 + 5 - - if len(result) < total_characters: - result = result.rjust(total_characters) - - return result - - -class GenerativeTextBenchmarkerTaskProgressState(BenchmarkerTaskProgressState): - output_tokens: float = 0 - prompt_tokens: float = 0 - output_tokens_rate: float = 0 - total_tokens_rate: float = 0 - tokens_ttft: float = 0 - tokens_itl: float = 0 - - @property - def fields(self) -> dict[str, str]: - fields = super().fields - fields["tokens_summary"] = self.formatted_tokens_summary - return fields - - @property - def formatted_tokens_summary(self) -> str: - if not self.started: - return " " - - return ( - f"[{Colors.INFO}]Tok:[/{Colors.INFO}] " - + BenchmarkerTaskProgressState.format_progress_display( - value=self.output_tokens_rate, - label="gen/s", - total_characters=12, - digits_places=4, - decimal_places=1, - ) - + ", " - + BenchmarkerTaskProgressState.format_progress_display( - value=self.total_tokens_rate, - label="tot/s", - total_characters=12, - digits_places=4, - decimal_places=1, - ) - + ", " - + BenchmarkerTaskProgressState.format_progress_display( - value=self.tokens_ttft, - label="TTFT", - units="ms", - total_characters=12, - digits_places=3, - decimal_places=1, - ) - + ", " - + BenchmarkerTaskProgressState.format_progress_display( - value=self.tokens_itl, - label="ITL", - units="ms", - total_characters=12, - digits_places=3, - decimal_places=1, - ) - + ", " - + BenchmarkerTaskProgressState.format_progress_display( - value=self.prompt_tokens, - label="Prompt", - total_characters=12, - digits_places=4, - decimal_places=0, - ) - + ", " - + BenchmarkerTaskProgressState.format_progress_display( - value=self.output_tokens, - label="Gen", - total_characters=12, - digits_places=4, - decimal_places=0, - ) - ) - - -BTPS = TypeVar("BTPS", bound=BenchmarkerTaskProgressState) - - -class BenchmarkerProgressDisplay(Generic[BTPS]): - def __init__(self, display_scheduler_stats: bool): - self.display_scheduler_stats = display_scheduler_stats - self.started = False - self.benchmarker_tasks_progress = Progress(*self.create_task_progress_columns()) - self.benchmarker_tasks_panel = Panel( - self.benchmarker_tasks_progress, - title="Benchmarks", - title_align="left", - expand=True, - ) - self.benchmarker_progress = Progress( - TextColumn("Generating...", style=f"italic {Colors.PROGRESS}"), - BarColumn( - bar_width=None, - complete_style=Colors.PROGRESS, - finished_style=Colors.SUCCESS, - ), - TextColumn( - "({task.fields[completed_benchmarks]}/{task.fields[total_benchmarks]})", - style=Colors.PROGRESS, - ), - TextColumn("["), - TimeElapsedColumn(), - TextColumn("<"), - TimeRemainingColumn(), - TextColumn("]"), - ) - self.benchmarker_live = Live( - Group( - self.benchmarker_tasks_panel, - self.benchmarker_progress, - ), - redirect_stdout=True, - redirect_stderr=True, - ) - self.active_task: Optional[TaskID] = None - self.benchmarker_tasks: list[BTPS] = [] - self.progress_task: Optional[TaskID] = None - - def update(self, result: BenchmarkerResult): - if result.type_ == "run_start": - if self.started: - raise RuntimeError("Progress display already started.") - - self.handle_start(result) - self.started = True - elif result.type_ == "run_complete": - if not self.started: - raise RuntimeError("Progress display not started.") - - self.handle_end(result) - self.started = False - else: - if not self.started: - raise RuntimeError("Progress display not started.") - - self.handle_update(result) - - def handle_start(self, result: BenchmarkerResult): - self.benchmarker_live.start() - - for index, strategy_type in enumerate(result.profile.strategy_types): - task_id = self.benchmarker_tasks_progress.add_task( - description=strategy_type, - start=False, - total=None, - completed=0, - visible=False, - ) - task_progress_state = self.create_task_progress_state( - task_id=task_id, - index=index, - strategy_type=strategy_type, - result=result, - ) - self.benchmarker_tasks.append(task_progress_state) - self.benchmarker_tasks_progress.update( - task_id, - description=task_progress_state.description, - visible=True, - **task_progress_state.fields, # type: ignore[arg-type] - ) - - self.progress_task = self.benchmarker_progress.add_task( - "", - total=len(self.benchmarker_tasks) * 1000, - completed_benchmarks=0, - total_benchmarks=len(self.benchmarker_tasks), - ) - - def handle_update(self, result: BenchmarkerResult): - current_state: BTPS = self.benchmarker_tasks[result.current_index] - - if result.type_ == "scheduler_start": - self.handle_update_scheduler_start(current_state, result) - self.active_task = current_state.task_id - elif result.type_ == "scheduler_update": - self.handle_update_scheduler_update(current_state, result) - elif result.type_ == "scheduler_complete": - self.handle_update_scheduler_complete(current_state, result) - elif result.type_ == "benchmark_compiled": - self.handle_update_benchmark_compiled(current_state, result) - else: - raise ValueError(f"Unknown result type: {result.type_}") - - if self.progress_task is None: - raise RuntimeError("Progress task not set.") - - self.benchmarker_tasks_progress.update( - current_state.task_id, - description=current_state.description, - completed=current_state.completed, - total=current_state.total, - **current_state.fields, # type: ignore[arg-type] - ) - self.benchmarker_progress.update( - self.progress_task, - completed=(result.current_index * 1000) + current_state.completed, - total=1000 * len(self.benchmarker_tasks), - completed_benchmarks=( - result.current_index + (1 if current_state.ended else 0) - ), - total_benchmarks=len(self.benchmarker_tasks), - ) - - if current_state.ended: - self.benchmarker_tasks_progress.stop_task(current_state.task_id) - self.active_task = None - - def handle_update_scheduler_start( - self, progress_state: BTPS, result: BenchmarkerResult - ): - if self.active_task is not None: - raise RuntimeError("Active task already set.") - - progress_state.strategy = result.current_strategy # type: ignore[assignment] - progress_state.started = True - current_aggregator: BenchmarkAggregator = result.current_aggregator # type: ignore[assignment] - progress_state.start_time = ( - current_aggregator.requests_stats.totals.total.start_time - ) - progress_state.max_number = current_aggregator.args.max_number - progress_state.max_duration = current_aggregator.args.max_duration - - def handle_update_scheduler_update( - self, progress_state: BTPS, result: BenchmarkerResult - ): - if self.active_task is None: - raise RuntimeError("Active task not set.") - - if self.active_task != progress_state.task_id: - raise RuntimeError("Active task does not match current task.") - - current_aggregator: BenchmarkAggregator = result.current_aggregator # type: ignore[assignment] - progress_state.in_warmup = current_aggregator.in_warmup - progress_state.in_cooldown = current_aggregator.in_cooldown - progress_state.requests_rate = ( - current_aggregator.requests_stats.totals.successful.rate - ) - progress_state.request_latency = ( - current_aggregator.requests_stats.request_time.mean - ) - progress_state.requests_processing = ( - current_aggregator.scheduler_stats.processing_requests.last - ) - progress_state.requests_successful = ( - current_aggregator.requests_stats.totals.successful.total - ) - progress_state.requests_incomplete = ( - current_aggregator.requests_stats.totals.incomplete.total - ) - progress_state.requests_errored = ( - current_aggregator.requests_stats.totals.errored.total - ) - progress_state.worker_overheads_time_ms = ( - current_aggregator.requests_stats.scheduled_time_delay.mean_ms - + current_aggregator.requests_stats.worker_start_delay.mean_ms - ) - progress_state.backend_overheads_time_ms = ( - current_aggregator.requests_stats.request_time_delay.mean_ms - ) - progress_state.requests_sleep_time_ms = ( - current_aggregator.requests_stats.scheduled_time_sleep.mean_ms - ) - progress_state.requests_targeted_start_time_delay_ms = ( - current_aggregator.requests_stats.request_start_time_targeted_delay.mean_ms - ) - - def handle_update_scheduler_complete( - self, - progress_state: BTPS, - result: BenchmarkerResult, # noqa: ARG002 - ): - if self.active_task is None: - raise RuntimeError("Active task not set.") - - if self.active_task != progress_state.task_id: - raise RuntimeError("Active task does not match current task.") - - progress_state.in_warmup = False - progress_state.in_cooldown = False - progress_state.compiling = True - - def handle_update_benchmark_compiled( - self, progress_state: BTPS, result: BenchmarkerResult - ): - if self.active_task is None: - raise RuntimeError("Active task not set.") - - if self.active_task != progress_state.task_id: - raise RuntimeError("Active task does not match current task.") - - current_benchmark: Benchmark = result.current_benchmark # type: ignore[assignment] - progress_state.compiling = False - progress_state.ended = True - progress_state.requests_rate = ( - current_benchmark.metrics.requests_per_second.successful.mean - ) - progress_state.requests_processing = ( - current_benchmark.metrics.request_concurrency.successful.mean - ) - - def handle_end(self, result: BenchmarkerResult): # noqa: ARG002 - if self.progress_task is None: - raise RuntimeError("Progress task not set.") - - self.benchmarker_progress.update( - self.progress_task, - completed=len(self.benchmarker_tasks) * 1000, - total=len(self.benchmarker_tasks) * 1000, - completed_benchmarks=len(self.benchmarker_tasks), - total_benchmarks=len(self.benchmarker_tasks), - ) - self.benchmarker_progress.stop_task(self.progress_task) - self.benchmarker_live.stop() - self.active_task = None - self.benchmarker_tasks = [] - self.progress_task = None - - def create_task_progress_columns(self) -> list[ProgressColumn]: - columns = [ - TextColumn("[{task.fields[start_time]}]"), - SpinnerColumn(style=Colors.PROGRESS), - TaskProgressColumn(style=Colors.PROGRESS), - TextColumn("{task.description}"), - TextColumn("({task.fields[progress_status]})"), - TextColumn(" "), - ] - - if not self.display_scheduler_stats: - columns += [ - TextColumn("{task.fields[requests_summary]}\n"), - ] - else: - columns += [ - TextColumn( - "{task.fields[requests_summary]}\n{task.fields[scheduler_stats]}\n" - ), - ] - - return columns - - def create_task_progress_state( - self, - task_id: TaskID, - index: int, # noqa: ARG002 - strategy_type: StrategyType, - result: BenchmarkerResult, # noqa: ARG002 - ) -> BTPS: - return BenchmarkerTaskProgressState( # type: ignore[return-value] - display_scheduler_stats=self.display_scheduler_stats, - task_id=task_id, - strategy=strategy_type, - ) - - -class GenerativeTextBenchmarkerProgressDisplay( - BenchmarkerProgressDisplay[GenerativeTextBenchmarkerTaskProgressState] -): - def handle_update_scheduler_update( - self, - progress_state: GenerativeTextBenchmarkerTaskProgressState, - result: BenchmarkerResult, - ): - super().handle_update_scheduler_update(progress_state, result) - current_aggregator: GenerativeBenchmarkAggregator = result.current_aggregator # type: ignore[assignment] - progress_state.output_tokens = ( - current_aggregator.requests_stats.output_tokens.mean - ) - progress_state.prompt_tokens = ( - current_aggregator.requests_stats.prompt_tokens.mean - ) - progress_state.output_tokens_rate = ( - current_aggregator.requests_stats.output_tokens.rate - ) - progress_state.total_tokens_rate = ( - current_aggregator.requests_stats.total_tokens.rate - ) - progress_state.tokens_ttft = ( - current_aggregator.requests_stats.time_to_first_token.mean_ms - ) - progress_state.tokens_itl = ( - current_aggregator.requests_stats.inter_token_latency.mean_ms - ) - - def handle_update_benchmark_compiled( - self, - progress_state: GenerativeTextBenchmarkerTaskProgressState, - result: BenchmarkerResult, - ): - super().handle_update_benchmark_compiled(progress_state, result) - - current_benchmark: GenerativeBenchmark = result.current_benchmark # type: ignore[assignment] - progress_state.request_latency = ( - current_benchmark.metrics.request_latency.successful.mean - ) - progress_state.requests_successful = current_benchmark.request_totals.successful - progress_state.requests_errored = current_benchmark.request_totals.errored - progress_state.requests_incomplete = current_benchmark.request_totals.incomplete - progress_state.output_tokens = ( - current_benchmark.metrics.output_token_count.successful.mean - ) - progress_state.prompt_tokens = ( - current_benchmark.metrics.prompt_token_count.successful.mean - ) - progress_state.output_tokens_rate = ( - current_benchmark.metrics.output_tokens_per_second.successful.mean - ) - progress_state.total_tokens_rate = ( - current_benchmark.metrics.tokens_per_second.successful.mean - ) - progress_state.tokens_ttft = ( - current_benchmark.metrics.time_to_first_token_ms.successful.mean - ) - progress_state.tokens_itl = ( - current_benchmark.metrics.inter_token_latency_ms.successful.mean - ) - - def create_task_progress_state( - self, - task_id: TaskID, - index: int, # noqa: ARG002 - strategy_type: StrategyType, - result: BenchmarkerResult, # noqa: ARG002 - ) -> GenerativeTextBenchmarkerTaskProgressState: - return GenerativeTextBenchmarkerTaskProgressState( - display_scheduler_stats=self.display_scheduler_stats, - task_id=task_id, - strategy=strategy_type, - ) - - def create_task_progress_columns(self) -> list[ProgressColumn]: - columns = super().create_task_progress_columns() - columns = columns[:-1] # remove the last display info column - - if not self.display_scheduler_stats: - columns += [ - TextColumn( - "{task.fields[requests_summary]}\n{task.fields[tokens_summary]}", - ), - ] - else: - columns += [ - TextColumn( - "{task.fields[requests_summary]}\n{task.fields[tokens_summary]}\n{task.fields[scheduler_stats]}", - ), - ] - - return columns diff --git a/src/guidellm/config.py b/src/guidellm/config.py deleted file mode 100644 index 9afcea74..00000000 --- a/src/guidellm/config.py +++ /dev/null @@ -1,215 +0,0 @@ -import json -from collections.abc import Sequence -from enum import Enum -from typing import Literal, Optional - -from pydantic import BaseModel, Field, model_validator -from pydantic_settings import BaseSettings, SettingsConfigDict - -__all__ = [ - "DatasetSettings", - "Environment", - "LoggingSettings", - "OpenAISettings", - "Settings", - "print_config", - "reload_settings", - "settings", -] - - -class Environment(str, Enum): - """ - Enum for the supported environments - """ - - LOCAL = "local" - DEV = "dev" - STAGING = "staging" - PROD = "prod" - - -ENV_REPORT_MAPPING = { - Environment.PROD: "https://guidellm.neuralmagic.com/local-report/index.html", - Environment.STAGING: "https://staging.guidellm.neuralmagic.com/local-report/index.html", - Environment.DEV: "https://dev.guidellm.neuralmagic.com/local-report/index.html", - Environment.LOCAL: "tests/dummy/report.html", -} - - -class LoggingSettings(BaseModel): - """ - Logging settings for the application - """ - - disabled: bool = False - clear_loggers: bool = True - console_log_level: str = "WARNING" - log_file: Optional[str] = None - log_file_level: Optional[str] = None - - -class DatasetSettings(BaseModel): - """ - Dataset settings for the application - """ - - preferred_data_columns: list[str] = Field( - default_factory=lambda: [ - "prompt", - "instruction", - "input", - "inputs", - "question", - "context", - "text", - "content", - "body", - "data", - ] - ) - preferred_data_splits: list[str] = Field( - default_factory=lambda: ["test", "tst", "validation", "val", "train"] - ) - - -class OpenAISettings(BaseModel): - """ - OpenAI settings for the application to connect to the API - for OpenAI server based pathways - """ - - api_key: Optional[str] = None - bearer_token: Optional[str] = None - organization: Optional[str] = None - project: Optional[str] = None - base_url: str = "http://localhost:8000" - max_output_tokens: int = 16384 - - -class Settings(BaseSettings): - """ - All the settings are powered by pydantic_settings and could be - populated from the .env file. - - The format to populate the settings is next - - ```sh - export GUIDELLM__LOGGING__DISABLED=true - export GUIDELLM__OPENAI__API_KEY=****** - ``` - """ - - model_config = SettingsConfigDict( - env_prefix="GUIDELLM__", - env_nested_delimiter="__", - extra="ignore", - validate_default=True, - env_file=".env", - ) - - # general settings - env: Environment = Environment.PROD - default_async_loop_sleep: float = 10e-5 - logging: LoggingSettings = LoggingSettings() - default_sweep_number: int = 10 - - # HTTP settings - request_timeout: int = 60 * 5 # 5 minutes - request_http2: bool = True - - # Scheduler settings - max_concurrency: int = 512 - max_worker_processes: int = 10 - max_add_requests_per_loop: int = 20 - - # Data settings - dataset: DatasetSettings = DatasetSettings() - - # Request/stats settings - preferred_prompt_tokens_source: Optional[ - Literal["request", "response", "local"] - ] = "response" - preferred_output_tokens_source: Optional[ - Literal["request", "response", "local"] - ] = "response" - preferred_backend: Literal["openai"] = "openai" - preferred_route: Literal["text_completions", "chat_completions"] = ( - "text_completions" - ) - openai: OpenAISettings = OpenAISettings() - - # Output settings - table_border_char: str = "=" - table_headers_border_char: str = "-" - table_column_separator_char: str = "|" - - @model_validator(mode="after") - @classmethod - def set_default_source(cls, values): - return values - - def generate_env_file(self) -> str: - """ - Generate the .env file from the current settings - """ - return Settings._recursive_generate_env( - self, - self.model_config["env_prefix"], # type: ignore # noqa: PGH003 - self.model_config["env_nested_delimiter"], # type: ignore # noqa: PGH003 - ) - - @staticmethod - def _recursive_generate_env(model: BaseModel, prefix: str, delimiter: str) -> str: - env_file = "" - add_models = [] - for key, value in model.model_dump().items(): - if isinstance(value, BaseModel): - # add nested properties to be processed after the current level - add_models.append((key, value)) - continue - - dict_values = ( - { - f"{prefix}{key.upper()}{delimiter}{sub_key.upper()}": sub_value - for sub_key, sub_value in value.items() - } - if isinstance(value, dict) - else {f"{prefix}{key.upper()}": value} - ) - - for tag, sub_value in dict_values.items(): - if isinstance(sub_value, Sequence) and not isinstance(sub_value, str): - value_str = ",".join(f'"{item}"' for item in sub_value) - env_file += f"{tag}=[{value_str}]\n" - elif isinstance(sub_value, dict): - value_str = json.dumps(sub_value) - env_file += f"{tag}={value_str}\n" - elif not sub_value: - env_file += f"{tag}=\n" - else: - env_file += f'{tag}="{sub_value}"\n' - - for key, value in add_models: - env_file += Settings._recursive_generate_env( - value, f"{prefix}{key.upper()}{delimiter}", delimiter - ) - return env_file - - -settings = Settings() - - -def reload_settings(): - """ - Reload the settings from the environment variables - """ - new_settings = Settings() - settings.__dict__.update(new_settings.__dict__) - - -def print_config(): - """ - Print the current configuration settings - """ - print(f"Settings: \n{settings.generate_env_file()}") # noqa: T201 diff --git a/src/guidellm/data/__init__.py b/src/guidellm/data/__init__.py deleted file mode 100644 index 8a48204e..00000000 --- a/src/guidellm/data/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -""" -Required for python < 3.12 -https://docs.python.org/3/library/importlib.resources.html#importlib.resources.files -""" diff --git a/src/guidellm/data/prideandprejudice.txt.gz b/src/guidellm/data/prideandprejudice.txt.gz deleted file mode 100644 index 8c7a10727c239964fff2e203f07318b29108eb2c..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 241795 zcmV(rK<>XEiwFoug!X3w|8R0?WMyG)WN>n2YIS63V`VOMcys`az1xl>Ns=Y_u3y1; zr#rB~KJ11+1hTTaJ7c%44sEk5_&49kX}c<+ww 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z!qGaD7<5*Oh!bbT=k#(LBGI*@$VwGyy%gltCy6`Pn73s{GQqi6VNe*}&^1te05>7kwQ3omzB6;(!_A?wW)DF#pcZnME;pH|uEB8aqtJGSOh>dI0>Za+B8c9Pu~%lg6H~Zqw5*&51*MDb zD@J@;x+ta1lgD4c?1RZxwCCd-3R-hBwRYlt%Yx!seMBS&)W-0}z7e|Z>GKUOULVX9 X_J@c2{~rJV|NjF3P@u7{S3n8?q;DzF diff --git a/src/guidellm/dataset/__init__.py b/src/guidellm/dataset/__init__.py deleted file mode 100644 index b90b72ff..00000000 --- a/src/guidellm/dataset/__init__.py +++ /dev/null @@ -1,22 +0,0 @@ -from .creator import ColumnInputTypes, DatasetCreator -from .entrypoints import load_dataset -from .file import FileDatasetCreator -from .hf_datasets import HFDatasetsCreator -from .in_memory import InMemoryDatasetCreator -from .synthetic import ( - SyntheticDatasetConfig, - SyntheticDatasetCreator, - SyntheticTextItemsGenerator, -) - -__all__ = [ - "ColumnInputTypes", - "DatasetCreator", - "FileDatasetCreator", - "HFDatasetsCreator", - "InMemoryDatasetCreator", - "SyntheticDatasetConfig", - "SyntheticDatasetCreator", - "SyntheticTextItemsGenerator", - "load_dataset", -] diff --git a/src/guidellm/dataset/creator.py b/src/guidellm/dataset/creator.py deleted file mode 100644 index a74ec8c0..00000000 --- a/src/guidellm/dataset/creator.py +++ /dev/null @@ -1,213 +0,0 @@ -from abc import ABC, abstractmethod -from pathlib import Path -from typing import Any, Literal, Optional, Union - -from datasets import Dataset, DatasetDict, IterableDataset, IterableDatasetDict -from transformers import PreTrainedTokenizerBase # type: ignore[import] - -__all__ = ["ColumnInputTypes", "DatasetCreator"] - -ColumnInputTypes = Literal[ - "prompt_column", - "text_column", - "prompt_tokens_count_column", - "output_tokens_count_column", -] - - -class DatasetCreator(ABC): - DEFAULT_SPLITS_TRAIN = [ - "train", - "training", - "train_set", - "training_set", - "train_dataset", - "training_dataset", - "train_data", - "training_data", - "pretrain", - "pretrain_set", - "pretrain_dataset", - "pretrain_data", - "pretraining", - ] - DEFAULT_SPLITS_CALIB = [ - "calibration", - "calib", - "cal", - "calibration_set", - "calib_set", - "cal_set", - "calibration_dataset", - "calib_dataset", - "cal_set", - "calibration_data", - "calib_data", - "cal_data", - ] - DEFAULT_SPLITS_VAL = [ - "validation", - "val", - "valid", - "validation_set", - "val_set", - "validation_dataset", - "val_dataset", - "validation_data", - "val_data", - "dev", - "dev_set", - "dev_dataset", - "dev_data", - ] - DEFAULT_SPLITS_TEST = [ - "test", - "testing", - "test_set", - "testing_set", - "test_dataset", - "testing_dataset", - "test_data", - "testing_data", - "eval", - "eval_set", - "eval_dataset", - "eval_data", - ] - DEFAULT_SPLITS_DATASET: dict[str, str] = {} - - @classmethod - def create( - cls, - data: Any, - data_args: Optional[dict[str, Any]], - processor: Optional[Union[str, Path, PreTrainedTokenizerBase]], - processor_args: Optional[dict[str, Any]], - random_seed: int = 42, - split_pref_order: Optional[list[str]] = None, - ) -> tuple[Union[Dataset, IterableDataset], dict[ColumnInputTypes, str]]: - if not cls.is_supported(data, data_args): - raise ValueError(f"Unsupported data type: {type(data)} given for {data}. ") - - split = cls.extract_args_split(data_args) - column_mappings = cls.extract_args_column_mappings(data_args) - dataset = cls.handle_create( - data, data_args, processor, processor_args, random_seed - ) - - if isinstance(dataset, (DatasetDict, IterableDatasetDict)): - dataset = cls.extract_dataset_split(dataset, split, split_pref_order) - - if not isinstance(dataset, (Dataset, IterableDataset)): - raise ValueError( - f"Unsupported data type: {type(dataset)} given for {dataset}." - ) - - return dataset, column_mappings - - @classmethod - def extract_args_split(cls, data_args: Optional[dict[str, Any]]) -> str: - split = "auto" - - if data_args and "split" in data_args: - split = data_args["split"] - del data_args["split"] - - return split - - @classmethod - def extract_args_column_mappings( - cls, - data_args: Optional[dict[str, Any]], - ) -> dict[ColumnInputTypes, str]: - columns: dict[ColumnInputTypes, str] = {} - - if data_args: - if "prompt_column" in data_args: - columns["prompt_column"] = data_args["prompt_column"] - del data_args["prompt_column"] - - if "prompt_tokens_count_column" in data_args: - columns["prompt_tokens_count_column"] = data_args[ - "prompt_tokens_count_column" - ] - del data_args["prompt_tokens_count_column"] - - if "output_tokens_count_column" in data_args: - columns["output_tokens_count_column"] = data_args[ - "output_tokens_count_column" - ] - del data_args["output_tokens_count_column"] - - return columns - - @classmethod - def extract_dataset_name( - cls, dataset: Union[Dataset, IterableDataset, DatasetDict, IterableDatasetDict] - ) -> Optional[str]: - if isinstance(dataset, (DatasetDict, IterableDatasetDict)): - dataset = dataset[list(dataset.keys())[0]] - - if isinstance(dataset, (Dataset, IterableDataset)): - if not hasattr(dataset, "info") or not hasattr( - dataset.info, "dataset_name" - ): - return None - - return dataset.info.dataset_name - - raise ValueError(f"Unsupported data type: {type(dataset)} given for {dataset}.") - - @classmethod - def extract_dataset_split( - cls, - dataset: Union[DatasetDict, IterableDatasetDict], - specified_split: Union[Literal["auto"], str] = "auto", - split_pref_order: Optional[Union[Literal["auto"], list[str]]] = "auto", - ) -> Union[Dataset, IterableDataset]: - if not isinstance(dataset, (DatasetDict, IterableDatasetDict)): - raise ValueError( - f"Unsupported data type: {type(dataset)} given for {dataset}." - ) - - if specified_split != "auto": - if specified_split not in dataset: - raise ValueError( - f"Split {specified_split} not found in dataset {dataset}." - ) - - return dataset[specified_split] - - dataset_name = cls.extract_dataset_name(dataset) - - if dataset_name and dataset_name in cls.DEFAULT_SPLITS_DATASET: - return dataset[cls.DEFAULT_SPLITS_DATASET[dataset_name]] - - if split_pref_order == "auto": - split_pref_order = [ - *cls.DEFAULT_SPLITS_TEST, - *cls.DEFAULT_SPLITS_VAL, - *cls.DEFAULT_SPLITS_CALIB, - *cls.DEFAULT_SPLITS_TRAIN, - ] - - for test_split in split_pref_order or []: - if test_split in dataset: - return dataset[test_split] - - return dataset[list(dataset.keys())[0]] - - @classmethod - @abstractmethod - def is_supported(cls, data: Any, data_args: Optional[dict[str, Any]]) -> bool: ... - - @classmethod - @abstractmethod - def handle_create( - cls, - data: Any, - data_args: Optional[dict[str, Any]], - processor: Optional[Union[str, Path, PreTrainedTokenizerBase]], - processor_args: Optional[dict[str, Any]], - random_seed: int, - ) -> Union[Dataset, DatasetDict, IterableDataset, IterableDatasetDict]: ... diff --git a/src/guidellm/dataset/entrypoints.py b/src/guidellm/dataset/entrypoints.py deleted file mode 100644 index cf689956..00000000 --- a/src/guidellm/dataset/entrypoints.py +++ /dev/null @@ -1,42 +0,0 @@ -from pathlib import Path -from typing import Any, Optional, Union - -from datasets import Dataset, IterableDataset -from transformers import PreTrainedTokenizerBase # type: ignore[import] - -from guidellm.dataset.creator import ColumnInputTypes -from guidellm.dataset.file import FileDatasetCreator -from guidellm.dataset.hf_datasets import HFDatasetsCreator -from guidellm.dataset.in_memory import InMemoryDatasetCreator -from guidellm.dataset.synthetic import SyntheticDatasetCreator - -__all__ = ["load_dataset"] - - -def load_dataset( - data: Any, - data_args: Optional[dict[str, Any]], - processor: Optional[Union[str, Path, PreTrainedTokenizerBase]], - processor_args: Optional[dict[str, Any]], - random_seed: int = 42, - split_pref_order: Optional[list[str]] = None, -) -> tuple[Union[Dataset, IterableDataset], dict[ColumnInputTypes, str]]: - creators = [ - InMemoryDatasetCreator, - SyntheticDatasetCreator, - FileDatasetCreator, - HFDatasetsCreator, - ] - - for creator in creators: - if creator.is_supported(data, data_args): - return creator.create( - data, - data_args, - processor, - processor_args, - random_seed, - split_pref_order, - ) - - raise ValueError(f"Unsupported data type: {type(data)} given for {data}. ") diff --git a/src/guidellm/dataset/file.py b/src/guidellm/dataset/file.py deleted file mode 100644 index 5d6df1d9..00000000 --- a/src/guidellm/dataset/file.py +++ /dev/null @@ -1,92 +0,0 @@ -from pathlib import Path -from typing import Any, Optional, Union - -import pandas as pd # type: ignore[import] -from datasets import ( - Dataset, - DatasetDict, - IterableDataset, - IterableDatasetDict, - load_dataset, -) -from transformers import PreTrainedTokenizerBase # type: ignore[import] - -from guidellm.dataset.creator import DatasetCreator - -__all__ = ["FileDatasetCreator"] - - -class FileDatasetCreator(DatasetCreator): - SUPPORTED_TYPES = { - ".txt", - ".text", - ".csv", - ".json", - ".jsonl", - ".parquet", - ".arrow", - ".hdf5", - ".tar", - } - - @classmethod - def is_supported(cls, data: Any, data_args: Optional[dict[str, Any]]) -> bool: # noqa: ARG003 - if isinstance(data, (str, Path)) and (path := Path(data)).exists(): - # local folder or py file, assume supported - return path.suffix.lower() in cls.SUPPORTED_TYPES - - return False - - @classmethod - def handle_create( - cls, - data: Any, - data_args: Optional[dict[str, Any]], - processor: Optional[Union[str, Path, PreTrainedTokenizerBase]], # noqa: ARG003 - processor_args: Optional[dict[str, Any]], # noqa: ARG003 - random_seed: int, # noqa: ARG003 - ) -> Union[Dataset, DatasetDict, IterableDataset, IterableDatasetDict]: - if not isinstance(data, (str, Path)): - raise ValueError(f"Unsupported data type: {type(data)} given for {data}. ") - - path = Path(data) - if not path.exists(): - raise FileNotFoundError(f"File not found: {path}") - - if not path.is_file(): - raise ValueError(f"Unsupported data type: {path} given for {path}. ") - - if path.suffix.lower() not in cls.SUPPORTED_TYPES: - raise ValueError(f"Unsupported file type: {path.suffix} given for {path}. ") - - return cls.load_dataset(path, data_args) - - @classmethod - def load_dataset( - cls, path: Path, data_args: Optional[dict[str, Any]] - ) -> Union[Dataset, IterableDataset]: - if path.suffix.lower() in {".txt", ".text"}: - with path.open("r") as file: - items = file.readlines() - - dataset = Dataset.from_dict({"text": items}, **(data_args or {})) - elif path.suffix.lower() == ".csv": - dataset = load_dataset("csv", data_files=str(path), **(data_args or {})) - elif path.suffix.lower() in {".json", ".jsonl"}: - dataset = load_dataset("json", data_files=str(path), **(data_args or {})) - elif path.suffix.lower() == ".parquet": - dataset = load_dataset("parquet", data_files=str(path), **(data_args or {})) - elif path.suffix.lower() == ".arrow": - dataset = load_dataset("arrow", data_files=str(path), **(data_args or {})) - elif path.suffix.lower() == ".hdf5": - dataset = Dataset.from_pandas(pd.read_hdf(str(path)), **(data_args or {})) - elif path.suffix.lower() == ".db": - dataset = Dataset.from_sql(con=str(path), **(data_args or {})) - elif path.suffix.lower() == ".tar": - dataset = load_dataset( - "webdataset", data_files=str(path), **(data_args or {}) - ) - else: - raise ValueError(f"Unsupported file type: {path.suffix} given for {path}. ") - - return dataset diff --git a/src/guidellm/dataset/hf_datasets.py b/src/guidellm/dataset/hf_datasets.py deleted file mode 100644 index 7f91facd..00000000 --- a/src/guidellm/dataset/hf_datasets.py +++ /dev/null @@ -1,62 +0,0 @@ -from pathlib import Path -from typing import Any, Optional, Union - -from datasets import ( - Dataset, - DatasetDict, - IterableDataset, - IterableDatasetDict, - get_dataset_config_info, - load_dataset, -) -from transformers import PreTrainedTokenizerBase # type: ignore[import] - -from guidellm.dataset.creator import DatasetCreator - -__all__ = ["HFDatasetsCreator"] - - -class HFDatasetsCreator(DatasetCreator): - @classmethod - def is_supported(cls, data: Any, data_args: Optional[dict[str, Any]]) -> bool: # noqa: ARG003 - if isinstance( - data, (Dataset, DatasetDict, IterableDataset, IterableDatasetDict) - ): - # base type is supported - return True - - if isinstance(data, (str, Path)) and (path := Path(data)).exists(): - # local folder or py file, assume supported - return path.is_dir() or path.suffix == ".py" - - if isinstance(data, (str, Path)): - try: - # try to load dataset - return get_dataset_config_info(data) is not None - except Exception: # noqa: BLE001, S110 - pass - - return False - - @classmethod - def handle_create( - cls, - data: Any, - data_args: Optional[dict[str, Any]], - processor: Optional[Union[str, Path, PreTrainedTokenizerBase]], # noqa: ARG003 - processor_args: Optional[dict[str, Any]], # noqa: ARG003 - random_seed: int, # noqa: ARG003 - ) -> Union[Dataset, DatasetDict, IterableDataset, IterableDatasetDict]: - if isinstance(data, (str, Path)): - data = load_dataset(data, **(data_args or {})) - elif data_args: - raise ValueError( - f"data_args should not be provided when data is a {type(data)}" - ) - - if isinstance( - data, (Dataset, DatasetDict, IterableDataset, IterableDatasetDict) - ): - return data - - raise ValueError(f"Unsupported data type: {type(data)} given for {data}. ") diff --git a/src/guidellm/dataset/in_memory.py b/src/guidellm/dataset/in_memory.py deleted file mode 100644 index af84f658..00000000 --- a/src/guidellm/dataset/in_memory.py +++ /dev/null @@ -1,132 +0,0 @@ -from collections.abc import Iterable -from pathlib import Path -from typing import Any, Optional, Union - -from datasets import ( - Dataset, - DatasetDict, - IterableDataset, - IterableDatasetDict, -) -from transformers import PreTrainedTokenizerBase # type: ignore[import] - -from guidellm.dataset.creator import DatasetCreator - -__all__ = ["InMemoryDatasetCreator"] - - -class InMemoryDatasetCreator(DatasetCreator): - @classmethod - def is_supported(cls, data: Any, data_args: Optional[dict[str, Any]]) -> bool: # noqa: ARG003 - return isinstance(data, Iterable) and not isinstance(data, str) - - @classmethod - def handle_create( - cls, - data: Any, - data_args: Optional[dict[str, Any]], - processor: Optional[Union[str, Path, PreTrainedTokenizerBase]], # noqa: ARG003 - processor_args: Optional[dict[str, Any]], # noqa: ARG003 - random_seed: int, # noqa: ARG003 - ) -> Union[Dataset, DatasetDict, IterableDataset, IterableDatasetDict]: - if not isinstance(data, Iterable): - raise TypeError( - f"Unsupported data format. Expected Iterable[Any], got {type(data)}" - ) - - if not data: - raise ValueError("Data is empty") - - if isinstance(data, dict): - # assume data is a dictionary of columns and values: {"c1": ["i1", "i2"]} - data_dict = cls.format_data_dict(data) - elif isinstance(data[0], dict): # type: ignore[index] - # assume data is a list of dictionaries: [{"c1": "i1"}, {"c1": "i2"}] - data_dict = cls.format_data_iterable_dicts(data) - else: - # assume data is a list of items with no columns: ["i1", "i2"] - data_dict = cls.format_data_iterable_values(data) - - return Dataset.from_dict(data_dict, **(data_args or {})) - - @classmethod - def format_data_dict(cls, data: dict[Any, Any]) -> dict[str, Any]: - if not isinstance(data, dict): - raise TypeError( - f"Unsupported data format. Expected Dict[str, Iterable[Any]], " - f"got {type(data)}" - ) - - if not all( - isinstance(key, str) and isinstance(val, Iterable) - for key, val in data.items() - ): - raise TypeError( - "Unsupported data format. Expected Dict[str, Iterable[Any]], " - f"got {type(data)}" - ) - - samples = len(list(data.values())[0]) - if not all(len(val) == samples for val in data.values()): - raise ValueError( - "Unsupported data format. Not all columns have the same number samples " - f"for {data}" - ) - - return data - - @classmethod - def format_data_iterable_dicts( - cls, data: Iterable[dict[Any, Any]] - ) -> dict[str, Any]: - if not isinstance(data, Iterable): - raise TypeError( - f"Unsupported data format. Expected Iterable[Dict[str, Any]], " - f"got {type(data)}" - ) - - if not all(isinstance(item, dict) for item in data): - raise TypeError( - f"Unsupported data format. Expected Iterable[Dict[str, Any]], " - f"got {type(data)}" - ) - - if not all(isinstance(key, str) for key in data[0]): # type: ignore[index] - raise TypeError( - "Unsupported data format. Expected Dict[str, Any], " - f"but one of the items had a non string column for {data}" - ) - - columns = list(data[0].keys()) # type: ignore[index] - if not all( - len(item) == len(columns) and all(key in item for key in columns) - for item in data - ): - raise ValueError( - "Unsupported data format. Not all items have the same columns " - f"for {data}" - ) - - data_dict: dict[str, Any] = {key: [] for key in columns} - for item in data: - for key, value in item.items(): - data_dict[key].append(value) - - return data_dict - - @classmethod - def format_data_iterable_values(cls, data: Iterable[Any]) -> dict[str, Any]: - if not isinstance(data, Iterable): - raise TypeError( - f"Unsupported data format. Expected Iterable[Iterable[Any]], " - f"got {type(data)}" - ) - - first_item = next(iter(data), None) - first_type = type(first_item) - if not all(isinstance(item, first_type) for item in data): - raise TypeError( - f"Unsupported data format. Not all types are the same for {data}" - ) - - return {"data": list(data)} diff --git a/src/guidellm/dataset/synthetic.py b/src/guidellm/dataset/synthetic.py deleted file mode 100644 index 3ccbce84..00000000 --- a/src/guidellm/dataset/synthetic.py +++ /dev/null @@ -1,262 +0,0 @@ -import json -import random -from collections.abc import Iterable, Iterator -from pathlib import Path -from typing import Any, Literal, Optional, Union - -import yaml -from datasets import ( - Dataset, - DatasetDict, - IterableDataset, - IterableDatasetDict, -) -from pydantic import BaseModel, Field -from transformers import PreTrainedTokenizerBase # type: ignore[import] - -from guidellm.dataset.creator import ColumnInputTypes, DatasetCreator -from guidellm.utils import EndlessTextCreator, IntegerRangeSampler, check_load_processor - -__all__ = [ - "SyntheticDatasetConfig", - "SyntheticDatasetCreator", - "SyntheticTextItemsGenerator", -] - - -class SyntheticDatasetConfig(BaseModel): - prompt_tokens: int = Field( - description="The average number of text tokens generated for prompts.", - gt=0, - ) - prompt_tokens_stdev: Optional[int] = Field( - description="The standard deviation of the tokens generated for prompts.", - gt=0, - default=None, - ) - prompt_tokens_min: Optional[int] = Field( - description="The minimum number of text tokens generated for prompts.", - gt=0, - default=None, - ) - prompt_tokens_max: Optional[int] = Field( - description="The maximum number of text tokens generated for prompts.", - gt=0, - default=None, - ) - output_tokens: int = Field( - description="The average number of text tokens generated for outputs.", - gt=0, - ) - output_tokens_stdev: Optional[int] = Field( - description="The standard deviation of the tokens generated for outputs.", - gt=0, - default=None, - ) - output_tokens_min: Optional[int] = Field( - description="The minimum number of text tokens generated for outputs.", - gt=0, - default=None, - ) - output_tokens_max: Optional[int] = Field( - description="The maximum number of text tokens generated for outputs.", - gt=0, - default=None, - ) - samples: int = Field( - description="The number of samples to generate for the dataset.", - gt=0, - default=1000, - ) - source: str = Field( - description="The source of the text data to be used for generation.", - default="data:prideandprejudice.txt.gz", - ) - - @staticmethod - def parse_str(data: Union[str, Path]) -> "SyntheticDatasetConfig": - if ( - isinstance(data, Path) - or data.strip().endswith(".config") - or data.strip().endswith(".yaml") - ): - return SyntheticDatasetConfig.parse_config_file(data) - - if data.strip().startswith("{"): - return SyntheticDatasetConfig.parse_json(data) - - if data.count("=") > 1: - return SyntheticDatasetConfig.parse_key_value_pairs(data) - - raise ValueError( - f"Unsupported data format. Expected JSON or key-value pairs, got {data}" - ) - - @staticmethod - def parse_json(data: str) -> "SyntheticDatasetConfig": - config_dict = json.loads(data.strip()) - - return SyntheticDatasetConfig(**config_dict) - - @staticmethod - def parse_key_value_pairs(data: str) -> "SyntheticDatasetConfig": - config_dict = {} - items = data.strip().split(",") - for item in items: - key, value = item.split("=") - config_dict[key.strip()] = ( - int(value.strip()) if value.strip().isnumeric() else value.strip() - ) - - return SyntheticDatasetConfig(**config_dict) # type: ignore[arg-type] - - @staticmethod - def parse_config_file(data: Union[str, Path]) -> "SyntheticDatasetConfig": - with Path(data).open("r") as file: - config_dict = yaml.safe_load(file) - - return SyntheticDatasetConfig(**config_dict) - - -class SyntheticTextItemsGenerator( - Iterable[ - dict[ - Literal["prompt", "prompt_tokens_count", "output_tokens_count"], - Union[str, int], - ] - ] -): - def __init__( - self, - config: SyntheticDatasetConfig, - processor: PreTrainedTokenizerBase, - random_seed: int, - ): - self.config = config - self.processor = processor - self.random_seed = random_seed - self.text_creator = EndlessTextCreator( - data=config.source, - ) - - def __iter__( - self, - ) -> Iterator[ - dict[ - Literal["prompt", "prompt_tokens_count", "output_tokens_count"], - Union[str, int], - ] - ]: - prompt_tokens_sampler = IntegerRangeSampler( - average=self.config.prompt_tokens, - variance=self.config.prompt_tokens_stdev, - min_value=self.config.prompt_tokens_min, - max_value=self.config.prompt_tokens_max, - random_seed=self.random_seed, - ) - output_tokens_sampler = IntegerRangeSampler( - average=self.config.output_tokens, - variance=self.config.output_tokens_stdev, - min_value=self.config.output_tokens_min, - max_value=self.config.output_tokens_max, - random_seed=self.random_seed + 1, # ensure diff dist from prompts - ) - # ensure diff distribution from output tokens - rand = random.Random(self.random_seed + 2) # noqa: S311 - - for _, prompt_tokens, output_tokens in zip( - range(self.config.samples), - prompt_tokens_sampler, - output_tokens_sampler, - ): - start_index = rand.randint(0, len(self.text_creator.words)) - yield { - "prompt": self._create_prompt(prompt_tokens, start_index), - "prompt_tokens_count": prompt_tokens, - "output_tokens_count": output_tokens, - } - - def _create_prompt(self, prompt_tokens: int, start_index: int) -> str: - if prompt_tokens <= 0: - return "" - - left = start_index - right = start_index + 4 * prompt_tokens - - while left < right: - mid = (left + right) // 2 - test_prompt = self.text_creator.create_text(start_index, mid - start_index) - test_tokens = len(self.processor.tokenize(test_prompt)) - - if test_tokens == prompt_tokens: - return test_prompt - elif test_tokens < prompt_tokens: - left = mid + 1 - else: - right = mid - - return self.text_creator.create_text(start_index, left - start_index) - - -class SyntheticDatasetCreator(DatasetCreator): - @classmethod - def is_supported(cls, data: Any, data_args: Optional[dict[str, Any]]) -> bool: # noqa: ARG003 - if ( - isinstance(data, Path) - and data.exists() - and data.suffix in {".config", ".yaml"} - ): - return True - - if isinstance(data, str): - data_str: str = data.strip() - if ( - data_str.startswith("{") - or data_str.count("=") > 1 - or data_str.endswith((".config", ".yaml")) - ): - return True - - return False - - @classmethod - def handle_create( - cls, - data: Any, - data_args: Optional[dict[str, Any]], - processor: Optional[Union[str, Path, PreTrainedTokenizerBase]], - processor_args: Optional[dict[str, Any]], - random_seed: int, - ) -> Union[Dataset, DatasetDict, IterableDataset, IterableDatasetDict]: - processor = check_load_processor( - processor, - processor_args, - error_msg=( - "Processor/tokenizer required for synthetic dataset generation." - ), - ) - - config = SyntheticDatasetConfig.parse_str(data) - generator = SyntheticTextItemsGenerator(config, processor, random_seed) - items = list(generator) - - return Dataset.from_list(items, **(data_args or {})) - - @classmethod - def extract_args_column_mappings( - cls, - data_args: Optional[dict[str, Any]], - ) -> dict[ColumnInputTypes, str]: - data_args_columns = super().extract_args_column_mappings(data_args) - - if data_args_columns: - raise ValueError( - f"Column mappings are not supported for synthetic datasets. " - f"Got {data_args_columns}" - ) - - return { - "prompt_column": "prompt", - "prompt_tokens_count_column": "prompt_tokens_count", - "output_tokens_count_column": "output_tokens_count", - } diff --git a/src/guidellm/logger.py b/src/guidellm/logger.py deleted file mode 100644 index f26966c0..00000000 --- a/src/guidellm/logger.py +++ /dev/null @@ -1,83 +0,0 @@ -""" -Logger configuration for GuideLLM. - -This module provides a flexible logging configuration using the loguru library. -It supports console and file logging with options to configure via environment -variables or direct function calls. - -Environment Variables: - - GUIDELLM__LOGGING__DISABLED: Disable logging (default: false). - - GUIDELLM__LOGGING__CLEAR_LOGGERS: Clear existing loggers - from loguru (default: true). - - GUIDELLM__LOGGING__LOG_LEVEL: Log level for console logging - (default: none, options: DEBUG, INFO, WARNING, ERROR, CRITICAL). - - GUIDELLM__LOGGING__FILE: Path to the log file for file logging - (default: guidellm.log if log file level set else none) - - GUIDELLM__LOGGING__FILE_LEVEL: Log level for file logging - (default: INFO if log file set else none). - -Usage: - from guidellm import logger, configure_logger, LoggerConfig - - # Configure metrics with default settings - configure_logger( - config=LoggingConfig - disabled=False, - clear_loggers=True, - console_log_level="DEBUG", - log_file=None, - log_file_level=None, - ) - ) - - logger.debug("This is a debug message") - logger.info("This is an info message") -""" - -import sys - -from loguru import logger - -from guidellm.config import LoggingSettings, settings - -__all__ = ["configure_logger", "logger"] - - -def configure_logger(config: LoggingSettings = settings.logging): - """ - Configure the metrics for LLM Compressor. - This function sets up the console and file logging - as per the specified or default parameters. - - Note: Environment variables take precedence over the function parameters. - - :param config: The configuration for the logger to use. - :type config: LoggerConfig - """ - - if config.disabled: - logger.disable("guidellm") - return - - logger.enable("guidellm") - - if config.clear_loggers: - logger.remove() - - # log as a human readable string with the time, function, level, and message - logger.add( - sys.stdout, - level=config.console_log_level.upper(), - format="{time} | {function} | {level} - {message}", - ) - - if config.log_file or config.log_file_level: - log_file = config.log_file or "guidellm.log" - log_file_level = config.log_file_level or "INFO" - # log as json to the file for easier parsing - logger.add(log_file, level=log_file_level.upper(), serialize=True) - - -# invoke logger setup on import with default values -# enabling console logging with INFO and disabling file logging -configure_logger() diff --git a/src/guidellm/objects/__init__.py b/src/guidellm/objects/__init__.py deleted file mode 100644 index 89e3c9b9..00000000 --- a/src/guidellm/objects/__init__.py +++ /dev/null @@ -1,18 +0,0 @@ -from .pydantic import StandardBaseModel, StatusBreakdown -from .statistics import ( - DistributionSummary, - Percentiles, - RunningStats, - StatusDistributionSummary, - TimeRunningStats, -) - -__all__ = [ - "DistributionSummary", - "Percentiles", - "RunningStats", - "StandardBaseModel", - "StatusBreakdown", - "StatusDistributionSummary", - "TimeRunningStats", -] diff --git a/src/guidellm/objects/pydantic.py b/src/guidellm/objects/pydantic.py deleted file mode 100644 index 8365be33..00000000 --- a/src/guidellm/objects/pydantic.py +++ /dev/null @@ -1,60 +0,0 @@ -from typing import Any, Generic, TypeVar - -from loguru import logger -from pydantic import BaseModel, ConfigDict, Field - -__all__ = ["StandardBaseModel", "StatusBreakdown"] - - -class StandardBaseModel(BaseModel): - """ - A base class for Pydantic models throughout GuideLLM enabling standard - configuration and logging. - """ - - model_config = ConfigDict( - extra="ignore", - use_enum_values=True, - validate_assignment=True, - from_attributes=True, - ) - - def __init__(self, /, **data: Any) -> None: - super().__init__(**data) - logger.debug( - "Initialized new instance of {} with data: {}", - self.__class__.__name__, - data, - ) - - -SuccessfulT = TypeVar("SuccessfulT") -ErroredT = TypeVar("ErroredT") -IncompleteT = TypeVar("IncompleteT") -TotalT = TypeVar("TotalT") - - -class StatusBreakdown(BaseModel, Generic[SuccessfulT, ErroredT, IncompleteT, TotalT]): - """ - A base class for Pydantic models that are separated by statuses including - successful, incomplete, and errored. It additionally enables the inclusion - of total, which is intended as the combination of all statuses. - Total may or may not be used depending on if it duplicates information. - """ - - successful: SuccessfulT = Field( - description="The results with a successful status.", - default=None, # type: ignore[assignment] - ) - errored: ErroredT = Field( - description="The results with an errored status.", - default=None, # type: ignore[assignment] - ) - incomplete: IncompleteT = Field( - description="The results with an incomplete status.", - default=None, # type: ignore[assignment] - ) - total: TotalT = Field( - description="The combination of all statuses.", - default=None, # type: ignore[assignment] - ) diff --git a/src/guidellm/objects/statistics.py b/src/guidellm/objects/statistics.py deleted file mode 100644 index 552b5c20..00000000 --- a/src/guidellm/objects/statistics.py +++ /dev/null @@ -1,947 +0,0 @@ -import math -import time as timer -from collections import defaultdict -from typing import Any, Literal, Optional - -import numpy as np -from pydantic import Field, computed_field - -from guidellm.objects.pydantic import StandardBaseModel, StatusBreakdown - -__all__ = [ - "DistributionSummary", - "Percentiles", - "RunningStats", - "StatusDistributionSummary", - "TimeRunningStats", -] - - -class Percentiles(StandardBaseModel): - """ - A pydantic model representing the standard percentiles of a distribution. - """ - - p001: float = Field( - description="The 0.1th percentile of the distribution.", - ) - p01: float = Field( - description="The 1st percentile of the distribution.", - ) - p05: float = Field( - description="The 5th percentile of the distribution.", - ) - p10: float = Field( - description="The 10th percentile of the distribution.", - ) - p25: float = Field( - description="The 25th percentile of the distribution.", - ) - p75: float = Field( - description="The 75th percentile of the distribution.", - ) - p90: float = Field( - description="The 90th percentile of the distribution.", - ) - p95: float = Field( - description="The 95th percentile of the distribution.", - ) - p99: float = Field( - description="The 99th percentile of the distribution.", - ) - p999: float = Field( - description="The 99.9th percentile of the distribution.", - ) - - -class DistributionSummary(StandardBaseModel): - """ - A pydantic model representing a statistical summary for a given - distribution of numerical values. - """ - - mean: float = Field( - description="The mean/average of the distribution.", - ) - median: float = Field( - description="The median of the distribution.", - ) - mode: float = Field( - description="The mode of the distribution.", - ) - variance: float = Field( - description="The variance of the distribution.", - ) - std_dev: float = Field( - description="The standard deviation of the distribution.", - ) - min: float = Field( - description="The minimum value of the distribution.", - ) - max: float = Field( - description="The maximum value of the distribution.", - ) - count: int = Field( - description="The number of values in the distribution.", - ) - total_sum: float = Field( - description="The total sum of the values in the distribution.", - ) - percentiles: Percentiles = Field( - description="The percentiles of the distribution.", - ) - cumulative_distribution_function: Optional[list[tuple[float, float]]] = Field( - description="The cumulative distribution function (CDF) of the distribution.", - default=None, - ) - - @staticmethod - def from_distribution_function( - distribution: list[tuple[float, float]], - include_cdf: bool = False, - ) -> "DistributionSummary": - """ - Create a statistical summary for a given distribution of weighted numerical - values or a probability distribution function (PDF). - 1. If the distribution is a PDF, it is expected to be a list of tuples - where each tuple contains (value, probability). The sum of the - probabilities should be 1. If it is not, it will be normalized. - 2. If the distribution is a values distribution function, it is expected - to be a list of tuples where each tuple contains (value, weight). - The weights are normalized to a probability distribution function. - - :param distribution: A list of tuples representing the distribution. - Each tuple contains (value, weight) or (value, probability). - :param include_cdf: Whether to include the calculated cumulative distribution - function (CDF) in the output DistributionSummary. - :return: An instance of DistributionSummary with calculated values. - """ - values, weights = zip(*distribution) if distribution else ([], []) - values = np.array(values) # type: ignore[assignment] - weights = np.array(weights) # type: ignore[assignment] - - # create the PDF - probabilities = weights / np.sum(weights) # type: ignore[operator] - pdf = np.column_stack((values, probabilities)) - pdf = pdf[np.argsort(pdf[:, 0])] - values = pdf[:, 0] # type: ignore[assignment] - probabilities = pdf[:, 1] - - # calculate the CDF - cumulative_probabilities = np.cumsum(probabilities) - cdf = np.column_stack((values, cumulative_probabilities)) - - # calculate statistics - mean = np.sum(values * probabilities).item() # type: ignore[attr-defined] - median = cdf[np.argmax(cdf[:, 1] >= 0.5), 0].item() if len(cdf) > 0 else 0 # noqa: PLR2004 - mode = values[np.argmax(probabilities)].item() if len(values) > 0 else 0 # type: ignore[call-overload] - variance = np.sum((values - mean) ** 2 * probabilities).item() # type: ignore[attr-defined] - std_dev = math.sqrt(variance) - minimum = values[0].item() if len(values) > 0 else 0 - maximum = values[-1].item() if len(values) > 0 else 0 - count = len(values) - total_sum = np.sum(values).item() # type: ignore[attr-defined] - - return DistributionSummary( - mean=mean, - median=median, - mode=mode, - variance=variance, - std_dev=std_dev, - min=minimum, - max=maximum, - count=count, - total_sum=total_sum, - percentiles=( - Percentiles( - p001=cdf[np.argmax(cdf[:, 1] >= 0.001), 0].item(), # noqa: PLR2004 - p01=cdf[np.argmax(cdf[:, 1] >= 0.01), 0].item(), # noqa: PLR2004 - p05=cdf[np.argmax(cdf[:, 1] >= 0.05), 0].item(), # noqa: PLR2004 - p10=cdf[np.argmax(cdf[:, 1] >= 0.1), 0].item(), # noqa: PLR2004 - p25=cdf[np.argmax(cdf[:, 1] >= 0.25), 0].item(), # noqa: PLR2004 - p75=cdf[np.argmax(cdf[:, 1] >= 0.75), 0].item(), # noqa: PLR2004 - p90=cdf[np.argmax(cdf[:, 1] >= 0.9), 0].item(), # noqa: PLR2004 - p95=cdf[np.argmax(cdf[:, 1] >= 0.95), 0].item(), # noqa: PLR2004 - p99=cdf[np.argmax(cdf[:, 1] >= 0.99), 0].item(), # noqa: PLR2004 - p999=cdf[np.argmax(cdf[:, 1] >= 0.999), 0].item(), # noqa: PLR2004 - ) - if len(cdf) > 0 - else Percentiles( - p001=0, - p01=0, - p05=0, - p10=0, - p25=0, - p75=0, - p90=0, - p95=0, - p99=0, - p999=0, - ) - ), - cumulative_distribution_function=cdf.tolist() if include_cdf else None, - ) - - @staticmethod - def from_values( - values: list[float], - weights: Optional[list[float]] = None, - include_cdf: bool = False, - ) -> "DistributionSummary": - """ - Create a statistical summary for a given distribution of numerical values. - This is a wrapper around from_distribution_function to handle the optional case - of including weights for the values. If weights are not provided, they are - automatically set to 1.0 for each value, so each value is equally weighted. - - :param values: A list of numerical values representing the distribution. - :param weights: A list of weights for each value in the distribution. - If not provided, all values are equally weighted. - :param include_cdf: Whether to include the calculated cumulative distribution - function (CDF) in the output DistributionSummary. - """ - if weights is None: - weights = [1.0] * len(values) - - if len(values) != len(weights): - raise ValueError( - "The length of values and weights must be the same.", - ) - - return DistributionSummary.from_distribution_function( - distribution=list(zip(values, weights)), - include_cdf=include_cdf, - ) - - @staticmethod - def from_request_times( - requests: list[tuple[float, float]], - distribution_type: Literal["concurrency", "rate"], - include_cdf: bool = False, - epsilon: float = 1e-6, - ) -> "DistributionSummary": - """ - Create a statistical summary for a given distribution of request times. - Specifically, this is used to measure concurrency or rate of requests - given an input list containing the start and end time of each request. - This will first convert the request times into a distribution function - and then calculate the statistics with from_distribution_function. - - :param requests: A list of tuples representing the start and end times of - each request. Example: [(start_1, end_1), (start_2, end_2), ...] - :param distribution_type: The type of distribution to calculate. - Either "concurrency" or "rate". - :param include_cdf: Whether to include the calculated cumulative distribution - function (CDF) in the output DistributionSummary. - :param epsilon: The epsilon value for merging close events. - :return: An instance of DistributionSummary with calculated values. - """ - if distribution_type == "concurrency": - # convert to delta changes based on when requests were running - time_deltas: dict[float, int] = defaultdict(int) - for start, end in requests: - time_deltas[start] += 1 - time_deltas[end] -= 1 - - # convert to the events over time measuring concurrency changes - events = [] - active = 0 - - for time, delta in sorted(time_deltas.items()): - active += delta - events.append((time, active)) - elif distribution_type == "rate": - # convert to events for when requests finished - global_start = min(start for start, _ in requests) if requests else 0 - events = [(global_start, 1)] + [(end, 1) for _, end in requests] - else: - raise ValueError( - f"Invalid distribution_type '{distribution_type}'. " - "Must be 'concurrency' or 'rate'." - ) - - # combine any events that are very close together - flattened_events: list[tuple[float, float]] = [] - for time, val in sorted(events): - last_time, last_val = ( - flattened_events[-1] if flattened_events else (None, None) - ) - - if ( - last_time is not None - and last_val is not None - and abs(last_time - time) <= epsilon - ): - flattened_events[-1] = (last_time, last_val + val) - else: - flattened_events.append((time, val)) - - # convert to value distribution function - distribution: dict[float, float] = defaultdict(float) - - for ind in range(len(flattened_events) - 1): - start_time, value = flattened_events[ind] - end_time, _ = flattened_events[ind + 1] - duration = end_time - start_time - - if distribution_type == "concurrency": - # weight the concurrency value by the duration - distribution[value] += duration - elif distribution_type == "rate": - # weight the rate value by the duration - rate = value / duration - distribution[rate] += duration - - distribution_list: list[tuple[float, float]] = sorted(distribution.items()) - - return DistributionSummary.from_distribution_function( - distribution=distribution_list, - include_cdf=include_cdf, - ) - - @staticmethod - def from_iterable_request_times( - requests: list[tuple[float, float]], - first_iter_times: list[float], - iter_counts: list[int], - first_iter_counts: Optional[list[int]] = None, - include_cdf: bool = False, - epsilon: float = 1e-6, - ) -> "DistributionSummary": - """ - Create a statistical summary for a given distribution of request times - for a request with iterable responses between the start and end. - For example, this is used to measure auto regressive requests where - a request is started and at some later point, iterative responses are - received. This will convert the request times and iterable values into - a distribution function and then calculate the statistics with - from_distribution_function. - - :param requests: A list of tuples representing the start and end times of - each request. Example: [(start_1, end_1), (start_2, end_2), ...] - :param first_iter_times: A list of times when the first iteration of - each request was received. Must be the same length as requests. - :param iter_counts: A list of the total number of iterations for each - request that occurred starting at the first iteration and ending - at the request end time. Must be the same length as requests. - :param first_iter_counts: A list of the number of iterations to log - for the first iteration of each request. For example, when calculating - total number of tokens processed, this is set to the prompt tokens number. - If not provided, defaults to 1 for each request. - :param include_cdf: Whether to include the calculated cumulative distribution - function (CDF) in the output DistributionSummary. - :param epsilon: The epsilon value for merging close events. - :return: An instance of DistributionSummary with calculated values. - """ - - if first_iter_counts is None: - first_iter_counts = [1] * len(requests) - - if ( - len(requests) != len(first_iter_times) - or len(requests) != len(iter_counts) - or len(requests) != len(first_iter_counts) - ): - raise ValueError( - "requests, first_iter_times, iter_counts, and first_iter_counts must" - "be the same length." - f"Given {len(requests)}, {len(first_iter_times)}, {len(iter_counts)}, " - f"{len(first_iter_counts)}", - ) - - # first break up the requests into individual iterable events - events = defaultdict(int) - global_start = min(start for start, _ in requests) if requests else 0 - global_end = max(end for _, end in requests) if requests else 0 - events[global_start] = 0 - events[global_end] = 0 - - for (_, end), first_iter, first_iter_count, total_count in zip( - requests, first_iter_times, first_iter_counts, iter_counts - ): - events[first_iter] += first_iter_count - - if total_count > 1: - iter_latency = (end - first_iter) / (total_count - 1) - for ind in range(1, total_count): - events[first_iter + ind * iter_latency] += 1 - - # combine any events that are very close together - flattened_events: list[tuple[float, int]] = [] - - for time, count in sorted(events.items()): - last_time, last_count = ( - flattened_events[-1] if flattened_events else (None, None) - ) - - if ( - last_time is not None - and last_count is not None - and abs(last_time - time) <= epsilon - ): - flattened_events[-1] = (last_time, last_count + count) - else: - flattened_events.append((time, count)) - - # convert to value distribution function - distribution: dict[float, float] = defaultdict(float) - - for ind in range(len(flattened_events) - 1): - start_time, count = flattened_events[ind] - end_time, _ = flattened_events[ind + 1] - duration = end_time - start_time - rate = count / duration - distribution[rate] += duration - - distribution_list = sorted(distribution.items()) - - return DistributionSummary.from_distribution_function( - distribution=distribution_list, - include_cdf=include_cdf, - ) - - -class StatusDistributionSummary( - StatusBreakdown[ - DistributionSummary, - DistributionSummary, - DistributionSummary, - DistributionSummary, - ] -): - """ - A pydantic model representing a statistical summary for a given - distribution of numerical values grouped by status. - Specifically used to represent the total, successful, incomplete, - and errored values for a benchmark or other statistical summary. - """ - - @staticmethod - def from_values( - value_types: list[Literal["successful", "incomplete", "error"]], - values: list[float], - weights: Optional[list[float]] = None, - include_cdf: bool = False, - ) -> "StatusDistributionSummary": - """ - Create a statistical summary by status for a given distribution of numerical - values. This is used to measure the distribution of values for different - statuses (e.g., successful, incomplete, error) and calculate the statistics - for each status. Weights are optional to weight the probability distribution - for each value by. If not provided, all values are equally weighted. - - :param value_types: A list of status types for each value in the distribution. - Must be one of 'successful', 'incomplete', or 'error'. - :param values: A list of numerical values representing the distribution. - Must be the same length as value_types. - :param weights: A list of weights for each value in the distribution. - If not provided, all values are equally weighted (set to 1). - Must be the same length as value_types. - :param include_cdf: Whether to include the calculated cumulative distribution - function (CDF) in the output StatusDistributionSummary. - :return: An instance of StatusDistributionSummary with calculated values. - """ - if any( - type_ not in {"successful", "incomplete", "error"} for type_ in value_types - ): - raise ValueError( - "value_types must be one of 'successful', 'incomplete', or 'error'. " - f"Got {value_types} instead.", - ) - - if weights is None: - weights = [1.0] * len(values) - - if len(value_types) != len(values) or len(value_types) != len(weights): - raise ValueError( - "The length of value_types, values, and weights must be the same.", - ) - - _, successful_values, successful_weights = ( - zip(*successful) - if ( - successful := list( - filter( - lambda val: val[0] == "successful", - zip(value_types, values, weights), - ) - ) - ) - else ([], [], []) - ) - _, incomplete_values, incomplete_weights = ( - zip(*incomplete) - if ( - incomplete := list( - filter( - lambda val: val[0] == "incomplete", - zip(value_types, values, weights), - ) - ) - ) - else ([], [], []) - ) - _, errored_values, errored_weights = ( - zip(*errored) - if ( - errored := list( - filter( - lambda val: val[0] == "error", - zip(value_types, values, weights), - ) - ) - ) - else ([], [], []) - ) - - return StatusDistributionSummary( - total=DistributionSummary.from_values( - values, - weights, - include_cdf=include_cdf, - ), - successful=DistributionSummary.from_values( - successful_values, # type: ignore[arg-type] - successful_weights, # type: ignore[arg-type] - include_cdf=include_cdf, - ), - incomplete=DistributionSummary.from_values( - incomplete_values, # type: ignore[arg-type] - incomplete_weights, # type: ignore[arg-type] - include_cdf=include_cdf, - ), - errored=DistributionSummary.from_values( - errored_values, # type: ignore[arg-type] - errored_weights, # type: ignore[arg-type] - include_cdf=include_cdf, - ), - ) - - @staticmethod - def from_request_times( - request_types: list[Literal["successful", "incomplete", "error"]], - requests: list[tuple[float, float]], - distribution_type: Literal["concurrency", "rate"], - include_cdf: bool = False, - epsilon: float = 1e-6, - ) -> "StatusDistributionSummary": - """ - Create a statistical summary by status for given distribution of request times. - This is used to measure the distribution of request times for different statuses - (e.g., successful, incomplete, error) for concurrency and rates. - This will call into DistributionSummary.from_request_times to calculate - the statistics for each status. - - :param request_types: List of status types for each request in the distribution. - Must be one of 'successful', 'incomplete', or 'error'. - :param requests: A list of tuples representing the start and end times of - each request. Example: [(start_1, end_1), (start_2, end_2), ...]. - Must be the same length as request_types. - :param distribution_type: The type of distribution to calculate. - Either "concurrency" or "rate". - :param include_cdf: Whether to include the calculated cumulative distribution - function (CDF) in the output StatusDistributionSummary. - :param epsilon: The epsilon value for merging close events. - :return: An instance of StatusDistributionSummary with calculated values. - """ - if distribution_type not in {"concurrency", "rate"}: - raise ValueError( - f"Invalid distribution_type '{distribution_type}'. " - "Must be 'concurrency' or 'rate'." - ) - - if any( - type_ not in {"successful", "incomplete", "error"} - for type_ in request_types - ): - raise ValueError( - "request_types must be one of 'successful', 'incomplete', or 'error'. " - f"Got {request_types} instead.", - ) - - if len(request_types) != len(requests): - raise ValueError( - "The length of request_types and requests must be the same. " - f"Got {len(request_types)} and {len(requests)} instead.", - ) - - _, successful_requests = ( - zip(*successful) - if ( - successful := list( - filter( - lambda val: val[0] == "successful", - zip(request_types, requests), - ) - ) - ) - else ([], []) - ) - _, incomplete_requests = ( - zip(*incomplete) - if ( - incomplete := list( - filter( - lambda val: val[0] == "incomplete", - zip(request_types, requests), - ) - ) - ) - else ([], []) - ) - _, errored_requests = ( - zip(*errored) - if ( - errored := list( - filter( - lambda val: val[0] == "error", - zip(request_types, requests), - ) - ) - ) - else ([], []) - ) - - return StatusDistributionSummary( - total=DistributionSummary.from_request_times( - requests, - distribution_type=distribution_type, - include_cdf=include_cdf, - epsilon=epsilon, - ), - successful=DistributionSummary.from_request_times( - successful_requests, # type: ignore[arg-type] - distribution_type=distribution_type, - include_cdf=include_cdf, - epsilon=epsilon, - ), - incomplete=DistributionSummary.from_request_times( - incomplete_requests, # type: ignore[arg-type] - distribution_type=distribution_type, - include_cdf=include_cdf, - epsilon=epsilon, - ), - errored=DistributionSummary.from_request_times( - errored_requests, # type: ignore[arg-type] - distribution_type=distribution_type, - include_cdf=include_cdf, - epsilon=epsilon, - ), - ) - - @staticmethod - def from_iterable_request_times( - request_types: list[Literal["successful", "incomplete", "error"]], - requests: list[tuple[float, float]], - first_iter_times: list[float], - iter_counts: Optional[list[int]] = None, - first_iter_counts: Optional[list[int]] = None, - include_cdf: bool = False, - epsilon: float = 1e-6, - ) -> "StatusDistributionSummary": - """ - Create a statistical summary by status for given distribution of request times - for a request with iterable responses between the start and end. - For example, this is used to measure auto regressive requests where - a request is started and at some later point, iterative responses are - received. This will call into DistributionSummary.from_iterable_request_times - to calculate the statistics for each status. - - :param request_types: List of status types for each request in the distribution. - Must be one of 'successful', 'incomplete', or 'error'. - :param requests: A list of tuples representing the start and end times of - each request. Example: [(start_1, end_1), (start_2, end_2), ...]. - Must be the same length as request_types. - :param first_iter_times: A list of times when the first iteration of - each request was received. Must be the same length as requests. - :param iter_counts: A list of the total number of iterations for each - request that occurred starting at the first iteration and ending - at the request end time. Must be the same length as requests. - If not provided, defaults to 1 for each request. - :param first_iter_counts: A list of the number of iterations to log - for the first iteration of each request. For example, when calculating - total number of tokens processed, this is set to the prompt tokens number. - If not provided, defaults to 1 for each request. - :param include_cdf: Whether to include the calculated cumulative distribution - function (CDF) in the output StatusDistributionSummary. - :param epsilon: The epsilon value for merging close events. - :return: An instance of StatusDistributionSummary with calculated values. - """ - if any( - type_ not in {"successful", "incomplete", "error"} - for type_ in request_types - ): - raise ValueError( - "request_types must be one of 'successful', 'incomplete', or 'error'. " - f"Got {request_types} instead.", - ) - - if iter_counts is None: - iter_counts = [1] * len(requests) - - if first_iter_counts is None: - first_iter_counts = [1] * len(requests) - - if ( - len(request_types) != len(requests) - or len(requests) != len(first_iter_times) - or len(requests) != len(iter_counts) - or len(requests) != len(first_iter_counts) - ): - raise ValueError( - "request_types, requests, first_iter_times, iter_counts, and " - "first_iter_counts must be the same length." - f"Given {len(request_types)}, {len(requests)}, " - f"{len(first_iter_times)}, {len(iter_counts)}, " - f"{len(first_iter_counts)}", - ) - - ( - _, - successful_requests, - successful_first_iter_times, - successful_iter_counts, - successful_first_iter_counts, - ) = ( - zip(*successful) - if ( - successful := list( - filter( - lambda val: val[0] == "successful", - zip( - request_types, - requests, - first_iter_times, - iter_counts, - first_iter_counts, - ), - ) - ) - ) - else ([], [], [], [], []) - ) - ( - _, - incomplete_requests, - incomplete_first_iter_times, - incomplete_iter_counts, - incomplete_first_iter_counts, - ) = ( - zip(*incomplete) - if ( - incomplete := list( - filter( - lambda val: val[0] == "incomplete", - zip( - request_types, - requests, - first_iter_times, - iter_counts, - first_iter_counts, - ), - ) - ) - ) - else ([], [], [], [], []) - ) - ( - _, - errored_requests, - errored_first_iter_times, - errored_iter_counts, - errored_first_iter_counts, - ) = ( - zip(*errored) - if ( - errored := list( - filter( - lambda val: val[0] == "error", - zip( - request_types, - requests, - first_iter_times, - iter_counts, - first_iter_counts, - ), - ) - ) - ) - else ([], [], [], [], []) - ) - - return StatusDistributionSummary( - total=DistributionSummary.from_iterable_request_times( - requests, - first_iter_times, - iter_counts, - first_iter_counts, - include_cdf=include_cdf, - epsilon=epsilon, - ), - successful=DistributionSummary.from_iterable_request_times( - successful_requests, # type: ignore[arg-type] - successful_first_iter_times, # type: ignore[arg-type] - successful_iter_counts, # type: ignore[arg-type] - successful_first_iter_counts, # type: ignore[arg-type] - include_cdf=include_cdf, - epsilon=epsilon, - ), - incomplete=DistributionSummary.from_iterable_request_times( - incomplete_requests, # type: ignore[arg-type] - incomplete_first_iter_times, # type: ignore[arg-type] - incomplete_iter_counts, # type: ignore[arg-type] - incomplete_first_iter_counts, # type: ignore[arg-type] - include_cdf=include_cdf, - epsilon=epsilon, - ), - errored=DistributionSummary.from_iterable_request_times( - errored_requests, # type: ignore[arg-type] - errored_first_iter_times, # type: ignore[arg-type] - errored_iter_counts, # type: ignore[arg-type] - errored_first_iter_counts, # type: ignore[arg-type] - include_cdf=include_cdf, - epsilon=epsilon, - ), - ) - - -class RunningStats(StandardBaseModel): - """ - Create a running statistics object to track the mean, rate, and other - statistics of a stream of values. - 1. The start time is set to the time the object is created. - 2. The count is set to 0. - 3. The total is set to 0. - 4. The last value is set to 0. - 5. The mean is calculated as the total / count. - """ - - start_time: float = Field( - default_factory=timer.time, - description=( - "The time the running statistics object was created. " - "This is used to calculate the rate of the statistics." - ), - ) - count: int = Field( - default=0, - description="The number of values added to the running statistics.", - ) - total: float = Field( - default=0.0, - description="The total sum of the values added to the running statistics.", - ) - last: float = Field( - default=0.0, - description="The last value added to the running statistics.", - ) - - @computed_field # type: ignore[misc] - @property - def mean(self) -> float: - """ - :return: The mean of the running statistics (total / count). - If count is 0, return 0.0. - """ - if self.count == 0: - return 0.0 - return self.total / self.count - - @computed_field # type: ignore[misc] - @property - def rate(self) -> float: - """ - :return: The rate of the running statistics - (total / (time.time() - start_time)). - If count is 0, return 0.0. - """ - if self.count == 0: - return 0.0 - return self.total / (timer.time() - self.start_time) - - def __add__(self, value: Any) -> float: - """ - Enable the use of the + operator to add a value to the running statistics. - - :param value: The value to add to the running statistics. - :return: The mean of the running statistics. - """ - if not isinstance(value, (int, float)): - raise ValueError( - f"Value must be an int or float, got {type(value)} instead.", - ) - - self.update(value) - - return self.mean - - def __iadd__(self, value: Any) -> "RunningStats": - """ - Enable the use of the += operator to add a value to the running statistics. - - :param value: The value to add to the running statistics. - :return: The running statistics object. - """ - if not isinstance(value, (int, float)): - raise ValueError( - f"Value must be an int or float, got {type(value)} instead.", - ) - - self.update(value) - - return self - - def update(self, value: float, count: int = 1) -> None: - """ - Update the running statistics with a new value. - - :param value: The new value to add to the running statistics. - :param count: The number of times to 'count' for the value. - If not provided, defaults to 1. - """ - self.count += count - self.total += value - self.last = value - - -class TimeRunningStats(RunningStats): - """ - Create a running statistics object to track the mean, rate, and other - statistics of a stream of time values. This is used to track time values - in milliseconds and seconds. - - Adds time specific computed_fields such as measurements in milliseconds and seconds. - """ - - @computed_field # type: ignore[misc] - @property - def total_ms(self) -> float: - """ - :return: The total time multiplied by 1000.0 to convert to milliseconds. - """ - return self.total * 1000.0 - - @computed_field # type: ignore[misc] - @property - def last_ms(self) -> float: - """ - :return: The last time multiplied by 1000.0 to convert to milliseconds. - """ - return self.last * 1000.0 - - @computed_field # type: ignore[misc] - @property - def mean_ms(self) -> float: - """ - :return: The mean time multiplied by 1000.0 to convert to milliseconds. - """ - return self.mean * 1000.0 - - @computed_field # type: ignore[misc] - @property - def rate_ms(self) -> float: - """ - :return: The rate of the running statistics multiplied by 1000.0 - to convert to milliseconds. - """ - return self.rate * 1000.0 diff --git a/src/guidellm/request/__init__.py b/src/guidellm/request/__init__.py deleted file mode 100644 index db3059cc..00000000 --- a/src/guidellm/request/__init__.py +++ /dev/null @@ -1,15 +0,0 @@ -from .loader import ( - GenerativeRequestLoader, - GenerativeRequestLoaderDescription, - RequestLoader, - RequestLoaderDescription, -) -from .request import GenerationRequest - -__all__ = [ - "GenerationRequest", - "GenerativeRequestLoader", - "GenerativeRequestLoaderDescription", - "RequestLoader", - "RequestLoaderDescription", -] diff --git a/src/guidellm/request/loader.py b/src/guidellm/request/loader.py deleted file mode 100644 index 50ab3cca..00000000 --- a/src/guidellm/request/loader.py +++ /dev/null @@ -1,284 +0,0 @@ -from abc import abstractmethod -from collections.abc import Iterable, Iterator -from pathlib import Path -from typing import ( - Any, - Literal, - Optional, - Union, -) - -from datasets import Dataset, DatasetDict, IterableDataset, IterableDatasetDict -from transformers import PreTrainedTokenizerBase # type: ignore[import] - -from guidellm.config import settings -from guidellm.dataset import ColumnInputTypes, load_dataset -from guidellm.objects import StandardBaseModel -from guidellm.request.request import GenerationRequest - -__all__ = [ - "GenerativeRequestLoader", - "GenerativeRequestLoaderDescription", - "RequestLoader", - "RequestLoaderDescription", -] - - -class RequestLoaderDescription(StandardBaseModel): - type_: Literal["request_loader"] = "request_loader" - - -class RequestLoader(Iterable): - @abstractmethod - def __iter__(self): ... - - @abstractmethod - def __len__(self): ... - - @property - @abstractmethod - def description(self) -> RequestLoaderDescription: ... - - -class GenerativeRequestLoaderDescription(RequestLoaderDescription): - type_: Literal["generative_request_loader"] = "generative_request_loader" # type: ignore[assignment] - data: str - data_args: Optional[dict[str, Any]] - processor: str - processor_args: Optional[dict[str, Any]] - - -class GenerativeRequestLoader(RequestLoader): - DEFAULT_PROMPT_COLUMNS = [ - "prompt", - "prompts", - "instruction", - "instructions", - "question", - "questions", - "input", - "inputs", - "context", - "content", - "conversation", - "conversations", - "turn", - "turns", - "text", - ] - - def __init__( - self, - data: Union[ - str, - Path, - Iterable[Union[str, dict[str, Any]]], - Dataset, - DatasetDict, - IterableDataset, - IterableDatasetDict, - ], - data_args: Optional[dict[str, Any]], - processor: Optional[Union[str, Path, PreTrainedTokenizerBase]], - processor_args: Optional[dict[str, Any]], - shuffle: bool = True, - iter_type: Literal["finite", "infinite"] = "finite", - random_seed: int = 42, - ): - self.data = data - self.data_args = data_args - dataset, args_column_mappings = load_dataset( - data, - data_args, - processor, - processor_args, - random_seed, - ) - self.dataset = dataset - self.processor = processor - self.processor_args = processor_args - self.shuffle = shuffle - self.iter_type = iter_type - self.random_seed = random_seed - - self.column_mappings = self._create_column_mappings(args_column_mappings) - self.preserve_iter_state = iter_type == "infinite" # ensure no caching requests - self._preserved_iter = None - - def __iter__(self) -> Iterator[GenerationRequest]: - scope_create_count = 0 - - while (dataset_iter := self._get_dataset_iter(scope_create_count)) is not None: - scope_create_count += 1 - - for item in dataset_iter: - yield self._create_request(item) - - self._preserved_iter = None - - def __len__(self) -> int: - if self.iter_type == "finite": - return self.num_unique_items() - - raise ValueError(f"Unable to determine length of dataset: {self.data}") - - @property - def description(self) -> GenerativeRequestLoaderDescription: - return GenerativeRequestLoaderDescription( - data=str(self.data), - data_args=self.data_args, - processor=str(self.processor), - processor_args=self.processor_args, - ) - - def num_unique_items(self, raise_err: bool = True) -> int: - try: - return len(self.dataset) - except Exception: # noqa: BLE001, S110 - pass - - dataset_size = self.dataset.info.dataset_size - if dataset_size is not None: - return dataset_size - - if raise_err: - raise ValueError("Unable to determine number of items in the dataset") - - return -1 - - def _create_column_mappings( - self, - args_column_mappings: dict[ColumnInputTypes, str], - ) -> dict[ColumnInputTypes, str]: - column_mappings: dict[ColumnInputTypes, str] = {} - - if "text_column" in args_column_mappings: - column_mappings["prompt_column"] = args_column_mappings["text_column"] - else: - column_mappings["prompt_column"] = self._extract_text_column() - - if "prompt_tokens_count_column" in args_column_mappings: - column_mappings["prompt_tokens_count_column"] = args_column_mappings[ - "prompt_tokens_count_column" - ] - elif prompt_tokens_count_column := self._extract_prompt_tokens_count_column(): - column_mappings["prompt_tokens_count_column"] = prompt_tokens_count_column - - if "output_tokens_count_column" in args_column_mappings: - column_mappings["output_tokens_count_column"] = args_column_mappings[ - "output_tokens_count_column" - ] - elif output_tokens_count_column := self._extract_output_tokens_count_column(): - column_mappings["output_tokens_count_column"] = output_tokens_count_column - - return column_mappings - - def _extract_text_column(self) -> str: - column_names = self._dataset_columns( - err_msg=( - "Unable to determine text column from dataset and it is required. " - "To specify the text column, set the 'text_column' key in the " - "'data_args' dictionary." - ) - ) - - if not column_names: - raise ValueError( - "Unable to determine text column from dataset and it is required. " - "To specify the text column, set the 'text_column' key in the " - "'data_args' dictionary." - ) - - if len(column_names) == 1: - return column_names[0] - - for def_column in self.DEFAULT_PROMPT_COLUMNS: - if def_column in column_names: - return def_column - - raise ValueError( - f"Unable to determine text column from dataset columns: {column_names}. " - "To specify the text column, set the 'text_column' key in the " - "'data_args' dictionary." - ) - - def _extract_prompt_tokens_count_column(self) -> Optional[str]: - column_names = self._dataset_columns() - - if column_names and "prompt_tokens_count" in column_names: - return "prompt_tokens_count" - - if column_names and "prompt_tokens" in column_names: - return "prompt_tokens" - - return None - - def _extract_output_tokens_count_column(self) -> Optional[str]: - column_names = self._dataset_columns() - - if column_names and "output_tokens_count" in column_names: - return "output_tokens_count" - - if column_names and "output_tokens" in column_names: - return "output_tokens" - - return None - - def _dataset_columns(self, err_msg: Optional[str] = None) -> Optional[list[str]]: - try: - column_names = self.dataset.column_names - - if not column_names and err_msg: - raise ValueError(f"No column names found in dataset: {self.data}") - except Exception as err: - if err_msg: - raise ValueError(err_msg) from err - - column_names = None - - return column_names - - def _get_dataset_iter( - self, scope_create_count: int - ) -> Optional[Iterator[dict[str, Any]]]: - if scope_create_count > 0 and self.iter_type != "infinite": - return None - - if self.preserve_iter_state and self._preserved_iter is not None: - return self._preserved_iter - - dataset = ( - self.dataset - if not self.shuffle - else self.dataset.shuffle(seed=self.random_seed) - ) - - dataset_iter = iter(dataset) - - if self.preserve_iter_state: - self._preserved_iter = dataset_iter - - return dataset_iter - - def _create_request(self, item: dict[str, Any]) -> GenerationRequest: - prompt_tokens = ( - item[self.column_mappings["prompt_tokens_count_column"]] - if "prompt_tokens_count_column" in self.column_mappings - else None - ) - output_tokens = ( - item[self.column_mappings["output_tokens_count_column"]] - if "output_tokens_count_column" in self.column_mappings - else None - ) - - return GenerationRequest( - request_type=settings.preferred_route, - content=item[self.column_mappings["prompt_column"]], - stats=( - {"prompt_tokens": prompt_tokens} if prompt_tokens is not None else {} - ), - constraints=( - {"output_tokens": output_tokens} if output_tokens is not None else {} - ), - ) diff --git a/src/guidellm/request/request.py b/src/guidellm/request/request.py deleted file mode 100644 index 81c8cabd..00000000 --- a/src/guidellm/request/request.py +++ /dev/null @@ -1,79 +0,0 @@ -import uuid -from typing import Any, Literal, Optional - -from pydantic import Field - -from guidellm.objects.pydantic import StandardBaseModel - -__all__ = ["GenerationRequest"] - - -class GenerationRequest(StandardBaseModel): - """ - A class representing a request for generation. - This class is used to encapsulate the details of a generation request, - including the request ID, type, content, parameters, statistics, and constraints. - It is designed to be used with the BackendRequestsWorker class to handle - the generation process. - - :param request_id: The unique identifier for the request. - :param request_type: The type of request (e.g., text, chat). - :param content: The content for the request to send to the backend. - If request_type is 'text', this should be a string or list of strings - which will be resolved by backend.text_completions. - If request_type is 'chat', this should be a string, - a list of (str, Dict[str, Union[str, Dict[str, str]], Path, Image]), - or Any raw content which will be resolved by backend.chat_completions. - If raw content, raw_content=True must be passed in the params. - :param params: Additional parameters for the request passed in as kwargs. - For an http backend, these are passed into the body of the request. - :param stats: Statistics for the request, such as the number of prompt tokens. - Used for tracking and reporting purposes. - :param constraints: Constraints for the request, such as the maximum number - of output tokens. Used for controlling the behavior of the backend. - """ - - request_id: Optional[str] = Field( - default_factory=lambda: str(uuid.uuid4()), - description="The unique identifier for the request.", - ) - request_type: Literal["text_completions", "chat_completions"] = Field( - default="text_completions", - description=( - "The type of request (e.g., text, chat). " - "If request_type='text_completions', resolved by backend.text_completions. " - "If request_typ='chat_completions', resolved by backend.chat_completions." - ), - ) - content: Any = Field( - description=( - "The content for the request to send to the backend. " - "If request_type is 'text', this should be a string or list of strings " - "which will be resolved by backend.text_completions. " - "If request_type is 'chat', this should be a string, " - "a list of (str, Dict[str, Union[str, Dict[str, str]], Path, Image]), " - "or Any raw content which will be resolved by backend.chat_completions. " - "If raw content, raw_content=True must be passed in the params." - ) - ) - params: dict[str, Any] = Field( - default_factory=dict, - description=( - "Additional parameters for the request that will be passed in as kwargs. " - "For an http backend, these are passed into the body of the request. " - ), - ) - stats: dict[Literal["prompt_tokens"], int] = Field( - default_factory=dict, - description=( - "Statistics for the request, such as the number of prompt tokens. " - "Used for tracking and reporting purposes." - ), - ) - constraints: dict[Literal["output_tokens"], int] = Field( - default_factory=dict, - description=( - "Constraints for the request, such as the maximum number of output tokens. " - "Used for controlling the behavior of the backend." - ), - ) diff --git a/src/guidellm/scheduler/__init__.py b/src/guidellm/scheduler/__init__.py deleted file mode 100644 index 37bf1fd5..00000000 --- a/src/guidellm/scheduler/__init__.py +++ /dev/null @@ -1,52 +0,0 @@ -from .result import ( - SchedulerRequestInfo, - SchedulerRequestResult, - SchedulerResult, - SchedulerRunInfo, -) -from .scheduler import Scheduler -from .strategy import ( - AsyncConstantStrategy, - AsyncPoissonStrategy, - ConcurrentStrategy, - SchedulingStrategy, - StrategyType, - SynchronousStrategy, - ThroughputStrategy, - strategy_display_str, -) -from .types import RequestT, ResponseT -from .worker import ( - GenerativeRequestsWorker, - GenerativeRequestsWorkerDescription, - RequestsWorker, - ResolveStatus, - WorkerDescription, - WorkerProcessRequest, - WorkerProcessResult, -) - -__all__ = [ - "AsyncConstantStrategy", - "AsyncPoissonStrategy", - "ConcurrentStrategy", - "GenerativeRequestsWorker", - "GenerativeRequestsWorkerDescription", - "RequestT", - "RequestsWorker", - "ResolveStatus", - "ResponseT", - "Scheduler", - "SchedulerRequestInfo", - "SchedulerRequestResult", - "SchedulerResult", - "SchedulerRunInfo", - "SchedulingStrategy", - "StrategyType", - "SynchronousStrategy", - "ThroughputStrategy", - "WorkerDescription", - "WorkerProcessRequest", - "WorkerProcessResult", - "strategy_display_str", -] diff --git a/src/guidellm/scheduler/result.py b/src/guidellm/scheduler/result.py deleted file mode 100644 index 0f12687f..00000000 --- a/src/guidellm/scheduler/result.py +++ /dev/null @@ -1,137 +0,0 @@ -from typing import ( - Generic, - Literal, - Optional, -) - -from guidellm.objects import StandardBaseModel -from guidellm.scheduler.strategy import SchedulingStrategy -from guidellm.scheduler.types import RequestT, ResponseT - -__all__ = [ - "SchedulerRequestInfo", - "SchedulerRequestResult", - "SchedulerResult", - "SchedulerRunInfo", -] - - -class SchedulerRunInfo(StandardBaseModel): - """ - Information about the current run of the scheduler. - This class holds metadata about the scheduling run, - including the start and end times, the number of processes, - and the scheduling strategy used. - It also tracks the number of requests created, queued, pending, - and completed during the run. - - :param start_time: The start time of the scheduling run. - :param end_time: The end time of the scheduling run; - if None, then this will be math.inf. - :param end_number: The maximum number of requests to be processed; - if None, then this will be math.inf. - :param processes: The number of processes used in the scheduling run. - :param strategy: The scheduling strategy used in the run. - This should be an instance of SchedulingStrategy. - :param created_requests: The number of requests created during the run. - :param queued_requests: The number of requests queued during the run. - :param scheduled_requests: The number of requests scheduled during the run. - (requests pending being sent to the worker but recieved by a process) - :param processing_requests: The number of requests actively being run. - :param completed_requests: The number of requests completed during the run. - """ - - start_time: float - end_time: float - end_number: float - processes: int - strategy: SchedulingStrategy - - created_requests: int = 0 - queued_requests: int = 0 - scheduled_requests: int = 0 - processing_requests: int = 0 - completed_requests: int = 0 - - -class SchedulerRequestInfo(StandardBaseModel): - """ - Information about a specific request run through the scheduler. - This class holds metadata about the request, including - the targeted start time, queued time, start time, end time, - and the process ID that handled the request. - - :param targeted_start_time: The targeted start time for the request (time.time()). - :param queued_time: The time the request was queued (time.time()). - :param scheduled_time: The time the request was scheduled (time.time()) - (any sleep time before the request was sent to the worker). - :param worker_start: The time the worker started processing request (time.time()). - :param worker_end: The time the worker finished processing request. (time.time()). - :param process_id: The ID of the underlying process that handled the request. - """ - - requested: bool = False - completed: bool = False - errored: bool = False - canceled: bool = False - - targeted_start_time: float = -1 - queued_time: float = -1 - dequeued_time: float = -1 - scheduled_time: float = -1 - worker_start: float = -1 - request_start: float = -1 - request_end: float = -1 - worker_end: float = -1 - process_id: int = -1 - - -class SchedulerResult(StandardBaseModel): - """ - The yielded, iterative result for a scheduler run. - These are triggered on the start and end of the run, - as well as on the start and end of each request. - Depending on the type, it will hold the request and response - along with information and statistics about the request and general run. - - :param type_: The type of the result, which can be one of: - - "run_start": Indicates the start of the run. - - "run_complete": Indicates the completion of the run (teardown happens after). - - "request_start": Indicates the start of a request. - - "request_complete": Indicates the completion of a request. - :param request: The request that was processed. - :param response: The response from the worker for the request. - :param request_info: Information about the request, including - the targeted start time, queued time, start time, end time, - and the process ID that handled the request. - :param run_info: Information about the current run of the scheduler, - including the start and end times, the number of processes, - and the scheduling strategy used. - It also tracks the number of requests created, queued, pending, - and completed during the run. - """ - - pydantic_type: Literal["scheduler_result"] = "scheduler_result" - type_: Literal[ - "run_start", - "run_complete", - "request_scheduled", - "request_start", - "request_complete", - ] - run_info: SchedulerRunInfo - - -class SchedulerRequestResult( - SchedulerResult, - Generic[RequestT, ResponseT], -): - pydantic_type: Literal["scheduler_request_result"] = "scheduler_request_result" # type: ignore[assignment] - type_: Literal[ - "request_scheduled", - "request_start", - "request_complete", - ] - request: RequestT - request_info: SchedulerRequestInfo - response: Optional[ResponseT] = None diff --git a/src/guidellm/scheduler/scheduler.py b/src/guidellm/scheduler/scheduler.py deleted file mode 100644 index 06203827..00000000 --- a/src/guidellm/scheduler/scheduler.py +++ /dev/null @@ -1,382 +0,0 @@ -import asyncio -import math -import multiprocessing -import multiprocessing.queues -import time -from collections.abc import AsyncGenerator, Iterable, Iterator -from concurrent.futures import ProcessPoolExecutor -from typing import ( - Any, - Generic, - Optional, - Union, -) - -from loguru import logger - -from guidellm.config import settings -from guidellm.scheduler.result import ( - SchedulerRequestResult, - SchedulerResult, - SchedulerRunInfo, -) -from guidellm.scheduler.strategy import SchedulingStrategy -from guidellm.scheduler.types import RequestT, ResponseT -from guidellm.scheduler.worker import ( - RequestsWorker, - WorkerProcessRequest, - WorkerProcessResult, -) - -__all__ = ["Scheduler"] - - -class Scheduler(Generic[RequestT, ResponseT]): - """ - A class that handles the scheduling of requests to a worker. - This class is responsible for managing the lifecycle of the requests, - including their creation, queuing, and processing. - It uses a multiprocessing approach to handle requests concurrently - and efficiently, based on the specified scheduling strategy. - The Scheduler class is designed to work with a RequestsWorker, - which is an abstract base class that defines the interface for a worker - that can resolve requests asynchronously or synchronously. - The Scheduler class also supports different scheduling strategies, - including synchronous, throughput, and concurrent strategies. - - :param worker: The worker that will process the requests. - This should be an instance of RequestsWorker. - :param request_loader: An iterable that generates requests. - This can be a list, generator, or any other iterable. - The requests will be processed by the worker. - """ - - def __init__( - self, - worker: RequestsWorker[RequestT, ResponseT], - request_loader: Iterable[RequestT], - ): - if not isinstance(worker, RequestsWorker): - raise ValueError(f"Invalid worker: {worker}") - - if not isinstance(request_loader, Iterable): - raise ValueError(f"Invalid request_loader: {request_loader}") - - self.worker = worker - self.request_loader = request_loader - - async def run( - self, - scheduling_strategy: SchedulingStrategy, - max_number: Optional[int] = None, - max_duration: Optional[float] = None, - ) -> AsyncGenerator[ - Union[SchedulerResult, SchedulerRequestResult[RequestT, ResponseT]], None - ]: - """ - The main method that runs the scheduler. - This method is a generator that yields SchedulerResult objects - at the start and end of the run, as well as at the start and end - of each request. - It uses multiprocessing to handle requests concurrently - and efficiently, based on the specified scheduling strategy. - The method also handles the lifecycle of the requests, - including their creation, queuing, and processing. - The method is designed to be used as an asynchronous generator, - allowing it to be used with asyncio and other asynchronous frameworks. - - :param scheduling_strategy: The scheduling strategy to use. - Specifies the times at which requests will be sent as well how many - worker processes are used and if requests are scheduled sync or async. - This can be one of the following: - - "synchronous": Requests are sent synchronously. - - "throughput": Requests are sent at the maximum rate possible. - - An instance of SchedulingStrategy. - :param max_number: The maximum number of requests to process. - If None, then no limit is set and either the iterator must be exhaustible - or the max_duration must be set. - :param max_duration: The maximum duration for the scheduling run. - If None, then no limit is set and either the iterator must be exhaustible - or the max_number must be set. - :return: An asynchronous generator that yields SchedulerResult objects. - Each SchedulerResult object contains information about the request, - the response, and the run information. - """ - if scheduling_strategy is None or not isinstance( - scheduling_strategy, SchedulingStrategy - ): - raise ValueError(f"Invalid scheduling strategy: {scheduling_strategy}") - - if max_number is not None and max_number < 1: - raise ValueError(f"Invalid max_number: {max_number}") - - if max_duration is not None and max_duration < 0: - raise ValueError(f"Invalid max_duration: {max_duration}") - - with ( - multiprocessing.Manager() as manager, - ProcessPoolExecutor( - max_workers=scheduling_strategy.processes_limit - ) as executor, - ): - requests_iter: Optional[Iterator[Any]] = None - futures, requests_queue, responses_queue = await self._start_processes( - manager, executor, scheduling_strategy - ) - run_info, requests_iter, times_iter = self._run_setup( - futures, scheduling_strategy, max_number, max_duration - ) - yield SchedulerResult( - type_="run_start", - run_info=run_info, - ) - - try: - while True: - # check errors and raise them - for future in futures: - if future.done() and (err := future.exception()) is not None: - raise err - - if ( - requests_iter is None - and run_info.completed_requests >= run_info.created_requests - ): - # we've exhausted all requests we've wanted to run - # and yielded all responses - break - - requests_iter = self._add_requests( - requests_iter, - times_iter, - requests_queue, - run_info, - ) - await asyncio.sleep(0) # enable requests to start - - iter_result = self._check_result_ready( - responses_queue, - run_info, - ) - if iter_result is not None: - yield iter_result - - # yield control to the event loop - await asyncio.sleep(settings.default_async_loop_sleep) - except Exception as err: - raise RuntimeError(f"Scheduler run failed: {err}") from err - - yield SchedulerResult( - type_="run_complete", - run_info=run_info, - ) - - await self._stop_processes(futures, requests_queue) - - async def _start_processes( - self, - manager, - executor: ProcessPoolExecutor, - scheduling_strategy: SchedulingStrategy, - ) -> tuple[ - list[asyncio.Future], - multiprocessing.Queue, - multiprocessing.Queue, - ]: - await self.worker.prepare_multiprocessing() - requests_queue = manager.Queue( - maxsize=scheduling_strategy.queued_requests_limit - ) - responses_queue = manager.Queue() - - num_processes = min( - scheduling_strategy.processes_limit, - scheduling_strategy.processing_requests_limit, - ) - requests_limit_split = ( - scheduling_strategy.processing_requests_limit - // scheduling_strategy.processes_limit - ) - requests_limit_remain = ( - scheduling_strategy.processing_requests_limit - % scheduling_strategy.processes_limit - ) - process_ids = (id_ for id_ in range(num_processes)) - process_requests_limits = ( - requests_limit_split + 1 - if i < requests_limit_remain - else requests_limit_split - for i in range(num_processes) - ) - - futures = [] - loop = asyncio.get_event_loop() - for id_, requests_limit in zip(process_ids, process_requests_limits): - if scheduling_strategy.processing_mode == "sync": - futures.append( - loop.run_in_executor( - executor, - self.worker.process_loop_synchronous, - requests_queue, - responses_queue, - id_, - ) - ) - elif scheduling_strategy.processing_mode == "async": - futures.append( - loop.run_in_executor( - executor, - self.worker.process_loop_asynchronous, - requests_queue, - responses_queue, - requests_limit, - id_, - ) - ) - else: - raise ValueError( - f"Invalid processing mode: {scheduling_strategy.processing_mode} " - f"for strategy: {scheduling_strategy}" - ) - - await asyncio.sleep(0.1) # give time for processes to start - - return futures, requests_queue, responses_queue - - def _run_setup( - self, - processes: list[asyncio.Future], - scheduling_strategy: SchedulingStrategy, - max_number: Optional[int], - max_duration: Optional[float], - ) -> tuple[SchedulerRunInfo, Iterator[Any], Iterator[float]]: - requests_iter = iter(self.request_loader) - start_time = time.time() - times_iter = iter(scheduling_strategy.request_times()) - end_time = time.time() + (max_duration or math.inf) - end_number = max_number or math.inf - - try: - # update end number if the request loader is finite and less than max - iter_length = len(self.request_loader) # type: ignore[arg-type] - if 0 < iter_length < end_number: - end_number = iter_length - except Exception: # noqa: BLE001, S110 - pass - - if end_number == math.inf and end_time is None: - logger.warning( - "No end number or end time set, " - "scheduler will run indefinitely until the request loader is exhausted." - ) - - info = SchedulerRunInfo( - start_time=start_time, - end_time=end_time, - end_number=end_number, - processes=len(processes), - strategy=scheduling_strategy, - ) - - return info, requests_iter, times_iter - - def _add_requests( - self, - requests_iter: Optional[Iterator[Any]], - times_iter: Iterator[float], - requests_queue: multiprocessing.Queue, - run_info: SchedulerRunInfo, - ) -> Optional[Iterator[Any]]: - if requests_iter is not None: - try: - added_count = 0 - - while ( - not requests_queue.full() - and added_count < settings.max_add_requests_per_loop - ): - if run_info.created_requests >= run_info.end_number: - raise StopIteration - - if ( - request_time := next(times_iter) - ) >= run_info.end_time or time.time() >= run_info.end_time: - raise StopIteration - - request = next(requests_iter) - work_req: WorkerProcessRequest[RequestT] = WorkerProcessRequest( - request=request, - start_time=request_time, - timeout_time=run_info.end_time, - queued_time=time.time(), - ) - requests_queue.put(work_req) - - run_info.created_requests += 1 - run_info.queued_requests += 1 - added_count += 1 - except StopIteration: - # we've reached the limit number, limit time, or exhausted the requests - # set to None to stop adding more and tell the loop no more requests - requests_iter = None - - return requests_iter - - def _check_result_ready( - self, - responses_queue: multiprocessing.Queue, - run_info: SchedulerRunInfo, - ) -> Optional[SchedulerRequestResult[RequestT, ResponseT]]: - try: - process_response: WorkerProcessResult[RequestT, ResponseT] = ( - responses_queue.get_nowait() - ) - except multiprocessing.queues.Empty: # type: ignore[attr-defined] - return None - - if process_response.type_ == "request_scheduled": - run_info.queued_requests -= 1 - run_info.scheduled_requests += 1 - - return SchedulerRequestResult( - type_="request_scheduled", - run_info=run_info, - request=process_response.request, - request_info=process_response.info, - response=None, - ) - - if process_response.type_ == "request_start": - run_info.scheduled_requests -= 1 - run_info.processing_requests += 1 - - return SchedulerRequestResult( - type_="request_start", - run_info=run_info, - request=process_response.request, - request_info=process_response.info, - response=None, - ) - - if process_response.type_ == "request_complete": - run_info.processing_requests -= 1 - run_info.completed_requests += 1 - - return SchedulerRequestResult( - type_="request_complete", - run_info=run_info, - request=process_response.request, - request_info=process_response.info, - response=process_response.response, - ) - raise ValueError(f"Invalid process response type: {process_response}") - - async def _stop_processes( - self, - futures: list[asyncio.Future], - requests_queue: multiprocessing.Queue, - ): - for _ in futures: - requests_queue.put(None) - - await asyncio.gather(*futures) diff --git a/src/guidellm/scheduler/strategy.py b/src/guidellm/scheduler/strategy.py deleted file mode 100644 index 200c799e..00000000 --- a/src/guidellm/scheduler/strategy.py +++ /dev/null @@ -1,493 +0,0 @@ -import math -import os -import random -import time -from collections.abc import Generator -from typing import ( - Literal, - Optional, - Union, -) - -from pydantic import Field - -from guidellm.config import settings -from guidellm.objects import StandardBaseModel - -__all__ = [ - "AsyncConstantStrategy", - "AsyncPoissonStrategy", - "ConcurrentStrategy", - "SchedulingStrategy", - "StrategyType", - "SynchronousStrategy", - "ThroughputStrategy", - "strategy_display_str", -] - - -StrategyType = Literal["synchronous", "concurrent", "throughput", "constant", "poisson"] - - -class SchedulingStrategy(StandardBaseModel): - """ - An abstract base class for scheduling strategies. - This class defines the interface for scheduling requests and provides - a common structure for all scheduling strategies. - Subclasses should implement the `request_times` method to provide - specific scheduling behavior. - - :param type_: The type of scheduling strategy to use. - This should be one of the predefined strategy types. - """ - - type_: Literal["strategy"] = Field( - description="The type of scheduling strategy schedule requests with.", - ) - - @property - def processing_mode(self) -> Literal["sync", "async"]: - """ - The processing mode for the scheduling strategy, either 'sync' or 'async'. - This property determines how the worker processes are setup: - either to run synchronously with one request at a time or asynchronously. - This property should be implemented by subclasses to return - the appropriate processing mode. - - :return: The processing mode for the scheduling strategy, - either 'sync' or 'async'. - """ - return "async" - - @property - def processes_limit(self) -> int: - """ - The limit on the number of worker processes for the scheduling strategy. - It determines how many worker processes are created - for the scheduling strategy and must be implemented by subclasses. - - :return: The number of processes for the scheduling strategy. - """ - cpu_cores = os.cpu_count() or 1 - - return min(max(1, cpu_cores - 1), settings.max_worker_processes) - - @property - def queued_requests_limit(self) -> Optional[int]: - """ - The maximum number of queued requests for the scheduling strategy. - It determines how many requests can be queued at one time - for the scheduling strategy and must be implemented by subclasses. - - :return: The maximum number of queued requests for the scheduling strategy. - """ - return settings.max_concurrency - - @property - def processing_requests_limit(self) -> int: - """ - The maximum number of processing requests for the scheduling strategy. - It determines how many requests can be processed at one time - for the scheduling strategy and must be implemented by subclasses. - - :return: The maximum number of processing requests for the scheduling strategy. - """ - return settings.max_concurrency - - def request_times(self) -> Generator[float, None, None]: - """ - A generator that yields timestamps for when requests should be sent. - This method should be implemented by subclasses to provide specific - scheduling behavior. - - :return: A generator that yields timestamps for request scheduling - or -1 for requests that should be sent immediately. - """ - raise NotImplementedError("Subclasses must implement request_times() method.") - - -class SynchronousStrategy(SchedulingStrategy): - """ - A class representing a synchronous scheduling strategy. - This strategy schedules requests synchronously, one at a time, - with the maximum rate possible. - It inherits from the `SchedulingStrategy` base class and - implements the `request_times` method to provide the specific - behavior for synchronous scheduling. - - :param type_: The synchronous StrategyType to schedule requests synchronously. - """ - - type_: Literal["synchronous"] = "synchronous" # type: ignore[assignment] - - @property - def processing_mode(self) -> Literal["sync"]: - """ - The processing mode for the scheduling strategy, either 'sync' or 'async'. - This property determines how the worker processes are setup: - either to run synchronously with one request at a time or asynchronously. - - :return: 'sync' for synchronous scheduling strategy - for the single worker process. - """ - return "sync" - - @property - def processes_limit(self) -> int: - """ - The limit on the number of worker processes for the scheduling strategy. - It determines how many worker processes are created - for the scheduling strategy and must be implemented by subclasses. - - :return: 1 for the synchronous scheduling strategy to limit - the worker processes to one. - """ - return 1 - - @property - def queued_requests_limit(self) -> int: - """ - The maximum number of queued requests for the scheduling strategy. - It determines how many requests can be queued at one time - for the scheduling strategy and must be implemented by subclasses. - - :return: 1 for the synchronous scheduling strategy to limit - the queued requests to one that is ready to be processed. - """ - return 1 - - @property - def processing_requests_limit(self) -> int: - """ - The maximum number of processing requests for the scheduling strategy. - It determines how many requests can be processed at one time - for the scheduling strategy and must be implemented by subclasses. - - :return: 1 for the synchronous scheduling strategy to limit - the processing requests to one that is ready to be processed. - """ - return 1 - - def request_times(self) -> Generator[float, None, None]: - """ - A generator that yields time.time() so requests are sent immediately, - while scheduling them synchronously. - - :return: A generator that yields time.time() for immediate request scheduling. - """ - while True: - yield time.time() - - -class ConcurrentStrategy(SchedulingStrategy): - """ - A class representing a concurrent scheduling strategy. - This strategy schedules requests concurrently with the specified - number of streams. - It inherits from the `SchedulingStrategy` base class and - implements the `request_times` method to provide the specific - behavior for concurrent scheduling. - - :param type_: The concurrent StrategyType to schedule requests concurrently. - :param streams: The number of concurrent streams to use for scheduling requests. - Each stream runs synchronously with the maximum rate possible. - This must be a positive integer. - """ - - type_: Literal["concurrent"] = "concurrent" # type: ignore[assignment] - streams: int = Field( - description=( - "The number of concurrent streams to use for scheduling requests. " - "Each stream runs sychronously with the maximum rate possible. " - "This must be a positive integer." - ), - gt=0, - ) - - @property - def processing_mode(self) -> Literal["sync"]: - """ - The processing mode for the scheduling strategy, either 'sync' or 'async'. - This property determines how the worker processes are setup: - either to run synchronously with one request at a time or asynchronously. - - :return: 'sync' for synchronous scheduling strategy - for the multiple worker processes equal to streams. - """ - return "sync" - - @property - def processes_limit(self) -> int: - """ - The limit on the number of worker processes for the scheduling strategy. - It determines how many worker processes are created - for the scheduling strategy and must be implemented by subclasses. - - :return: {self.streams} for the concurrent scheduling strategy to limit - the worker processes to the number of streams. - """ - return self.streams - - @property - def queued_requests_limit(self) -> int: - """ - The maximum number of queued requests for the scheduling strategy. - It determines how many requests can be queued at one time - for the scheduling strategy and must be implemented by subclasses. - - :return: {self.streams} for the concurrent scheduling strategy to limit - the queued requests to the number of streams that are ready to be processed. - """ - return self.streams - - @property - def processing_requests_limit(self) -> int: - """ - The maximum number of processing requests for the scheduling strategy. - It determines how many requests can be processed at one time - for the scheduling strategy and must be implemented by subclasses. - - :return: {self.streams} for the concurrent scheduling strategy to limit - the processing requests to the number of streams that ready to be processed. - """ - return self.streams - - def request_times(self) -> Generator[float, None, None]: - """ - A generator that yields time.time() so requests are sent - immediately, while scheduling them concurrently with the specified - number of streams. - - :return: A generator that yields time.time() for immediate request scheduling. - """ - while True: - yield time.time() - - -class ThroughputStrategy(SchedulingStrategy): - """ - A class representing a throughput scheduling strategy. - This strategy schedules as many requests asynchronously as possible, - with the maximum rate possible. - It inherits from the `SchedulingStrategy` base class and - implements the `request_times` method to provide the specific - behavior for throughput scheduling. - - :param type_: The throughput StrategyType to schedule requests asynchronously. - """ - - type_: Literal["throughput"] = "throughput" # type: ignore[assignment] - max_concurrency: Optional[int] = Field( - default=None, - description=( - "The maximum number of concurrent requests to schedule. " - "If set to None, the concurrency value from settings will be used. " - "This must be a positive integer greater than 0." - ), - gt=0, - ) - - @property - def processing_mode(self) -> Literal["async"]: - """ - The processing mode for the scheduling strategy, either 'sync' or 'async'. - This property determines how the worker processes are setup: - either to run synchronously with one request at a time or asynchronously. - - :return: 'async' for asynchronous scheduling strategy - for the multiple worker processes handling requests. - """ - return "async" - - @property - def queued_requests_limit(self) -> int: - """ - The maximum number of queued requests for the scheduling strategy. - It determines how many requests can be queued at one time - for the scheduling strategy and must be implemented by subclasses. - - :return: The processing requests limit to ensure that there are enough - requests even for the worst case scenario where the max concurrent - requests are pulled at once for processing. - """ - return self.processing_requests_limit - - @property - def processing_requests_limit(self) -> int: - """ - The maximum number of processing requests for the scheduling strategy. - It determines how many requests can be processed at one time - for the scheduling strategy and must be implemented by subclasses. - - :return: {self.max_concurrency} for the throughput scheduling strategy to limit - the processing requests to the maximum concurrency. - If max_concurrency is None, then the default processing requests limit - will be used. - """ - return self.max_concurrency or super().processing_requests_limit - - def request_times(self) -> Generator[float, None, None]: - """ - A generator that yields the start time.time() so requests are sent - immediately, while scheduling as many asynchronously as possible. - - :return: A generator that yields the start time.time() - for immediate request scheduling. - """ - start_time = time.time() - - while True: - yield start_time - - -class AsyncConstantStrategy(ThroughputStrategy): - """ - A class representing an asynchronous constant scheduling strategy. - This strategy schedules requests asynchronously at a constant request rate - in requests per second. - If initial_burst is set, it will send an initial burst of math.floor(rate) - requests to reach the target rate. - This is useful to ensure that the target rate is reached quickly - and then maintained. - It inherits from the `SchedulingStrategy` base class and - implements the `request_times` method to provide the specific - behavior for asynchronous constant scheduling. - - :param type_: The constant StrategyType to schedule requests asynchronously. - :param rate: The rate at which to schedule requests asynchronously in - requests per second. This must be a positive float. - :param initial_burst: True to send an initial burst of requests - (math.floor(self.rate)) to reach target rate. - False to not send an initial burst. - """ - - type_: Literal["constant"] = "constant" # type: ignore[assignment] - rate: float = Field( - description=( - "The rate at which to schedule requests asynchronously in " - "requests per second. This must be a positive float." - ), - gt=0, - ) - initial_burst: bool = Field( - default=True, - description=( - "True to send an initial burst of requests (math.floor(self.rate)) " - "to reach target rate. False to not send an initial burst." - ), - ) - - def request_times(self) -> Generator[float, None, None]: - """ - A generator that yields timestamps for when requests should be sent. - This method schedules requests asynchronously at a constant rate - in requests per second. - If burst_time is set, it will send an initial burst of requests - to reach the target rate. - This is useful to ensure that the target rate is reached quickly - and then maintained. - - :return: A generator that yields timestamps for request scheduling. - """ - start_time = time.time() - constant_increment = 1.0 / self.rate - - # handle bursts first to get to the desired rate - if self.initial_burst is not None: - # send an initial burst equal to the rate - # to reach the target rate - burst_count = math.floor(self.rate) - for _ in range(burst_count): - yield start_time - - start_time += constant_increment - - counter = 0 - - # continue with constant rate after bursting - while True: - yield start_time + constant_increment * counter - counter += 1 - - -class AsyncPoissonStrategy(ThroughputStrategy): - """ - A class representing an asynchronous Poisson scheduling strategy. - This strategy schedules requests asynchronously at a Poisson request rate - in requests per second. - If initial_burst is set, it will send an initial burst of math.floor(rate) - requests to reach the target rate. - It inherits from the `SchedulingStrategy` base class and - implements the `request_times` method to provide the specific - behavior for asynchronous Poisson scheduling. - - :param type_: The Poisson StrategyType to schedule requests asynchronously. - :param rate: The rate at which to schedule requests asynchronously in - requests per second. This must be a positive float. - :param initial_burst: True to send an initial burst of requests - (math.floor(self.rate)) to reach target rate. - False to not send an initial burst. - """ - - type_: Literal["poisson"] = "poisson" # type: ignore[assignment] - rate: float = Field( - description=( - "The rate at which to schedule requests asynchronously in " - "requests per second. This must be a positive float." - ), - gt=0, - ) - initial_burst: bool = Field( - default=True, - description=( - "True to send an initial burst of requests (math.floor(self.rate)) " - "to reach target rate. False to not send an initial burst." - ), - ) - random_seed: int = Field( - default=42, - description=("The random seed to use for the Poisson distribution. "), - ) - - def request_times(self) -> Generator[float, None, None]: - """ - A generator that yields timestamps for when requests should be sent. - This method schedules requests asynchronously at a Poisson rate - in requests per second. - The inter arrival time between requests is exponentially distributed - based on the rate. - - :return: A generator that yields timestamps for request scheduling. - """ - start_time = time.time() - - if self.initial_burst is not None: - # send an initial burst equal to the rate - # to reach the target rate - burst_count = math.floor(self.rate) - for _ in range(burst_count): - yield start_time - else: - yield start_time - - # set the random seed for reproducibility - rand = random.Random(self.random_seed) # noqa: S311 - - while True: - inter_arrival_time = rand.expovariate(self.rate) - start_time += inter_arrival_time - yield start_time - - -def strategy_display_str(strategy: Union[StrategyType, SchedulingStrategy]) -> str: - strategy_type = strategy if isinstance(strategy, str) else strategy.type_ - strategy_instance = strategy if isinstance(strategy, SchedulingStrategy) else None - - if strategy_type == "concurrent": - rate = f"@{strategy_instance.streams}" if strategy_instance else "@##" # type: ignore[attr-defined] - elif strategy_type in ("constant", "poisson"): - rate = f"@{strategy_instance.rate:.2f}" if strategy_instance else "@#.##" # type: ignore[attr-defined] - else: - rate = "" - - return f"{strategy_type}{rate}" diff --git a/src/guidellm/scheduler/types.py b/src/guidellm/scheduler/types.py deleted file mode 100644 index 42535d71..00000000 --- a/src/guidellm/scheduler/types.py +++ /dev/null @@ -1,7 +0,0 @@ -from typing import TypeVar - -__all__ = ["RequestT", "ResponseT"] - - -RequestT = TypeVar("RequestT") -ResponseT = TypeVar("ResponseT") diff --git a/src/guidellm/scheduler/worker.py b/src/guidellm/scheduler/worker.py deleted file mode 100644 index 9f24d9f9..00000000 --- a/src/guidellm/scheduler/worker.py +++ /dev/null @@ -1,511 +0,0 @@ -import asyncio -import math -import multiprocessing -import multiprocessing.queues -import time -from abc import ABC, abstractmethod -from collections.abc import AsyncGenerator -from dataclasses import dataclass -from typing import ( - Any, - Generic, - Literal, - Optional, - Union, -) - -from loguru import logger -from pydantic import Field - -from guidellm.backend import ( - Backend, - BackendType, - RequestArgs, - ResponseSummary, - StreamingTextResponse, -) -from guidellm.objects import StandardBaseModel -from guidellm.request import GenerationRequest -from guidellm.scheduler.result import SchedulerRequestInfo -from guidellm.scheduler.types import RequestT, ResponseT - -__all__ = [ - "GenerativeRequestsWorker", - "GenerativeRequestsWorkerDescription", - "RequestsWorker", - "ResolveStatus", - "WorkerDescription", - "WorkerProcessRequest", - "WorkerProcessResult", -] - - -@dataclass -class WorkerProcessRequest(Generic[RequestT]): - request: RequestT - start_time: float - timeout_time: float - queued_time: float - - -@dataclass -class WorkerProcessResult(Generic[RequestT, ResponseT]): - type_: Literal["request_scheduled", "request_start", "request_complete"] - request: RequestT - response: Optional[ResponseT] - info: SchedulerRequestInfo - - -@dataclass -class ResolveStatus: - requested: bool - completed: bool - errored: bool - canceled: bool - - request_start: float - request_end: float - - -class WorkerDescription(StandardBaseModel): - type_: Literal["worker"] = "worker" - - -class RequestsWorker(ABC, Generic[RequestT, ResponseT]): - """ - An abstract base class for a worker that processes requests. - This class defines the interface for a worker that can resolve requests - asynchronously or synchronously within the Scheduler class. - Subclasses must implement the `resolve` method, - which takes a request directly given from the load generator, - along with the desired start_time for the request and a timeout_time. - The `resolve` method should return the response from the backend. - """ - - @property - @abstractmethod - def description(self) -> WorkerDescription: - """ - An abstract property that must be implemented by subclasses. - This property should return a Serializable class representing the information - about the worker instance. - """ - ... - - @abstractmethod - async def prepare_multiprocessing(self): - """ - An abstract method that must be implemented by subclasses. - This is useful for workers that have instance state that can not - be shared across processes and should be cleared out and re-initialized - for each new process. - """ - ... - - @abstractmethod - async def resolve( - self, - request: RequestT, - timeout_time: float, - ) -> tuple[ResolveStatus, ResponseT]: - """ - An abstract method that must be implemented by subclasses. - This method should handle the resolution of a request through asyncio, - including any necessary backend processing and response handling. - - :param request: The request to be resolved generated by the load generator. - :param timeout_time: The timeout time for the request, if there is no timeout - given, then this will be math.inf. - :return: The response from the worker. - """ - ... - - async def get_request( - self, requests_queue: multiprocessing.Queue - ) -> Optional[WorkerProcessRequest[RequestT]]: - return await asyncio.to_thread(requests_queue.get) # type: ignore[attr-defined] - - async def send_result( - self, - results_queue: multiprocessing.Queue, - result: WorkerProcessResult[RequestT, ResponseT], - ): - await asyncio.to_thread(results_queue.put, result) # type: ignore[attr-defined] - - async def resolve_scheduler_request( - self, - request: Any, - queued_time: float, - dequeued_time: float, - start_time: float, - timeout_time: float, - results_queue: multiprocessing.Queue, - process_id: int, - ): - info = SchedulerRequestInfo( - targeted_start_time=start_time, - queued_time=queued_time, - dequeued_time=dequeued_time, - scheduled_time=time.time(), - process_id=process_id, - ) - result: WorkerProcessResult[RequestT, ResponseT] = WorkerProcessResult( - type_="request_scheduled", - request=request, - response=None, - info=info, - ) - asyncio.create_task(self.send_result(results_queue, result)) - - if (wait_time := start_time - time.time()) > 0: - await asyncio.sleep(wait_time) - - info.worker_start = time.time() - result = WorkerProcessResult( - type_="request_start", - request=request, - response=None, - info=info, - ) - asyncio.create_task(self.send_result(results_queue, result)) - - status, response = await self.resolve(request, timeout_time) - info.worker_end = time.time() - info.requested = status.requested - info.completed = status.completed - info.errored = status.errored - info.canceled = status.canceled - info.request_start = status.request_start - info.request_end = status.request_end - result = WorkerProcessResult( - type_="request_complete", - request=request, - response=response, - info=info, - ) - asyncio.create_task(self.send_result(results_queue, result)) - - def process_loop_synchronous( - self, - requests_queue: multiprocessing.Queue, - results_queue: multiprocessing.Queue, - process_id: int, - ): - async def _process_runner(): - while ( - process_request := await self.get_request(requests_queue) - ) is not None: - dequeued_time = time.time() - - await self.resolve_scheduler_request( - request=process_request.request, - queued_time=process_request.queued_time, - dequeued_time=dequeued_time, - start_time=process_request.start_time, - timeout_time=process_request.timeout_time, - results_queue=results_queue, - process_id=process_id, - ) - - try: - asyncio.run(_process_runner()) - except Exception as exc: # noqa: BLE001 - logger.error( - f"Error in worker process {process_id}: {exc}", - exc_info=True, - stack_info=True, - ) - - def process_loop_asynchronous( - self, - requests_queue: multiprocessing.Queue, - results_queue: multiprocessing.Queue, - max_concurrency: int, - process_id: int, - ): - async def _process_runner(): - pending = asyncio.Semaphore(max_concurrency) - - if pending.locked(): - raise ValueError("Async worker called with max_concurrency < 1") - - while ( - process_request := await self.get_request(requests_queue) - ) is not None: - dequeued_time = time.time() - - await pending.acquire() - - def _task_done(_: asyncio.Task): - nonlocal pending - pending.release() - - task = asyncio.create_task( - self.resolve_scheduler_request( - request=process_request.request, - queued_time=process_request.queued_time, - dequeued_time=dequeued_time, - start_time=process_request.start_time, - timeout_time=process_request.timeout_time, - results_queue=results_queue, - process_id=process_id, - ) - ) - task.add_done_callback(_task_done) - await asyncio.sleep(0) # enable start task immediately - - try: - asyncio.run(_process_runner()) - except Exception as exc: # noqa: BLE001 - logger.error( - f"Error in worker process {process_id}: {exc}", - exc_info=True, - stack_info=True, - ) - - -class GenerativeRequestsWorkerDescription(WorkerDescription): - type_: Literal["generative_requests_worker"] = "generative_requests_worker" # type: ignore[assignment] - backend_type: BackendType - backend_target: str - backend_model: str - backend_info: dict[str, Any] = Field( - default_factory=dict, - ) - - -class GenerativeRequestsWorker(RequestsWorker[GenerationRequest, ResponseSummary]): - """ - A class that handles the execution of requests using a backend. - This class is responsible for sending requests to the backend, - handling responses, and managing errors. - - :param backend: The backend to use for handling requests. - This should be an instance of Backend such as an OpenAIHTTPBackend. - """ - - def __init__(self, backend: Backend): - self.backend = backend - - @property - def description(self) -> GenerativeRequestsWorkerDescription: - """ - Get the description of the worker. - :return: The description of the worker. - """ - return GenerativeRequestsWorkerDescription( - backend_type=self.backend.type_, - backend_target=self.backend.target, - backend_model=self.backend.model or "None", - backend_info=self.backend.info, - ) - - async def prepare_multiprocessing(self): - """ - Prepare the worker for multiprocessing. - This is useful for workers that have instance state that can not - be shared across processes and should be cleared out and re-initialized - for each new process. - """ - await self.backend.prepare_multiprocessing() - - def process_loop_synchronous( - self, - requests_queue: multiprocessing.Queue, - results_queue: multiprocessing.Queue, - process_id: int, - ): - asyncio.run(self.backend.validate()) - super().process_loop_synchronous( - requests_queue=requests_queue, - results_queue=results_queue, - process_id=process_id, - ) - - def process_loop_asynchronous( - self, - requests_queue: multiprocessing.Queue, - results_queue: multiprocessing.Queue, - max_concurrency: int, - process_id: int, - ): - asyncio.run(self.backend.validate()) - super().process_loop_asynchronous( - requests_queue=requests_queue, - results_queue=results_queue, - max_concurrency=max_concurrency, - process_id=process_id, - ) - - async def resolve( - self, - request: GenerationRequest, - timeout_time: float, - ) -> tuple[ResolveStatus, ResponseSummary]: - """ - Resolve a request by sending it to the backend and handling the response. - This method sends the request to the backend, waits for a response, - and handles any errors that may occur during the process. - - :param request: The request to resolve. - :param timeout_time: The time to wait for a response before timing out. - If timeout_time is math.inf, the request will not timeout. - :return: A ResponseSummary object containing the response from the backend. - If an error occurs, the ResponseSummary will contain the error message. - """ - resolve_start_time = time.time() - response = None - error: Optional[str] = None - status = ResolveStatus( - requested=False, - completed=False, - errored=False, - canceled=False, - request_start=-1, - request_end=-1, - ) - - try: - if timeout_time < time.time(): - raise asyncio.TimeoutError( - "The timeout time has already passed." - ) # exit early - - status.requested = True - request_func, request_kwargs = self._create_request_func_kwargs(request) - - async def _runner(): - # wrap function so we can enforce timeout and - # still return the latest state from the backend - async for resp in request_func(**request_kwargs): # type: ignore[operator] - nonlocal response - response = resp - - await asyncio.wait_for( - _runner(), - timeout=timeout_time - time.time() if timeout_time < math.inf else None, - ) - - if not response: - raise ValueError( - f"No response received for request: {request} " - f"and backend: {self.backend}" - ) - if not isinstance(response, ResponseSummary): - raise ValueError( - f"Received no ResponseSummary for request: {request} " - f"and backend: {self.backend}, received: {response}" - ) - - status.completed = True - except asyncio.TimeoutError: - error = "TimeoutError: The request timed out before completing." - status.errored = True - status.canceled = True - except Exception as exc: # noqa: BLE001 - error = str(exc) - status.errored = True - - return self._handle_response( - status=status, - request=request, - response=response, - error=error, - resolve_start_time=resolve_start_time, - ) - - def _create_request_func_kwargs( - self, - request: GenerationRequest, - ) -> tuple[ - AsyncGenerator[Union[StreamingTextResponse, ResponseSummary], None], - dict[str, Any], - ]: - request_func: AsyncGenerator[ - Union[StreamingTextResponse, ResponseSummary], None - ] - request_kwargs: dict[str, Any] - - if request.request_type == "text_completions": - request_func = self.backend.text_completions # type: ignore[assignment] - request_kwargs = { - "prompt": request.content, - "request_id": request.request_id, - "prompt_token_count": request.stats.get("prompt_tokens", None), - "output_token_count": request.constraints.get("output_tokens", None), - **request.params, - } - elif request.request_type == "chat_completions": - request_func = self.backend.chat_completions # type: ignore[assignment] - request_kwargs = { - "content": request.content, - "request_id": request.request_id, - "prompt_token_count": request.stats.get("prompt_tokens", None), - "output_token_count": request.constraints.get("output_tokens", None), - **request.params, - } - else: - raise ValueError( - f"Invalid request type: {request.request_type} for {request}" - ) - - return request_func, request_kwargs - - def _handle_response( - self, - status: ResolveStatus, - request: GenerationRequest, - response: Any, - error: Optional[str], - resolve_start_time: float, - ) -> tuple[ResolveStatus, ResponseSummary]: - if response is None or not isinstance( - response, (ResponseSummary, StreamingTextResponse) - ): - # nothing received or invalid response, fill in defaults for error - if response: - error = str( - ValueError( - f"Invalid response: {type(response)} for request: {request}; " - ) - ) + (error or "") - - response = ResponseSummary( - value="", - request_args=RequestArgs( - target=self.backend.target, - headers={}, - payload={}, - ), - start_time=resolve_start_time, - end_time=status.request_end, - first_iter_time=None, - last_iter_time=None, - request_id=request.request_id, - error=error or "Unknown error", - ) - elif isinstance(response, StreamingTextResponse): - response = ResponseSummary( - value=response.value, - request_args=RequestArgs( - target=self.backend.target, - headers={}, - payload={}, - ), - start_time=response.start_time, - end_time=time.time(), - first_iter_time=response.first_iter_time, - last_iter_time=response.time if response.iter_count > 0 else None, - request_prompt_tokens=request.stats.get("prompt_tokens", None), - request_output_tokens=request.constraints.get("output_tokens", None), - response_prompt_tokens=None, - response_output_tokens=response.iter_count, - request_id=request.request_id, - error=error or "Unknown error", - ) - - response.error = error - status.request_start = response.start_time - status.request_end = response.end_time - - return status, response diff --git a/src/guidellm/utils/__init__.py b/src/guidellm/utils/__init__.py deleted file mode 100644 index 753bef02..00000000 --- a/src/guidellm/utils/__init__.py +++ /dev/null @@ -1,27 +0,0 @@ -from .colors import Colors -from .hf_transformers import ( - check_load_processor, -) -from .random import IntegerRangeSampler -from .text import ( - EndlessTextCreator, - clean_text, - filter_text, - is_puncutation, - load_text, - split_text, - split_text_list_by_length, -) - -__all__ = [ - "Colors", - "EndlessTextCreator", - "IntegerRangeSampler", - "check_load_processor", - "clean_text", - "filter_text", - "is_puncutation", - "load_text", - "split_text", - "split_text_list_by_length", -] diff --git a/src/guidellm/utils/colors.py b/src/guidellm/utils/colors.py deleted file mode 100644 index e4d60d52..00000000 --- a/src/guidellm/utils/colors.py +++ /dev/null @@ -1,8 +0,0 @@ -__all__ = ["Colors"] - - -class Colors: - INFO: str = "light_steel_blue" - PROGRESS: str = "dark_slate_gray1" - SUCCESS: str = "chartreuse1" - ERROR: str = "orange_red1" diff --git a/src/guidellm/utils/hf_transformers.py b/src/guidellm/utils/hf_transformers.py deleted file mode 100644 index 1f2aa1b5..00000000 --- a/src/guidellm/utils/hf_transformers.py +++ /dev/null @@ -1,35 +0,0 @@ -from pathlib import Path -from typing import Any, Optional, Union - -from transformers import AutoTokenizer, PreTrainedTokenizerBase # type: ignore[import] - -__all__ = [ - "check_load_processor", -] - - -def check_load_processor( - processor: Optional[Union[str, Path, PreTrainedTokenizerBase]], - processor_args: Optional[dict[str, Any]], - error_msg: str, -) -> PreTrainedTokenizerBase: - if processor is None: - raise ValueError(f"Processor/Tokenizer is required for {error_msg}.") - - try: - if isinstance(processor, (str, Path)): - loaded = AutoTokenizer.from_pretrained( - processor, - **(processor_args or {}), - ) - else: - loaded = processor - except Exception as err: - raise ValueError( - f"Failed to load processor/Tokenizer for {error_msg}." - ) from err - - if not isinstance(loaded, PreTrainedTokenizerBase): - raise ValueError(f"Invalid processor/Tokenizer for {error_msg}.") - - return loaded diff --git a/src/guidellm/utils/random.py b/src/guidellm/utils/random.py deleted file mode 100644 index 19ec49b3..00000000 --- a/src/guidellm/utils/random.py +++ /dev/null @@ -1,43 +0,0 @@ -import random -from collections.abc import Iterator -from typing import Optional - -__all__ = ["IntegerRangeSampler"] - - -class IntegerRangeSampler: - def __init__( - self, - average: int, - variance: Optional[int], - min_value: Optional[int], - max_value: Optional[int], - random_seed: int, - ): - self.average = average - self.variance = variance - self.min_value = min_value - self.max_value = max_value - self.seed = random_seed - self.rng = random.Random(random_seed) # noqa: S311 - - def __iter__(self) -> Iterator[int]: - calc_min = self.min_value - if calc_min is None: - calc_min = max( - 1, self.average - 5 * self.variance if self.variance else self.average - ) - calc_max = self.max_value - if calc_max is None: - calc_max = ( - self.average + 5 * self.variance if self.variance else self.average - ) - - while True: - if calc_min == calc_max: - yield calc_min - elif not self.variance: - yield self.rng.randint(calc_min, calc_max + 1) - else: - rand = self.rng.gauss(self.average, self.variance) - yield round(max(calc_min, min(calc_max, rand))) diff --git a/src/guidellm/utils/text.py b/src/guidellm/utils/text.py deleted file mode 100644 index cdefaa14..00000000 --- a/src/guidellm/utils/text.py +++ /dev/null @@ -1,216 +0,0 @@ -import gzip -import re -import textwrap -from importlib.resources import as_file, files # type: ignore[attr-defined] -from pathlib import Path -from typing import Any, Optional, Union - -import ftfy -import httpx -from loguru import logger - -from guidellm import data as package_data -from guidellm.config import settings - -__all__ = [ - "EndlessTextCreator", - "clean_text", - "filter_text", - "is_puncutation", - "load_text", - "split_text", - "split_text_list_by_length", -] - -MAX_PATH_LENGTH = 4096 - - -def split_text_list_by_length( - text_list: list[Any], - max_characters: Union[int, list[int]], - pad_horizontal: bool = True, - pad_vertical: bool = True, -) -> list[list[str]]: - """ - Split a list of strings into a list of strings, - each with a maximum length of max_characters - - :param text_list: the list of strings to split - :param max_characters: the maximum length of each string - :param pad_horizontal: whether to pad the strings horizontally, defaults to True - :param pad_vertical: whether to pad the strings vertically, defaults to True - :return: a list of strings - """ - if not isinstance(max_characters, list): - max_characters = [max_characters] * len(text_list) - - if len(max_characters) != len(text_list): - raise ValueError( - f"max_characters must be a list of the same length as text_list, " - f"but got {len(max_characters)} and {len(text_list)}" - ) - - result: list[list[str]] = [] - for index, text in enumerate(text_list): - lines = textwrap.wrap(text, max_characters[index]) - result.append(lines) - - if pad_vertical: - max_lines = max(len(lines) for lines in result) - for lines in result: - while len(lines) < max_lines: - lines.append(" ") - - if pad_horizontal: - for index in range(len(result)): - lines = result[index] - max_chars = max_characters[index] - new_lines = [] - for line in lines: - new_lines.append(line.rjust(max_chars)) - result[index] = new_lines - - return result - - -def filter_text( - text: str, - filter_start: Optional[Union[str, int]] = None, - filter_end: Optional[Union[str, int]] = None, -) -> str: - """ - Filter text by start and end strings or indices - - :param text: the text to filter - :param filter_start: the start string or index to filter from - :param filter_end: the end string or index to filter to - :return: the filtered text - """ - filter_start_index = -1 - filter_end_index = -1 - - if filter_start and isinstance(filter_start, str): - filter_start_index = text.index(filter_start) - elif filter_start: - if not isinstance(filter_start, int): - raise ValueError(f"Invalid filter start index: {filter_start}") - filter_start_index = filter_start - - if filter_end and isinstance(filter_end, str): - filter_end_index = text.index(filter_end) - elif filter_end: - if not isinstance(filter_end, int): - raise ValueError(f"Invalid filter end index: {filter_end}") - filter_end_index = filter_end - - if filter_start_index > -1: - text = text[filter_start_index:] - if filter_end_index > -1: - text = text[:filter_end_index] - - return text - - -def clean_text(text: str) -> str: - return re.sub(r"\s+", " ", ftfy.fix_text(text)).strip() - - -def split_text(text: str, split_punctuation: bool = False) -> list[str]: - text = clean_text(text) - - if split_punctuation: - return re.findall(r"[\w]+|[.,!?;]", text) - - return text.split() - - -def load_text(data: Union[str, Path], encoding: Optional[str] = None) -> str: - """ - Load an HTML file from a path or URL - - :param data: the path or URL to load the HTML file from - :type data: Union[str, Path] - :param encoding: the encoding to use when reading the file - :type encoding: str - :return: the HTML content - :rtype: str - """ - logger.debug("Loading text: {}", data) - - if not data: - return "" - - # check URLs - if isinstance(data, str) and data.strip().startswith(("http", "ftp")): - with httpx.Client(timeout=settings.request_timeout) as client: - response = client.get(data.strip()) - response.raise_for_status() - return response.text - - # check package data - if isinstance(data, str) and data.startswith("data:"): - resource_path = files(package_data).joinpath(data[5:]) - with ( - as_file(resource_path) as resource_file, - gzip.open(resource_file, "rt", encoding=encoding) as file, - ): - return file.read() - - # check gzipped files - if isinstance(data, str) and data.endswith(".gz"): - with gzip.open(data, "rt", encoding=encoding) as file: - return file.read() - - # check if it's raw text by not being a path - if isinstance(data, str) and ( - len(data) > MAX_PATH_LENGTH or not Path(data).exists() - ): - return data - - # assume local file - if not isinstance(data, Path): - data = Path(data) - - if not data.exists() or not data.is_file(): - raise FileNotFoundError(f"File not found: {data}") - - return data.read_text(encoding=encoding) - - -def is_puncutation(text: str) -> bool: - """ - Check if the text is a punctuation - - :param text: the text to check - :type text: str - :return: True if the text is a punctuation, False otherwise - :rtype: bool - """ - return len(text) == 1 and not text.isalnum() and not text.isspace() - - -class EndlessTextCreator: - def __init__( - self, - data: Union[str, Path], - filter_start: Optional[Union[str, int]] = None, - filter_end: Optional[Union[str, int]] = None, - ): - self.data = data - self.text = load_text(data) - self.filtered_text = filter_text(self.text, filter_start, filter_end) - self.words = split_text(self.filtered_text, split_punctuation=True) - - def create_text(self, start: int, length: int) -> str: - text = "" - - for counter in range(length): - index = (start + counter) % len(self.words) - add_word = self.words[index] - - if counter != 0 and not is_puncutation(add_word): - text += " " - - text += add_word - - return text diff --git a/tests/__init__.py b/tests/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/tests/e2e/__init__.py b/tests/e2e/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/tests/e2e/test_placeholder.py b/tests/e2e/test_placeholder.py deleted file mode 100644 index 0d35031c..00000000 --- a/tests/e2e/test_placeholder.py +++ /dev/null @@ -1,6 +0,0 @@ -import pytest - - -@pytest.mark.smoke -def test_placeholder(): - assert True diff --git a/tests/integration/__init__.py b/tests/integration/__init__.py deleted file mode 100644 index c43cd8d5..00000000 --- a/tests/integration/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -""" -Integration tests test the functionality between 2 or more dedicated modules. -In our case, some integration tests may hit external infrastructure components. - -For example, the backend integration test may hit -a mocked OpenAI-compatible server for better testing. -""" diff --git a/tests/integration/test_placeholder.py b/tests/integration/test_placeholder.py deleted file mode 100644 index 0d35031c..00000000 --- a/tests/integration/test_placeholder.py +++ /dev/null @@ -1,6 +0,0 @@ -import pytest - - -@pytest.mark.smoke -def test_placeholder(): - assert True diff --git a/tests/unit/__init__.py b/tests/unit/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/tests/unit/backend/__init__.py b/tests/unit/backend/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/tests/unit/backend/test_backend.py b/tests/unit/backend/test_backend.py deleted file mode 100644 index 1115d509..00000000 --- a/tests/unit/backend/test_backend.py +++ /dev/null @@ -1,136 +0,0 @@ -import time - -import pytest - -from guidellm.backend import ( - Backend, - ResponseSummary, - StreamingTextResponse, -) - - -@pytest.mark.smoke -def test_backend_registry(): - assert Backend._registry["mock"] is not None # type: ignore - - backend_instance = Backend.create("mock") # type: ignore - assert backend_instance is not None - - with pytest.raises(ValueError): - Backend.register("mock")("backend") # type: ignore - - with pytest.raises(ValueError): - Backend.create("invalid_type") # type: ignore - - -@pytest.mark.smoke -@pytest.mark.asyncio -async def test_backend_text_completions(mock_backend): - index = 0 - prompt = "Test Prompt" - request_id = "test-request-id" - prompt_token_count = 3 - output_token_count = 10 - final_resp = None - - async for response in mock_backend.text_completions( - prompt=prompt, - request_id=request_id, - prompt_token_count=prompt_token_count, - output_token_count=output_token_count, - ): - assert isinstance(response, (StreamingTextResponse, ResponseSummary)) - - if index == 0: - assert isinstance(response, StreamingTextResponse) - assert response.type_ == "start" - assert response.iter_count == 0 - assert response.delta == "" - assert response.time == pytest.approx(time.time(), abs=0.01) - assert response.request_id == request_id - elif not isinstance(response, ResponseSummary): - assert response.type_ == "iter" - assert response.iter_count == index - assert len(response.delta) > 0 - assert response.time == pytest.approx(time.time(), abs=0.01) - assert response.request_id == request_id - else: - assert not final_resp - final_resp = response - assert isinstance(response, ResponseSummary) - assert len(response.value) > 0 - assert response.iterations > 0 - assert response.start_time > 0 - assert response.end_time == pytest.approx(time.time(), abs=0.01) - assert response.request_prompt_tokens == prompt_token_count - assert response.request_output_tokens == output_token_count - assert response.response_prompt_tokens == 3 - assert response.response_output_tokens == 10 - assert response.request_id == request_id - - index += 1 - - assert final_resp - - -@pytest.mark.smoke -@pytest.mark.asyncio -async def test_backend_chat_completions(mock_backend): - index = 0 - prompt = "Test Prompt" - request_id = "test-request-id" - prompt_token_count = 3 - output_token_count = 10 - final_resp = None - - async for response in mock_backend.chat_completions( - content=prompt, - request_id=request_id, - prompt_token_count=prompt_token_count, - output_token_count=output_token_count, - ): - assert isinstance(response, (StreamingTextResponse, ResponseSummary)) - - if index == 0: - assert isinstance(response, StreamingTextResponse) - assert response.type_ == "start" - assert response.iter_count == 0 - assert response.delta == "" - assert response.time == pytest.approx(time.time(), abs=0.01) - assert response.request_id == request_id - elif not isinstance(response, ResponseSummary): - assert response.type_ == "iter" - assert response.iter_count == index - assert len(response.delta) > 0 - assert response.time == pytest.approx(time.time(), abs=0.01) - assert response.request_id == request_id - else: - assert not final_resp - final_resp = response - assert isinstance(response, ResponseSummary) - assert len(response.value) > 0 - assert response.iterations > 0 - assert response.start_time > 0 - assert response.end_time == pytest.approx(time.time(), abs=0.01) - assert response.request_prompt_tokens == prompt_token_count - assert response.request_output_tokens == output_token_count - assert response.response_prompt_tokens == 3 - assert response.response_output_tokens == 10 - assert response.request_id == request_id - - index += 1 - - assert final_resp - - -@pytest.mark.smoke -@pytest.mark.asyncio -async def test_backend_models(mock_backend): - models = await mock_backend.available_models() - assert models == ["mock-model"] - - -@pytest.mark.smoke -@pytest.mark.asyncio -async def test_backend_validate(mock_backend): - await mock_backend.validate() diff --git a/tests/unit/backend/test_openai_backend.py b/tests/unit/backend/test_openai_backend.py deleted file mode 100644 index e9c3c687..00000000 --- a/tests/unit/backend/test_openai_backend.py +++ /dev/null @@ -1,201 +0,0 @@ -import time - -import pytest - -from guidellm.backend import OpenAIHTTPBackend, ResponseSummary, StreamingTextResponse -from guidellm.config import settings - - -@pytest.mark.smoke -def test_openai_http_backend_default_initialization(): - backend = OpenAIHTTPBackend() - assert backend.target == settings.openai.base_url - assert backend.model is None - assert backend.authorization == settings.openai.bearer_token - assert backend.organization == settings.openai.organization - assert backend.project == settings.openai.project - assert backend.timeout == settings.request_timeout - assert backend.http2 is True - assert backend.max_output_tokens == settings.openai.max_output_tokens - - -@pytest.mark.smoke -def test_openai_http_backend_intialization(): - backend = OpenAIHTTPBackend( - target="http://test-target", - model="test-model", - api_key="test-key", - organization="test-org", - project="test-proj", - timeout=10, - http2=False, - max_output_tokens=100, - ) - assert backend.target == "http://test-target" - assert backend.model == "test-model" - assert backend.authorization == "Bearer test-key" - assert backend.organization == "test-org" - assert backend.project == "test-proj" - assert backend.timeout == 10 - assert backend.http2 is False - assert backend.max_output_tokens == 100 - - -@pytest.mark.smoke -@pytest.mark.asyncio -async def test_openai_http_backend_available_models(httpx_openai_mock): - backend = OpenAIHTTPBackend(target="http://target.mock") - models = await backend.available_models() - assert models == ["mock-model"] - - -@pytest.mark.smoke -@pytest.mark.asyncio -async def test_openai_http_backend_validate(httpx_openai_mock): - backend = OpenAIHTTPBackend(target="http://target.mock", model="mock-model") - await backend.validate() - - backend = OpenAIHTTPBackend(target="http://target.mock") - await backend.validate() - assert backend.model == "mock-model" - - backend = OpenAIHTTPBackend(target="http://target.mock", model="invalid-model") - with pytest.raises(ValueError): - await backend.validate() - - -@pytest.mark.smoke -@pytest.mark.asyncio -async def test_openai_http_backend_text_completions(httpx_openai_mock): - backend = OpenAIHTTPBackend(target="http://target.mock", model="mock-model") - - index = 0 - final_resp = None - async for response in backend.text_completions("Test Prompt", request_id="test-id"): - assert isinstance(response, (StreamingTextResponse, ResponseSummary)) - - if index == 0: - assert isinstance(response, StreamingTextResponse) - assert response.type_ == "start" - assert response.iter_count == 0 - assert response.delta == "" - assert response.time == pytest.approx(time.time(), abs=0.01) - assert response.request_id == "test-id" - elif not isinstance(response, ResponseSummary): - assert response.type_ == "iter" - assert response.iter_count == index - assert len(response.delta) > 0 - assert response.time == pytest.approx(time.time(), abs=0.01) - assert response.request_id == "test-id" - else: - assert not final_resp - final_resp = response - assert isinstance(response, ResponseSummary) - assert len(response.value) > 0 - assert response.request_args is not None - assert response.iterations > 0 - assert response.start_time > 0 - assert response.end_time == pytest.approx(time.time(), abs=0.01) - assert response.request_prompt_tokens is None - assert response.request_output_tokens is None - assert response.response_prompt_tokens == 3 - assert response.response_output_tokens > 0 # type: ignore - assert response.request_id == "test-id" - - index += 1 - assert final_resp - - -@pytest.mark.smoke -@pytest.mark.asyncio -async def test_openai_http_backend_text_completions_counts(httpx_openai_mock): - backend = OpenAIHTTPBackend( - target="http://target.mock", - model="mock-model", - max_output_tokens=100, - ) - final_resp = None - - async for response in backend.text_completions( - "Test Prompt", request_id="test-id", prompt_token_count=3, output_token_count=10 - ): - final_resp = response - - assert final_resp - assert isinstance(final_resp, ResponseSummary) - assert len(final_resp.value) > 0 - assert final_resp.request_args is not None - assert final_resp.request_prompt_tokens == 3 - assert final_resp.request_output_tokens == 10 - assert final_resp.response_prompt_tokens == 3 - assert final_resp.response_output_tokens == 10 - assert final_resp.request_id == "test-id" - - -@pytest.mark.smoke -@pytest.mark.asyncio -async def test_openai_http_backend_chat_completions(httpx_openai_mock): - backend = OpenAIHTTPBackend(target="http://target.mock", model="mock-model") - - index = 0 - final_resp = None - async for response in backend.chat_completions("Test Prompt", request_id="test-id"): - assert isinstance(response, (StreamingTextResponse, ResponseSummary)) - - if index == 0: - assert isinstance(response, StreamingTextResponse) - assert response.type_ == "start" - assert response.iter_count == 0 - assert response.delta == "" - assert response.time == pytest.approx(time.time(), abs=0.01) - assert response.request_id == "test-id" - elif not isinstance(response, ResponseSummary): - assert response.type_ == "iter" - assert response.iter_count == index - assert len(response.delta) > 0 - assert response.time == pytest.approx(time.time(), abs=0.01) - assert response.request_id == "test-id" - else: - assert not final_resp - final_resp = response - assert isinstance(response, ResponseSummary) - assert len(response.value) > 0 - assert response.request_args is not None - assert response.iterations > 0 - assert response.start_time > 0 - assert response.end_time == pytest.approx(time.time(), abs=0.01) - assert response.request_prompt_tokens is None - assert response.request_output_tokens is None - assert response.response_prompt_tokens == 3 - assert response.response_output_tokens > 0 # type: ignore - assert response.request_id == "test-id" - - index += 1 - - assert final_resp - - -@pytest.mark.smoke -@pytest.mark.asyncio -async def test_openai_http_backend_chat_completions_counts(httpx_openai_mock): - backend = OpenAIHTTPBackend( - target="http://target.mock", - model="mock-model", - max_output_tokens=100, - ) - final_resp = None - - async for response in backend.chat_completions( - "Test Prompt", request_id="test-id", prompt_token_count=3, output_token_count=10 - ): - final_resp = response - - assert final_resp - assert isinstance(final_resp, ResponseSummary) - assert len(final_resp.value) > 0 - assert final_resp.request_args is not None - assert final_resp.request_prompt_tokens == 3 - assert final_resp.request_output_tokens == 10 - assert final_resp.response_prompt_tokens == 3 - assert final_resp.response_output_tokens == 10 - assert final_resp.request_id == "test-id" diff --git a/tests/unit/backend/test_response.py b/tests/unit/backend/test_response.py deleted file mode 100644 index dd0c1349..00000000 --- a/tests/unit/backend/test_response.py +++ /dev/null @@ -1,184 +0,0 @@ -from typing import get_args - -import pytest - -from guidellm.backend import ( - RequestArgs, - ResponseSummary, - StreamingResponseType, - StreamingTextResponse, -) - - -@pytest.mark.smoke -def test_streaming_response_types(): - valid_types = get_args(StreamingResponseType) - assert valid_types == ("start", "iter") - - -@pytest.mark.smoke -def test_streaming_text_response_default_initilization(): - response = StreamingTextResponse( - type_="start", - value="", - start_time=0.0, - first_iter_time=None, - iter_count=0, - delta="", - time=0.0, - ) - assert response.request_id is None - - -@pytest.mark.smoke -def test_streaming_text_response_initialization(): - response = StreamingTextResponse( - type_="start", - value="Hello, world!", - start_time=0.0, - first_iter_time=0.0, - iter_count=1, - delta="Hello, world!", - time=1.0, - request_id="123", - ) - assert response.type_ == "start" - assert response.value == "Hello, world!" - assert response.start_time == 0.0 - assert response.first_iter_time == 0.0 - assert response.iter_count == 1 - assert response.delta == "Hello, world!" - assert response.time == 1.0 - assert response.request_id == "123" - - -@pytest.mark.smoke -def test_streaming_text_response_marshalling(): - response = StreamingTextResponse( - type_="start", - value="Hello, world!", - start_time=0.0, - first_iter_time=0.0, - iter_count=0, - delta="Hello, world!", - time=1.0, - request_id="123", - ) - serialized = response.model_dump() - deserialized = StreamingTextResponse.model_validate(serialized) - - for key, value in vars(response).items(): - assert getattr(deserialized, key) == value - - -@pytest.mark.smoke -def test_request_args_default_initialization(): - args = RequestArgs( - target="http://example.com", - headers={}, - payload={}, - ) - assert args.timeout is None - assert args.http2 is None - - -@pytest.mark.smoke -def test_request_args_initialization(): - args = RequestArgs( - target="http://example.com", - headers={ - "Authorization": "Bearer token", - }, - payload={ - "query": "Hello, world!", - }, - timeout=10.0, - http2=True, - ) - assert args.target == "http://example.com" - assert args.headers == {"Authorization": "Bearer token"} - assert args.payload == {"query": "Hello, world!"} - assert args.timeout == 10.0 - assert args.http2 is True - - -@pytest.mark.smoke -def test_response_args_marshalling(): - args = RequestArgs( - target="http://example.com", - headers={"Authorization": "Bearer token"}, - payload={"query": "Hello, world!"}, - timeout=10.0, - http2=True, - ) - serialized = args.model_dump() - deserialized = RequestArgs.model_validate(serialized) - - for key, value in vars(args).items(): - assert getattr(deserialized, key) == value - - -@pytest.mark.smoke -def test_response_summary_default_initialization(): - summary = ResponseSummary( - value="Hello, world!", - request_args=RequestArgs( - target="http://example.com", - headers={}, - payload={}, - ), - start_time=0.0, - end_time=0.0, - first_iter_time=None, - last_iter_time=None, - ) - assert summary.value == "Hello, world!" - assert summary.request_args.target == "http://example.com" - assert summary.request_args.headers == {} - assert summary.request_args.payload == {} - assert summary.start_time == 0.0 - assert summary.end_time == 0.0 - assert summary.first_iter_time is None - assert summary.last_iter_time is None - assert summary.iterations == 0 - assert summary.request_prompt_tokens is None - assert summary.request_output_tokens is None - assert summary.response_prompt_tokens is None - assert summary.response_output_tokens is None - assert summary.request_id is None - - -@pytest.mark.smoke -def test_response_summary_initialization(): - summary = ResponseSummary( - value="Hello, world!", - request_args=RequestArgs( - target="http://example.com", - headers={}, - payload={}, - ), - start_time=1.0, - end_time=2.0, - iterations=3, - first_iter_time=1.0, - last_iter_time=2.0, - request_prompt_tokens=5, - request_output_tokens=10, - response_prompt_tokens=5, - response_output_tokens=10, - request_id="123", - ) - assert summary.value == "Hello, world!" - assert summary.request_args.target == "http://example.com" - assert summary.request_args.headers == {} - assert summary.request_args.payload == {} - assert summary.start_time == 1.0 - assert summary.end_time == 2.0 - assert summary.iterations == 3 - assert summary.first_iter_time == 1.0 - assert summary.last_iter_time == 2.0 - assert summary.request_prompt_tokens == 5 - assert summary.request_output_tokens == 10 - assert summary.response_prompt_tokens == 5 - assert summary.response_output_tokens == 10 - assert summary.request_id == "123" diff --git a/tests/unit/benchmark/__init__.py b/tests/unit/benchmark/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/tests/unit/benchmark/test_output.py b/tests/unit/benchmark/test_output.py deleted file mode 100644 index 9076834b..00000000 --- a/tests/unit/benchmark/test_output.py +++ /dev/null @@ -1,208 +0,0 @@ -import csv -import json -from pathlib import Path -from unittest.mock import patch - -import pytest -import yaml -from pydantic import ValidationError - -from guidellm.benchmark import ( - GenerativeBenchmarksReport, -) -from guidellm.benchmark.output import GenerativeBenchmarksConsole -from tests.unit.mock_benchmark import mock_generative_benchmark - - -def test_generative_benchmark_initilization(): - report = GenerativeBenchmarksReport() - assert len(report.benchmarks) == 0 - - mock_benchmark = mock_generative_benchmark() - report_with_benchmarks = GenerativeBenchmarksReport(benchmarks=[mock_benchmark]) - assert len(report_with_benchmarks.benchmarks) == 1 - assert report_with_benchmarks.benchmarks[0] == mock_benchmark - - -def test_generative_benchmark_invalid_initilization(): - with pytest.raises(ValidationError): - GenerativeBenchmarksReport(benchmarks="invalid_type") # type: ignore[arg-type] - - -def test_generative_benchmark_marshalling(): - mock_benchmark = mock_generative_benchmark() - report = GenerativeBenchmarksReport(benchmarks=[mock_benchmark]) - - serialized = report.model_dump() - deserialized = GenerativeBenchmarksReport.model_validate(serialized) - deserialized_benchmark = deserialized.benchmarks[0] - - for field in mock_benchmark.model_fields: - assert getattr(mock_benchmark, field) == getattr(deserialized_benchmark, field) - - -def test_file_json(): - mock_benchmark = mock_generative_benchmark() - report = GenerativeBenchmarksReport(benchmarks=[mock_benchmark]) - - mock_path = Path("mock_report.json") - report.save_file(mock_path) - - with mock_path.open("r") as file: - saved_data = json.load(file) - assert saved_data == report.model_dump() - - loaded_report = GenerativeBenchmarksReport.load_file(mock_path) - loaded_benchmark = loaded_report.benchmarks[0] - - for field in mock_benchmark.model_fields: - assert getattr(mock_benchmark, field) == getattr(loaded_benchmark, field) - - mock_path.unlink() - - -def test_file_yaml(): - mock_benchmark = mock_generative_benchmark() - report = GenerativeBenchmarksReport(benchmarks=[mock_benchmark]) - - mock_path = Path("mock_report.yaml") - report.save_file(mock_path) - - with mock_path.open("r") as file: - saved_data = yaml.safe_load(file) - assert saved_data == report.model_dump() - - loaded_report = GenerativeBenchmarksReport.load_file(mock_path) - loaded_benchmark = loaded_report.benchmarks[0] - - for field in mock_benchmark.model_fields: - assert getattr(mock_benchmark, field) == getattr(loaded_benchmark, field) - - mock_path.unlink() - - -def test_file_csv(): - mock_benchmark = mock_generative_benchmark() - report = GenerativeBenchmarksReport(benchmarks=[mock_benchmark]) - - mock_path = Path("mock_report.csv") - report.save_csv(mock_path) - - with mock_path.open("r") as file: - reader = csv.reader(file) - headers = next(reader) - rows = list(reader) - - assert "Type" in headers - assert len(rows) == 1 - - mock_path.unlink() - - -def test_console_benchmarks_profile_str(): - console = GenerativeBenchmarksConsole(enabled=True) - mock_benchmark = mock_generative_benchmark() - console.benchmarks = [mock_benchmark] - assert ( - console.benchmarks_profile_str == "type=synchronous, strategies=['synchronous']" - ) - - -def test_console_benchmarks_args_str(): - console = GenerativeBenchmarksConsole(enabled=True) - mock_benchmark = mock_generative_benchmark() - console.benchmarks = [mock_benchmark] - assert console.benchmarks_args_str == ( - "max_number=None, max_duration=10.0, warmup_number=None, " - "warmup_duration=None, cooldown_number=None, cooldown_duration=None" - ) - - -def test_console_benchmarks_worker_desc_str(): - console = GenerativeBenchmarksConsole(enabled=True) - mock_benchmark = mock_generative_benchmark() - console.benchmarks = [mock_benchmark] - assert console.benchmarks_worker_desc_str == str(mock_benchmark.worker) - - -def test_console_benchmarks_request_loader_desc_str(): - console = GenerativeBenchmarksConsole(enabled=True) - mock_benchmark = mock_generative_benchmark() - console.benchmarks = [mock_benchmark] - assert console.benchmarks_request_loader_desc_str == str( - mock_benchmark.request_loader - ) - - -def test_console_benchmarks_extras_str(): - console = GenerativeBenchmarksConsole(enabled=True) - mock_benchmark = mock_generative_benchmark() - console.benchmarks = [mock_benchmark] - assert console.benchmarks_extras_str == "None" - - -def test_console_print_section_header(): - console = GenerativeBenchmarksConsole(enabled=True) - with patch.object(console.console, "print") as mock_print: - console.print_section_header("Test Header") - mock_print.assert_called_once() - - -def test_console_print_labeled_line(): - console = GenerativeBenchmarksConsole(enabled=True) - with patch.object(console.console, "print") as mock_print: - console.print_labeled_line("Label", "Value") - mock_print.assert_called_once() - - -def test_console_print_line(): - console = GenerativeBenchmarksConsole(enabled=True) - with patch.object(console.console, "print") as mock_print: - console.print_line("Test Line") - mock_print.assert_called_once() - - -def test_console_print_table(): - console = GenerativeBenchmarksConsole(enabled=True) - headers = ["Header1", "Header2"] - rows = [["Row1Col1", "Row1Col2"], ["Row2Col1", "Row2Col2"]] - with ( - patch.object(console, "print_section_header") as mock_header, - patch.object(console, "print_table_divider") as mock_divider, - patch.object(console, "print_table_row") as mock_row, - ): - console.print_table(headers, rows, "Test Table") - mock_header.assert_called_once() - mock_divider.assert_called() - mock_row.assert_called() - - -def test_console_print_benchmarks_metadata(): - console = GenerativeBenchmarksConsole(enabled=True) - mock_benchmark = mock_generative_benchmark() - console.benchmarks = [mock_benchmark] - with ( - patch.object(console, "print_section_header") as mock_header, - patch.object(console, "print_labeled_line") as mock_labeled, - ): - console.print_benchmarks_metadata() - mock_header.assert_called_once() - mock_labeled.assert_called() - - -def test_console_print_benchmarks_info(): - console = GenerativeBenchmarksConsole(enabled=True) - mock_benchmark = mock_generative_benchmark() - console.benchmarks = [mock_benchmark] - with patch.object(console, "print_table") as mock_table: - console.print_benchmarks_info() - mock_table.assert_called_once() - - -def test_console_print_benchmarks_stats(): - console = GenerativeBenchmarksConsole(enabled=True) - mock_benchmark = mock_generative_benchmark() - console.benchmarks = [mock_benchmark] - with patch.object(console, "print_table") as mock_table: - console.print_benchmarks_stats() - mock_table.assert_called_once() diff --git a/tests/unit/conftest.py b/tests/unit/conftest.py deleted file mode 100644 index a0457b6f..00000000 --- a/tests/unit/conftest.py +++ /dev/null @@ -1,195 +0,0 @@ -import json -from collections.abc import AsyncIterable -from typing import Any, Literal, Optional -from unittest.mock import MagicMock, patch - -import httpx -import pytest -import respx - -from guidellm.backend import ResponseSummary, StreamingTextResponse - -from .mock_backend import MockBackend - - -@pytest.fixture -def mock_auto_tokenizer(): - with patch("transformers.AutoTokenizer.from_pretrained") as mock_from_pretrained: - - def _fake_tokenize(text: str) -> list[int]: - tokens = text.split() - return [0] * len(tokens) - - mock_tokenizer = MagicMock() - mock_tokenizer.tokenize = MagicMock(side_effect=_fake_tokenize) - mock_from_pretrained.return_value = mock_tokenizer - yield mock_tokenizer - - -@pytest.fixture -def mock_backend(request): - params = request.param if hasattr(request, "param") else {} - kwargs = {} - - for key in ("model", "target", "iter_delay"): - if key in params: - kwargs[key] = params[key] - - return MockBackend(**kwargs) - - -class MockCompletionsIter(AsyncIterable): - def __init__( - self, - type_: Literal["text", "chat"], - prompt: str, - output_token_count: Optional[int], - target: Optional[str] = None, - model: Optional[str] = None, - iter_delay: Optional[float] = None, - ): - self._type = type_ - self._backend = MockBackend( - model=model, - target=target, - iter_delay=iter_delay, - ) - self._prompt = prompt - self._output_token_count = output_token_count - - async def __aiter__(self): - async for token_iter in ( - self._backend.text_completions( - prompt=self._prompt, output_token_count=self._output_token_count - ) - if self._type == "text" - else self._backend.chat_completions( - content=self._prompt, output_token_count=self._output_token_count - ) - ): - if ( - isinstance(token_iter, StreamingTextResponse) - and token_iter.type_ == "start" - ): - continue - - data: dict[str, Any] - - if isinstance(token_iter, StreamingTextResponse): - if self._type == "text": - data = { - "choices": [ - { - "index": token_iter.iter_count, - "text": token_iter.delta, - } - ] - } - elif self._type == "chat": - data = { - "choices": [ - { - "index": token_iter.iter_count, - "delta": {"content": token_iter.delta}, - } - ] - } - else: - raise ValueError("Invalid type for mock completions") - elif isinstance(token_iter, ResponseSummary): - data = { - "usage": { - "prompt_tokens": ( - len(self._prompt.split()) + self._prompt.count(" ") - ), - "completion_tokens": token_iter.response_output_tokens, - } - } - else: - raise ValueError("Invalid token_iter type") - - yield f"data: {json.dumps(data)}\n".encode() - - yield b"data: [DONE]\n" - - -@pytest.fixture -def httpx_openai_mock(request): - params = request.param if hasattr(request, "param") else {} - model = params.get("model", "mock-model") - target = params.get("target", "http://target.mock") - iter_delay = params.get("iter_delay", None) - - with respx.mock(assert_all_mocked=True, assert_all_called=False) as mock_router: - - async def _mock_completions_response(request) -> AsyncIterable[str]: - headers = request.headers - payload = json.loads(request.content) - - assert headers["Content-Type"] == "application/json" - assert payload["model"] == model - assert payload["stream"] is True - assert payload["stream_options"] == {"include_usage": True} - assert payload["prompt"] is not None - assert len(payload["prompt"]) > 0 - assert payload["max_completion_tokens"] > 0 - assert payload["max_tokens"] > 0 - - return httpx.Response( # type: ignore - 200, - stream=MockCompletionsIter( # type: ignore - type_="text", - prompt=payload["prompt"], - output_token_count=( - payload["max_completion_tokens"] - if payload.get("ignore_eos", False) - else None - ), - target=target, - model=model, - iter_delay=iter_delay, - ), - ) - - async def _mock_chat_completions_response(request): - headers = request.headers - payload = json.loads(request.content) - - assert headers["Content-Type"] == "application/json" - assert payload["model"] == model - assert payload["stream"] is True - assert payload["stream_options"] == {"include_usage": True} - assert payload["messages"] is not None - assert len(payload["messages"]) > 0 - assert payload["max_completion_tokens"] > 0 - assert payload["max_tokens"] > 0 - - return httpx.Response( # type: ignore - 200, - stream=MockCompletionsIter( # type: ignore - type_="chat", - prompt=payload["messages"][0]["content"], - output_token_count=( - payload["max_completion_tokens"] - if payload.get("ignore_eos", False) - else None - ), - target=target, - model=model, - iter_delay=iter_delay, - ), - ) - - mock_router.route(method="GET", path="/v1/models").mock( - return_value=httpx.Response( - 200, json={"data": [{"id": model} if model else {"id": "mock-model"}]} - ) - ) - mock_router.route(method="POST", path="/v1/completions").mock( - side_effect=_mock_completions_response # type: ignore - ) - mock_router.route(method="POST", path="/v1/chat/completions").mock( - side_effect=_mock_chat_completions_response - ) - - yield mock_router diff --git a/tests/unit/mock_backend.py b/tests/unit/mock_backend.py deleted file mode 100644 index f9613ae8..00000000 --- a/tests/unit/mock_backend.py +++ /dev/null @@ -1,178 +0,0 @@ -import asyncio -import random -import time -from collections.abc import AsyncGenerator -from pathlib import Path -from typing import Any, Optional, Union - -from lorem.text import TextLorem # type: ignore -from PIL import Image - -from guidellm.backend import ( - Backend, - RequestArgs, - ResponseSummary, - StreamingTextResponse, -) - - -@Backend.register("mock") # type: ignore -class MockBackend(Backend): - def __init__( - self, - model: Optional[str] = "mock-model", - target: Optional[str] = "mock-target", - iter_delay: Optional[float] = None, - ): - super().__init__(type_="mock") # type: ignore - self._model = model - self._target = target - self._iter_delay = iter_delay - - @property - def target(self) -> str: - return self._target # type: ignore - - @property - def model(self) -> Optional[str]: - return self._model - - @property - def info(self) -> dict[str, Any]: - return {} - - async def prepare_multiprocessing(self): - pass - - async def check_setup(self): - pass - - async def available_models(self) -> list[str]: - return [self.model] # type: ignore - - async def text_completions( # type: ignore - self, - prompt: Union[str, list[str]], - request_id: Optional[str] = None, - prompt_token_count: Optional[int] = None, - output_token_count: Optional[int] = None, - **kwargs, - ) -> AsyncGenerator[Union[StreamingTextResponse, ResponseSummary], None]: - if not isinstance(prompt, str) or not prompt: - raise ValueError("Prompt must be a non-empty string") - - async for response in self._text_prompt_response_generator( - prompt, - request_id, - prompt_token_count, - output_token_count, - ): - yield response - - async def chat_completions( # type: ignore - self, - content: Union[ - str, - list[Union[str, dict[str, Union[str, dict[str, str]]], Path, Image.Image]], - Any, - ], - request_id: Optional[str] = None, - prompt_token_count: Optional[int] = None, - output_token_count: Optional[int] = None, - raw_content: bool = False, - **kwargs, - ) -> AsyncGenerator[Union[StreamingTextResponse, ResponseSummary], None]: - if not isinstance(content, str) or not content: - raise ValueError("Content must be a non-empty string") - - async for response in self._text_prompt_response_generator( - content, - request_id, - prompt_token_count, - output_token_count, - ): - yield response - - async def _text_prompt_response_generator( - self, - prompt: str, - request_id: Optional[str], - prompt_token_count: Optional[int], - output_token_count: Optional[int], - ) -> AsyncGenerator[Union[StreamingTextResponse, ResponseSummary], None]: - tokens = self._get_tokens(output_token_count) - start_time = time.time() - - yield StreamingTextResponse( - type_="start", - value="", - start_time=start_time, - first_iter_time=None, - iter_count=0, - delta="", - time=start_time, - request_id=request_id, - ) - - first_iter_time = None - last_iter_time = None - - for index, token in enumerate(tokens): - if self._iter_delay: - await asyncio.sleep(self._iter_delay) - - if first_iter_time is None: - first_iter_time = time.time() - - yield StreamingTextResponse( - type_="iter", - value="".join(tokens[: index + 1]), - start_time=start_time, - first_iter_time=first_iter_time, - iter_count=index + 1, - delta=token, - time=time.time(), - request_id=request_id, - ) - - last_iter_time = time.time() - - yield ResponseSummary( - value="".join(tokens), - request_args=RequestArgs( - target=self.target, - headers={}, - payload={"prompt": prompt, "output_token_count": output_token_count}, - ), - iterations=len(tokens), - start_time=start_time, - end_time=time.time(), - first_iter_time=first_iter_time, - last_iter_time=last_iter_time, - request_prompt_tokens=prompt_token_count, - request_output_tokens=output_token_count, - response_prompt_tokens=len(prompt.split()) + prompt.count(" "), - response_output_tokens=len(tokens), - request_id=request_id, - ) - - @staticmethod - def _get_tokens(token_count: Optional[int] = None) -> list[str]: - if token_count is None: - token_count = random.randint(8, 512) - - words = TextLorem(srange=(token_count, token_count)).sentence().split() - tokens = [] # type: ignore - - for word in words: - if len(tokens) == token_count - 1: - tokens.append(".") - break - if len(tokens) == token_count - 2: - tokens.append(word) - tokens.append(".") - break - tokens.append(word) - tokens.append(" ") - - return tokens diff --git a/tests/unit/mock_benchmark.py b/tests/unit/mock_benchmark.py deleted file mode 100644 index 81364fa1..00000000 --- a/tests/unit/mock_benchmark.py +++ /dev/null @@ -1,271 +0,0 @@ -from guidellm.benchmark import ( - BenchmarkArgs, - BenchmarkRunStats, - GenerativeBenchmark, - GenerativeTextErrorStats, - GenerativeTextResponseStats, - SynchronousProfile, -) -from guidellm.objects import StatusBreakdown -from guidellm.request import GenerativeRequestLoaderDescription -from guidellm.scheduler import ( - GenerativeRequestsWorkerDescription, - SchedulerRequestInfo, - SynchronousStrategy, -) - -__all__ = ["mock_generative_benchmark"] - - -def mock_generative_benchmark() -> GenerativeBenchmark: - return GenerativeBenchmark.from_stats( - run_id="fa4a92c1-9a1d-4c83-b237-83fcc7971bd3", - successful=[ - GenerativeTextResponseStats( - request_id="181a63e2-dc26-4268-9cfc-2ed9279aae63", - request_type="text_completions", - scheduler_info=SchedulerRequestInfo( - requested=True, - completed=True, - errored=False, - canceled=False, - targeted_start_time=1744728125.203447, - queued_time=1744728125.204123, - dequeued_time=1744728125.2048807, - scheduled_time=1744728125.2048993, - worker_start=1744728125.2049701, - request_start=1744728125.2052872, - request_end=1744728126.7004411, - worker_end=1744728126.701175, - process_id=0, - ), - prompt="such a sacrifice to her advantage as years of gratitude cannot enough acknowledge. By this time she is actually with them! If such goodness does not make her miserable now, she will never deserve to be happy! What a meeting for her, when she first sees my aunt! We must endeavour to forget all that has passed on either side, said Jane I hope and trust they will yet be happy. His consenting to marry her is a proof, I will believe, that he is come to a right way of thinking. Their mutual affection will steady them; and I flatter myself they will settle so quietly, and live in so rational a manner", # noqa: E501 - output=", as to make their long life together very comfortable and very useful. I feel, if they and the honourable Mr. Thorpe, who still lives amongst us, should be all I need, I could perfectly rest happy. Writes to meet them in that kind of obedience which is necessary and honourable, and such", # noqa: E501 - prompt_tokens=128, - output_tokens=64, - start_time=1744728125.2052872, - end_time=1744728126.7004411, - first_token_time=1744728125.2473357, - last_token_time=1744728126.699908, - ), - GenerativeTextResponseStats( - request_id="8a7846d5-7624-420d-a269-831e568a848f", - request_type="text_completions", - scheduler_info=SchedulerRequestInfo( - requested=True, - completed=True, - errored=False, - canceled=False, - targeted_start_time=1744728125.204613, - queued_time=1744728125.2047558, - dequeued_time=1744728126.7025175, - scheduled_time=1744728126.7025256, - worker_start=1744728126.702579, - request_start=1744728126.7027814, - request_end=1744728128.1961868, - worker_end=1744728128.196895, - process_id=0, - ), - prompt="a reconciliation; and, after a little further resistance on the part of his aunt, her resentment gave way, either to her affection for him, or her curiosity to see how his wife conducted herself; and she condescended to wait on them at Pemberley, in spite of that pollution which its woods had received, not merely from the presence of such a mistress, but the visits of her uncle and aunt from the city. With the Gardiners they were always on the most intimate terms. Darcy, as well as Elizabeth, really loved them; and they were both ever sensible of the warmest gratitude towards the persons who,", # noqa: E501 - output=" in their own days of poverty, had been so hotel and hospitable to a young couple leaving Pemberley. Till the size of Mr. Bennet\u2019s salary had been altered, the blessing of their friendship was much more greatly needed by the family than it appeared after that event.\n- Mr. Darcy soon deserved", # noqa: E501 - prompt_tokens=128, - output_tokens=64, - start_time=1744728126.7027814, - end_time=1744728128.1961868, - first_token_time=1744728126.7526379, - last_token_time=1744728128.1956792, - ), - GenerativeTextResponseStats( - request_id="4cde0e6c-4531-4e59-aac1-07bc8b6e4139", - request_type="text_completions", - scheduler_info=SchedulerRequestInfo( - requested=True, - completed=True, - errored=False, - canceled=False, - targeted_start_time=1744728126.7031465, - queued_time=1744728126.7034643, - dequeued_time=1744728128.198447, - scheduled_time=1744728128.1984534, - worker_start=1744728128.198509, - request_start=1744728128.1986883, - request_end=1744728129.6919055, - worker_end=1744728129.692606, - process_id=0, - ), - prompt="struck her, that _she_ was selected from among her sisters as worthy of being the mistress of Hunsford Parsonage, and of assisting to form a quadrille table at Rosings, in the absence of more eligible visitors. The idea soon reached to conviction, as she observed his increasing civilities towards herself, and heard his frequent attempt at a compliment on her wit and vivacity; and though more astonished than gratified herself by this effect of her charms, it was not long before her mother gave her to understand that the probability of their marriage was exceedingly agreeable to _her_. Elizabeth, however, did not choose", # noqa: E501 - output=" to improve this conversation into a prophecy, and her mother would hardly take on herself to announce so important a phenomenon. At last he was to drive to Hunsford from Meryton on Sunday; they staid for an hour at eight o'clock, and the following day appeared to be hung up on the walls of", # noqa: E501 - prompt_tokens=128, - output_tokens=64, - start_time=1744728128.1986883, - end_time=1744728129.6919055, - first_token_time=1744728128.2481627, - last_token_time=1744728129.6914039, - ), - GenerativeTextResponseStats( - request_id="a95b96be-05d4-4130-b0dd-9528c01c9909", - request_type="text_completions", - scheduler_info=SchedulerRequestInfo( - requested=True, - completed=True, - errored=False, - canceled=False, - targeted_start_time=1744728128.1987216, - queued_time=1744728128.1991177, - dequeued_time=1744728129.6953137, - scheduled_time=1744728129.695318, - worker_start=1744728129.695379, - request_start=1744728129.6955585, - request_end=1744728131.187553, - worker_end=1744728131.188169, - process_id=0, - ), - prompt="were comfortable on this subject. Day after day passed away without bringing any other tidings of him than the report which shortly prevailed in Meryton of his coming no more to Netherfield the whole winter; a report which highly incensed Mrs. Bennet, and which she never failed to contradict as a most scandalous falsehood. Even Elizabeth began to fear not that Bingley was indifferent but that his sisters would be successful in keeping him away. Unwilling as she was to admit an idea so destructive to Jane s happiness, and so dishonourable to the stability of her lover, she could not prevent its frequently recurring", # noqa: E501 - output=" during these indefinite disputes; and was often seriously engaged in blaming her sisters for increasing a suspense which might only be caused by their own inattention to a subject of so much moment. Whether she had really made that impression on the s+.ayers, or whether she had merely imagined it, she could decide no farther, for", # noqa: E501 - prompt_tokens=128, - output_tokens=64, - start_time=1744728129.6955585, - end_time=1744728131.187553, - first_token_time=1744728129.7438853, - last_token_time=1744728131.187019, - ), - GenerativeTextResponseStats( - request_id="714b751c-bbfe-4b2a-a0af-7c1bf2c224ae", - request_type="text_completions", - scheduler_info=SchedulerRequestInfo( - requested=True, - completed=True, - errored=False, - canceled=False, - targeted_start_time=1744728129.6975086, - queued_time=1744728129.6978767, - dequeued_time=1744728131.190093, - scheduled_time=1744728131.190101, - worker_start=1744728131.1901798, - request_start=1744728131.1904676, - request_end=1744728132.6833503, - worker_end=1744728132.6839745, - process_id=0, - ), - prompt="? cried Elizabeth, brightening up for a moment. Upon my word, said Mrs. Gardiner, I begin to be of your uncle s opinion. It is really too great a violation of decency, honour, and interest, for him to be guilty of it. I cannot think so very ill of Wickham. Can you, yourself, Lizzie, so wholly give him up, as to believe him capable of it? Not perhaps of neglecting his own interest. But of every other neglect I can believe him capable. If, indeed, it should be so! But I dare not hope it. Why should they not go on", # noqa: E501 - output=" together? This is still a motive incapable of being denied. He has such a faculty of pleasing, and you know how much she likes him. \nQuestion: What made elder sisters the center of their families?\nSometimes early this would be discussed in the family circle, but that was a very exceptional treatment.\nThank you,", # noqa: E501 - prompt_tokens=128, - output_tokens=64, - start_time=1744728131.1904676, - end_time=1744728132.6833503, - first_token_time=1744728131.2394557, - last_token_time=1744728132.6828275, - ), - GenerativeTextResponseStats( - request_id="ef73ae8a-4c8f-4c88-b303-cfff152ce378", - request_type="text_completions", - scheduler_info=SchedulerRequestInfo( - requested=True, - completed=True, - errored=False, - canceled=False, - targeted_start_time=1744728131.1891043, - queued_time=1744728131.1893764, - dequeued_time=1744728132.6859632, - scheduled_time=1744728132.6859682, - worker_start=1744728132.6860242, - request_start=1744728132.6862206, - request_end=1744728134.1805167, - worker_end=1744728134.1813161, - process_id=0, - ), - prompt="was. But her commendation, though costing her some trouble, could by no means satisfy Mr. Collins, and he was very soon obliged to take her Ladyship s praise into his own hands. Sir William stayed only a week at Hunsford; but his visit was long enough to convince him of his daughter s being most comfortably settled, and of her possessing such a husband and such a neighbour as were not often met with. While Sir William was with them, Mr. Collins devoted his mornings to driving him out in his gig, and showing him the country but when he went away, the whole family returned to their usual employments", # noqa: E501 - output=", and the sides of the family in which he was more particularly interested, to their respective places in the establishment. Here Jane was occasionally up as a substitute to her indolent sister, in her matron s stead, but was more frequently left idle, and with her hours of quietness, the unwelcome intrusion", # noqa: E501 - prompt_tokens=128, - output_tokens=64, - start_time=1744728132.6862206, - end_time=1744728134.1805167, - first_token_time=1744728132.7354612, - last_token_time=1744728134.1797993, - ), - ], - errored=[], - incomplete=[ - GenerativeTextErrorStats( - request_id="1b3def04-ca81-4f59-a56c-452a069d91af", - request_type="text_completions", - scheduler_info=SchedulerRequestInfo( - requested=True, - completed=False, - errored=True, - canceled=True, - targeted_start_time=1744728132.686177, - queued_time=1744728132.6866345, - dequeued_time=1744728134.1831052, - scheduled_time=1744728134.1831107, - worker_start=1744728134.183183, - request_start=1744728134.183544, - request_end=1744728135.2031732, - worker_end=1744728135.2033112, - process_id=0, - ), - prompt="is to tempt anyone to our humble abode. Our plain manner of living, our small rooms, and few domestics, and the little we see of the world, must make Hunsford extremely dull to a young lady like yourself; but I hope you will believe us grateful for the condescension, and that we have done everything in our power to prevent you spending your time unpleasantly. Elizabeth was eager with her thanks and assurances of happiness. She had spent six weeks with great enjoyment; and the pleasure of being with Charlotte, and the kind attention she had received, must make _her_ feel the obliged. Mr. Collins", # noqa: E501 - output=", who certainly had an eye to Elizabeth's manner, was glad _he was not to lose the curiosity she had given, and requested her away_ , _for the politeness of her conciliating manner would", # noqa: E501 - prompt_tokens=128, - output_tokens=43, - start_time=1744728134.183544, - end_time=1744728135.2031732, - first_token_time=1744728134.2323751, - last_token_time=1744728135.1950455, - error="TimeoutError: The request timed out before completing.", - ) - ], - args=BenchmarkArgs( - profile=SynchronousProfile(), - strategy_index=0, - strategy=SynchronousStrategy(), - max_number=None, - max_duration=10.0, - warmup_number=None, - warmup_duration=None, - cooldown_number=None, - cooldown_duration=None, - ), - run_stats=BenchmarkRunStats( - start_time=1744728125.0772898, - end_time=1744728135.8407037, - requests_made=StatusBreakdown( - successful=6, - errored=0, - incomplete=1, - total=7, - ), - queued_time_avg=1.2821388585226876, - scheduled_time_delay_avg=7.96999250139509e-6, - scheduled_time_sleep_avg=0.0, - worker_start_delay_avg=6.399835859026228e-5, - worker_time_avg=1.4266603674207414, - worker_start_time_targeted_delay_avg=1.2825865745544434, - request_start_time_delay_avg=0.6414163964135307, - request_start_time_targeted_delay_avg=1.2827096836907523, - request_time_delay_avg=0.0004316908972603934, - request_time_avg=1.426228676523481, - ), - worker=GenerativeRequestsWorkerDescription( - backend_type="openai_http", - backend_target="http://localhost:8000", - backend_model="neuralmagic/Qwen2.5-7B-quantized.w8a8", - backend_info={ - "max_output_tokens": 16384, - "timeout": 300, - "http2": True, - "authorization": False, - "organization": None, - "project": None, - "text_completions_path": "/v1/completions", - "chat_completions_path": "/v1/chat/completions", - }, - ), - requests_loader=GenerativeRequestLoaderDescription( - data='{"prompt_tokens": 128, "output_tokens": 64}', - data_args=None, - processor="neuralmagic/Qwen2.5-7B-quantized.w8a8", - processor_args=None, - ), - extras={}, - ) diff --git a/tests/unit/objects/__init__.py b/tests/unit/objects/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/tests/unit/objects/test_pydantic.py b/tests/unit/objects/test_pydantic.py deleted file mode 100644 index cb7f438f..00000000 --- a/tests/unit/objects/test_pydantic.py +++ /dev/null @@ -1,43 +0,0 @@ -import pytest -from pydantic import computed_field - -from guidellm.objects.pydantic import StandardBaseModel - - -class ExampleModel(StandardBaseModel): - name: str - age: int - - @computed_field # type: ignore[misc] - @property - def computed(self) -> str: - return self.name + " " + str(self.age) - - -@pytest.mark.smoke -def test_standard_base_model_initialization(): - example = ExampleModel(name="John Doe", age=30) - assert example.name == "John Doe" - assert example.age == 30 - assert example.computed == "John Doe 30" - - -@pytest.mark.smoke -def test_standard_base_model_invalid_initialization(): - with pytest.raises(ValueError): - ExampleModel(name="John Doe", age="thirty") # type: ignore[arg-type] - - -@pytest.mark.smoke -def test_standard_base_model_marshalling(): - example = ExampleModel(name="John Doe", age=30) - serialized = example.model_dump() - assert serialized["name"] == "John Doe" - assert serialized["age"] == 30 - assert serialized["computed"] == "John Doe 30" - - serialized["computed"] = "Jane Doe 40" - deserialized = ExampleModel.model_validate(serialized) - assert deserialized.name == "John Doe" - assert deserialized.age == 30 - assert deserialized.computed == "John Doe 30" diff --git a/tests/unit/objects/test_statistics.py b/tests/unit/objects/test_statistics.py deleted file mode 100644 index f3332758..00000000 --- a/tests/unit/objects/test_statistics.py +++ /dev/null @@ -1,693 +0,0 @@ -import math -import time -from typing import Literal - -import numpy as np -import pytest - -from guidellm.objects import ( - DistributionSummary, - Percentiles, - RunningStats, - StatusDistributionSummary, - TimeRunningStats, -) - - -def create_default_percentiles() -> Percentiles: - return Percentiles( - p001=0.1, - p01=1.0, - p05=5.0, - p10=10.0, - p25=25.0, - p75=75.0, - p90=90.0, - p95=95.0, - p99=99.0, - p999=99.9, - ) - - -def create_default_distribution_summary() -> DistributionSummary: - return DistributionSummary( - mean=50.0, - median=50.0, - mode=50.0, - variance=835, - std_dev=math.sqrt(835), - min=0.0, - max=100.0, - count=1001, - total_sum=50050.0, - percentiles=create_default_percentiles(), - ) - - -@pytest.mark.smoke -def test_percentiles_initialization(): - percentiles = create_default_percentiles() - assert percentiles.p001 == 0.1 - assert percentiles.p01 == 1.0 - assert percentiles.p05 == 5.0 - assert percentiles.p10 == 10.0 - assert percentiles.p25 == 25.0 - assert percentiles.p75 == 75.0 - assert percentiles.p90 == 90.0 - assert percentiles.p95 == 95.0 - assert percentiles.p99 == 99.0 - assert percentiles.p999 == 99.9 - - -@pytest.mark.smoke -def test_percentiles_invalid_initialization(): - test_kwargs = { - "p001": 0.1, - "p01": 1.0, - "p05": 5.0, - "p10": 10.0, - "p25": 25.0, - "p75": 75.0, - "p90": 90.0, - "p95": 95.0, - "p99": 99.0, - "p999": 99.9, - } - test_missing_keys = list(test_kwargs.keys()) - - for missing_key in test_missing_keys: - kwargs = {key: val for key, val in test_kwargs.items() if key != missing_key} - with pytest.raises(ValueError): - Percentiles(**kwargs) - - -@pytest.mark.smoke -def test_percentiles_marshalling(): - percentiles = create_default_percentiles() - serialized = percentiles.model_dump() - deserialized = Percentiles.model_validate(serialized) - - for key, value in vars(percentiles).items(): - assert getattr(deserialized, key) == value - - -@pytest.mark.smoke -def test_distribution_summary_initilaization(): - distribution_summary = create_default_distribution_summary() - assert distribution_summary.mean == 50.0 - assert distribution_summary.median == 50.0 - assert distribution_summary.mode == 50.0 - assert distribution_summary.variance == 835 - assert distribution_summary.std_dev == math.sqrt(835) - assert distribution_summary.min == 0.0 - assert distribution_summary.max == 100.0 - assert distribution_summary.count == 1001 - assert distribution_summary.total_sum == 50050.0 - assert distribution_summary.percentiles.p001 == 0.1 - assert distribution_summary.percentiles.p01 == 1.0 - assert distribution_summary.percentiles.p05 == 5.0 - assert distribution_summary.percentiles.p10 == 10.0 - assert distribution_summary.percentiles.p25 == 25.0 - assert distribution_summary.percentiles.p75 == 75.0 - assert distribution_summary.percentiles.p90 == 90.0 - assert distribution_summary.percentiles.p95 == 95.0 - assert distribution_summary.percentiles.p99 == 99.0 - assert distribution_summary.percentiles.p999 == 99.9 - - -@pytest.mark.smoke -def test_distribution_summary_invalid_initialization(): - test_kwargs = { - "mean": 50.0, - "median": 50.0, - "mode": 50.0, - "variance": 835, - "std_dev": math.sqrt(835), - "min": 0.0, - "max": 100.0, - "count": 1001, - "total_sum": 50050.0, - "percentiles": create_default_percentiles(), - } - test_missing_keys = list(test_kwargs.keys()) - for missing_key in test_missing_keys: - kwargs = {key: val for key, val in test_kwargs.items() if key != missing_key} - with pytest.raises(ValueError): - DistributionSummary(**kwargs) # type: ignore[arg-type] - - -@pytest.mark.smoke -def test_distribution_summary_marshalling(): - distribution_summary = create_default_distribution_summary() - serialized = distribution_summary.model_dump() - deserialized = DistributionSummary.model_validate(serialized) - - for key, value in vars(distribution_summary).items(): - assert getattr(deserialized, key) == value - - -@pytest.mark.smoke -def test_distribution_summary_from_distribution_function(): - values = [val / 10.0 for val in range(1001)] - distribution = [(val, 1.0) for val in values] - distribution_summary = DistributionSummary.from_distribution_function(distribution) - assert distribution_summary.mean == pytest.approx(np.mean(values)) - assert distribution_summary.median == pytest.approx(np.median(values)) - assert distribution_summary.mode == 0.0 - assert distribution_summary.variance == pytest.approx(np.var(values, ddof=0)) - assert distribution_summary.std_dev == pytest.approx(np.std(values, ddof=0)) - assert distribution_summary.min == min(values) - assert distribution_summary.max == max(values) - assert distribution_summary.count == len(values) - assert distribution_summary.total_sum == sum(values) - assert distribution_summary.percentiles.p001 == pytest.approx( - np.percentile(values, 0.1) - ) - assert distribution_summary.percentiles.p01 == pytest.approx( - np.percentile(values, 1.0) - ) - assert distribution_summary.percentiles.p05 == pytest.approx( - np.percentile(values, 5.0) - ) - assert distribution_summary.percentiles.p10 == pytest.approx( - np.percentile(values, 10.0) - ) - assert distribution_summary.percentiles.p25 == pytest.approx( - np.percentile(values, 25.0) - ) - assert distribution_summary.percentiles.p75 == pytest.approx( - np.percentile(values, 75.0) - ) - assert distribution_summary.percentiles.p90 == pytest.approx( - np.percentile(values, 90.0) - ) - assert distribution_summary.percentiles.p95 == pytest.approx( - np.percentile(values, 95.0) - ) - assert distribution_summary.percentiles.p99 == pytest.approx( - np.percentile(values, 99.0) - ) - assert distribution_summary.percentiles.p999 == pytest.approx( - np.percentile(values, 99.9) - ) - assert distribution_summary.cumulative_distribution_function is None - - distribution_summary_cdf = DistributionSummary.from_distribution_function( - distribution, include_cdf=True - ) - assert distribution_summary_cdf.cumulative_distribution_function is not None - assert len(distribution_summary_cdf.cumulative_distribution_function) == len(values) - - -def test_distribution_summary_from_values(): - values = [val / 10 for val in range(1001)] - distribution_summary = DistributionSummary.from_values(values) - assert distribution_summary.mean == pytest.approx(np.mean(values)) - assert distribution_summary.median == pytest.approx(np.median(values)) - assert distribution_summary.mode == 0.0 - assert distribution_summary.variance == pytest.approx(np.var(values, ddof=0)) - assert distribution_summary.std_dev == pytest.approx(np.std(values, ddof=0)) - assert distribution_summary.min == min(values) - assert distribution_summary.max == max(values) - assert distribution_summary.count == len(values) - assert distribution_summary.total_sum == sum(values) - assert distribution_summary.percentiles.p001 == pytest.approx( - np.percentile(values, 0.1) - ) - assert distribution_summary.percentiles.p01 == pytest.approx( - np.percentile(values, 1.0) - ) - assert distribution_summary.percentiles.p05 == pytest.approx( - np.percentile(values, 5.0) - ) - assert distribution_summary.percentiles.p10 == pytest.approx( - np.percentile(values, 10.0) - ) - assert distribution_summary.percentiles.p25 == pytest.approx( - np.percentile(values, 25.0) - ) - assert distribution_summary.percentiles.p75 == pytest.approx( - np.percentile(values, 75.0) - ) - assert distribution_summary.percentiles.p90 == pytest.approx( - np.percentile(values, 90.0) - ) - assert distribution_summary.percentiles.p95 == pytest.approx( - np.percentile(values, 95.0) - ) - assert distribution_summary.percentiles.p99 == pytest.approx( - np.percentile(values, 99.0) - ) - assert distribution_summary.percentiles.p999 == pytest.approx( - np.percentile(values, 99.9) - ) - assert distribution_summary.cumulative_distribution_function is None - - distribution_summary_weights = DistributionSummary.from_values( - values, weights=[2] * len(values) - ) - assert distribution_summary_weights.mean == pytest.approx(np.mean(values)) - assert distribution_summary_weights.median == pytest.approx(np.median(values)) - assert distribution_summary_weights.mode == 0.0 - assert distribution_summary_weights.variance == pytest.approx( - np.var(values, ddof=0) - ) - assert distribution_summary_weights.std_dev == pytest.approx(np.std(values, ddof=0)) - assert distribution_summary_weights.min == min(values) - assert distribution_summary_weights.max == max(values) - assert distribution_summary_weights.count == len(values) - assert distribution_summary_weights.total_sum == sum(values) - assert distribution_summary_weights.cumulative_distribution_function is None - - distribution_summary_cdf = DistributionSummary.from_values(values, include_cdf=True) - assert distribution_summary_cdf.cumulative_distribution_function is not None - assert len(distribution_summary_cdf.cumulative_distribution_function) == len(values) - - -def test_distribution_summary_from_request_times_concurrency(): - # create consistent timestamped values matching a rate of 10 per second - requests = [(val / 10, val / 10 + 1) for val in range(10001)] - distribution_summary = DistributionSummary.from_request_times( - requests, distribution_type="concurrency" - ) - assert distribution_summary.mean == pytest.approx(10.0, abs=0.01) - assert distribution_summary.median == pytest.approx(10.0) - assert distribution_summary.mode == 10.0 - assert distribution_summary.variance == pytest.approx(0, abs=0.1) - assert distribution_summary.std_dev == pytest.approx(0, abs=0.3) - assert distribution_summary.min == pytest.approx(1) - assert distribution_summary.max == pytest.approx(10.0) - assert distribution_summary.count == 10 - assert distribution_summary.total_sum == pytest.approx(55.0) - assert distribution_summary.percentiles.p001 == pytest.approx(10, abs=5) - assert distribution_summary.percentiles.p01 == pytest.approx(10) - assert distribution_summary.percentiles.p05 == pytest.approx(10) - assert distribution_summary.percentiles.p10 == pytest.approx(10) - assert distribution_summary.percentiles.p25 == pytest.approx(10) - assert distribution_summary.percentiles.p75 == pytest.approx(10) - assert distribution_summary.percentiles.p90 == pytest.approx(10) - assert distribution_summary.percentiles.p95 == pytest.approx(10) - assert distribution_summary.percentiles.p99 == pytest.approx(10) - assert distribution_summary.percentiles.p999 == pytest.approx(10) - assert distribution_summary.cumulative_distribution_function is None - - distribution_summary_cdf = DistributionSummary.from_request_times( - requests, distribution_type="concurrency", include_cdf=True - ) - assert distribution_summary_cdf.cumulative_distribution_function is not None - assert len(distribution_summary_cdf.cumulative_distribution_function) == 10 - - -def test_distribution_summary_from_request_times_rate(): - # create consistent timestamped values matching a rate of 10 per second - requests = [(val / 10, val / 10 + 1) for val in range(10001)] - distribution_summary = DistributionSummary.from_request_times( - requests, distribution_type="rate" - ) - assert distribution_summary.mean == pytest.approx(10.0, abs=0.01) - assert distribution_summary.median == pytest.approx(10.0) - assert distribution_summary.mode == pytest.approx(10.0) - assert distribution_summary.variance == pytest.approx(0, abs=0.1) - assert distribution_summary.std_dev == pytest.approx(0, abs=0.3) - assert distribution_summary.min == pytest.approx(1.0) - assert distribution_summary.max == pytest.approx(10.0) - assert distribution_summary.count == 12 - assert distribution_summary.total_sum == pytest.approx(111.0) - assert distribution_summary.percentiles.p001 == pytest.approx(10.0, abs=0.5) - assert distribution_summary.percentiles.p01 == pytest.approx(10.0) - assert distribution_summary.percentiles.p05 == pytest.approx(10.0) - assert distribution_summary.percentiles.p10 == pytest.approx(10.0) - assert distribution_summary.percentiles.p25 == pytest.approx(10.0) - assert distribution_summary.percentiles.p75 == pytest.approx(10.0) - assert distribution_summary.percentiles.p90 == pytest.approx(10.0) - assert distribution_summary.percentiles.p95 == pytest.approx(10.0) - assert distribution_summary.percentiles.p99 == pytest.approx(10.0) - assert distribution_summary.percentiles.p999 == pytest.approx(10.0) - assert distribution_summary.cumulative_distribution_function is None - - distribution_summary_cdf = DistributionSummary.from_request_times( - requests, distribution_type="rate", include_cdf=True - ) - assert distribution_summary_cdf.cumulative_distribution_function is not None - assert len(distribution_summary_cdf.cumulative_distribution_function) == 12 - - -def test_distribution_summary_from_iterable_request_times(): - # create consistent timestamped values matching a rate of 10 per second - requests = [(val / 10, val / 10 + 1) for val in range(10001)] - # create 9 iterations for each request with first iter at start + 0.1 - # and spaced at 0.1 seconds apart - first_iter_times = [val / 10 + 0.1 for val in range(10001)] - iter_counts = [9 for _ in range(10001)] - first_iter_counts = [1 for _ in range(10001)] - - distribution_summary = DistributionSummary.from_iterable_request_times( - requests, first_iter_times, iter_counts, first_iter_counts - ) - assert distribution_summary.mean == pytest.approx(90.0, abs=0.1) - assert distribution_summary.median == pytest.approx(80.0) - assert distribution_summary.mode == pytest.approx(80.0) - assert distribution_summary.variance == pytest.approx(704.463, abs=0.001) - assert distribution_summary.std_dev == pytest.approx(26.541, abs=0.001) - assert distribution_summary.min == pytest.approx(0.0) - assert distribution_summary.max == pytest.approx(160.0) - assert distribution_summary.count == 44 - assert distribution_summary.total_sum == pytest.approx(3538.85, abs=0.01) - assert distribution_summary.percentiles.p001 == pytest.approx(80.0) - assert distribution_summary.percentiles.p01 == pytest.approx(80.0) - assert distribution_summary.percentiles.p05 == pytest.approx(80.0) - assert distribution_summary.percentiles.p10 == pytest.approx(80.0) - assert distribution_summary.percentiles.p25 == pytest.approx(80.0) - assert distribution_summary.percentiles.p75 == pytest.approx(80.0) - assert distribution_summary.percentiles.p90 == pytest.approx(160.0) - assert distribution_summary.percentiles.p95 == pytest.approx(160.0) - assert distribution_summary.percentiles.p99 == pytest.approx(160.0) - assert distribution_summary.percentiles.p999 == pytest.approx(160.0) - assert distribution_summary.cumulative_distribution_function is None - - distribution_summary_cdf = DistributionSummary.from_iterable_request_times( - requests, first_iter_times, iter_counts, first_iter_counts, include_cdf=True - ) - assert distribution_summary_cdf.cumulative_distribution_function is not None - assert len(distribution_summary_cdf.cumulative_distribution_function) == 44 - - -def test_status_distribution_summary_initialization(): - status_distribution_summary = StatusDistributionSummary( - total=create_default_distribution_summary(), - successful=create_default_distribution_summary(), - incomplete=create_default_distribution_summary(), - errored=create_default_distribution_summary(), - ) - assert status_distribution_summary.total.mean == 50.0 - assert status_distribution_summary.successful.mean == 50.0 - assert status_distribution_summary.incomplete.mean == 50.0 - assert status_distribution_summary.errored.mean == 50.0 - - -def test_status_distribution_summary_marshalling(): - status_distribution_summary = StatusDistributionSummary( - total=create_default_distribution_summary(), - successful=create_default_distribution_summary(), - incomplete=create_default_distribution_summary(), - errored=create_default_distribution_summary(), - ) - serialized = status_distribution_summary.model_dump() - deserialized = StatusDistributionSummary.model_validate(serialized) - - for key, value in vars(status_distribution_summary).items(): - for child_key, child_value in vars(value).items(): - assert getattr(getattr(deserialized, key), child_key) == child_value - - -def test_status_distribution_summary_from_values(): - value_types: list[Literal["successful", "incomplete", "error"]] = [ - "successful", - "incomplete", - "error", - ] * 1000 - values = [float(val % 3) for val in range(3000)] - status_distribution_summary = StatusDistributionSummary.from_values( - value_types, values - ) - assert status_distribution_summary.total.count == len(values) - assert status_distribution_summary.total.mean == pytest.approx(np.mean(values)) - assert status_distribution_summary.total.cumulative_distribution_function is None - assert status_distribution_summary.successful.mean == pytest.approx( - np.mean( - [val for ind, val in enumerate(values) if value_types[ind] == "successful"] - ) - ) - assert status_distribution_summary.successful.count == len( - [val for ind, val in enumerate(values) if value_types[ind] == "successful"] - ) - assert ( - status_distribution_summary.successful.cumulative_distribution_function is None - ) - assert status_distribution_summary.incomplete.mean == pytest.approx( - np.mean( - [val for ind, val in enumerate(values) if value_types[ind] == "incomplete"] - ) - ) - assert status_distribution_summary.incomplete.count == len( - [val for ind, val in enumerate(values) if value_types[ind] == "incomplete"] - ) - assert ( - status_distribution_summary.incomplete.cumulative_distribution_function is None - ) - assert status_distribution_summary.errored.mean == pytest.approx( - np.mean([val for ind, val in enumerate(values) if value_types[ind] == "error"]) - ) - assert status_distribution_summary.errored.count == len( - [val for ind, val in enumerate(values) if value_types[ind] == "error"] - ) - assert status_distribution_summary.errored.cumulative_distribution_function is None - - status_distribution_summary_cdf = StatusDistributionSummary.from_values( - value_types, values, include_cdf=True - ) - assert ( - status_distribution_summary_cdf.total.cumulative_distribution_function - is not None - ) - assert ( - status_distribution_summary_cdf.successful.cumulative_distribution_function - is not None - ) - assert ( - status_distribution_summary_cdf.incomplete.cumulative_distribution_function - is not None - ) - assert ( - status_distribution_summary_cdf.errored.cumulative_distribution_function - is not None - ) - - -def test_status_distribution_summary_from_request_times(): - request_types: list[Literal["successful", "incomplete", "error"]] = [ - "successful", - "incomplete", - "error", - ] * 1000 - requests = [((val % 3) / 10, (val % 3) / 10 + 1) for val in range(3000)] - status_distribution_summary = StatusDistributionSummary.from_request_times( - request_types, requests, distribution_type="concurrency" - ) - assert status_distribution_summary.total.mean == pytest.approx(2500.0, abs=0.01) - assert status_distribution_summary.total.cumulative_distribution_function is None - assert status_distribution_summary.successful.mean == pytest.approx( - 1000.0, abs=0.01 - ) - assert ( - status_distribution_summary.successful.cumulative_distribution_function is None - ) - assert status_distribution_summary.incomplete.mean == pytest.approx( - 1000.0, abs=0.01 - ) - assert ( - status_distribution_summary.incomplete.cumulative_distribution_function is None - ) - assert status_distribution_summary.errored.mean == pytest.approx(1000.0, abs=0.01) - assert status_distribution_summary.errored.cumulative_distribution_function is None - - status_distribution_summary_cdf = StatusDistributionSummary.from_request_times( - request_types, requests, distribution_type="concurrency", include_cdf=True - ) - assert ( - status_distribution_summary_cdf.total.cumulative_distribution_function - is not None - ) - assert ( - status_distribution_summary_cdf.successful.cumulative_distribution_function - is not None - ) - assert ( - status_distribution_summary_cdf.incomplete.cumulative_distribution_function - is not None - ) - assert ( - status_distribution_summary_cdf.errored.cumulative_distribution_function - is not None - ) - - -def test_status_distribution_summary_from_iterable_request_times(): - request_types: list[Literal["successful", "incomplete", "error"]] = [ - "successful", - "incomplete", - "error", - ] * 1000 - requests = [(val % 3 / 10, val % 3 / 10 + 1) for val in range(3000)] - first_iter_times = [val % 3 / 10 + 0.1 for val in range(3000)] - iter_counts = [9 for _ in range(3000)] - first_iter_counts = [1 for _ in range(3000)] - status_distribution_summary = StatusDistributionSummary.from_iterable_request_times( - request_types, - requests, - first_iter_times, - iter_counts, - first_iter_counts, - ) - assert status_distribution_summary.total.mean == pytest.approx(21666.66, abs=0.01) - assert status_distribution_summary.total.cumulative_distribution_function is None - assert status_distribution_summary.successful.mean == pytest.approx( - 8000.0, abs=0.01 - ) - assert ( - status_distribution_summary.successful.cumulative_distribution_function is None - ) - assert status_distribution_summary.incomplete.mean == pytest.approx( - 8000.0, abs=0.01 - ) - assert ( - status_distribution_summary.incomplete.cumulative_distribution_function is None - ) - assert status_distribution_summary.errored.mean == pytest.approx(8000.0, abs=0.01) - assert status_distribution_summary.errored.cumulative_distribution_function is None - - status_distribution_summary_cdf = ( - StatusDistributionSummary.from_iterable_request_times( - request_types, - requests, - first_iter_times, - iter_counts, - first_iter_counts, - include_cdf=True, - ) - ) - assert ( - status_distribution_summary_cdf.total.cumulative_distribution_function - is not None - ) - assert ( - status_distribution_summary_cdf.successful.cumulative_distribution_function - is not None - ) - assert ( - status_distribution_summary_cdf.incomplete.cumulative_distribution_function - is not None - ) - assert ( - status_distribution_summary_cdf.errored.cumulative_distribution_function - is not None - ) - - -def test_running_stats_initialization(): - running_stats = RunningStats() - assert running_stats.start_time == pytest.approx(time.time(), abs=0.01) - assert running_stats.count == 0 - assert running_stats.total == 0 - assert running_stats.last == 0 - assert running_stats.mean == 0 - assert running_stats.rate == 0 - - -def test_running_stats_marshalling(): - running_stats = RunningStats() - serialized = running_stats.model_dump() - deserialized = RunningStats.model_validate(serialized) - - for key, value in vars(running_stats).items(): - assert getattr(deserialized, key) == value - - -def test_running_stats_update(): - running_stats = RunningStats() - running_stats.update(1) - assert running_stats.count == 1 - assert running_stats.total == 1 - assert running_stats.last == 1 - assert running_stats.mean == 1 - time.sleep(1.0) - assert running_stats.rate == pytest.approx( - 1.0 / (time.time() - running_stats.start_time), abs=0.1 - ) - - running_stats.update(2) - assert running_stats.count == 2 - assert running_stats.total == 3 - assert running_stats.last == 2 - assert running_stats.mean == 1.5 - time.sleep(1) - assert running_stats.rate == pytest.approx( - 3 / (time.time() - running_stats.start_time), abs=0.1 - ) - - -def test_running_stats_add(): - running_stats = RunningStats() - mean = running_stats + 1 - assert mean == 1 - assert mean == running_stats.mean - assert running_stats.count == 1 - assert running_stats.total == 1 - assert running_stats.last == 1 - - -def test_running_stats_iadd(): - running_stats = RunningStats() - running_stats += 1 - assert running_stats.count == 1 - assert running_stats.total == 1 - assert running_stats.last == 1 - assert running_stats.mean == 1 - - -def test_time_running_stats_initialization(): - time_running_stats = TimeRunningStats() - assert time_running_stats.start_time == pytest.approx(time.time(), abs=0.01) - assert time_running_stats.count == 0 - assert time_running_stats.total == 0 - assert time_running_stats.last == 0 - assert time_running_stats.mean == 0 - assert time_running_stats.rate == 0 - assert time_running_stats.total_ms == 0 - assert time_running_stats.last_ms == 0 - assert time_running_stats.mean_ms == 0 - assert time_running_stats.rate_ms == 0 - - -def test_time_running_stats_marshalling(): - time_running_stats = TimeRunningStats() - serialized = time_running_stats.model_dump() - deserialized = TimeRunningStats.model_validate(serialized) - - for key, value in vars(time_running_stats).items(): - assert getattr(deserialized, key) == value - - -def test_time_running_stats_update(): - time_running_stats = TimeRunningStats() - time_running_stats.update(1) - assert time_running_stats.count == 1 - assert time_running_stats.total == 1 - assert time_running_stats.last == 1 - assert time_running_stats.mean == 1 - assert time_running_stats.total_ms == 1000 - assert time_running_stats.last_ms == 1000 - assert time_running_stats.mean_ms == 1000 - time.sleep(1.0) - assert time_running_stats.rate == pytest.approx( - 1.0 / (time.time() - time_running_stats.start_time), abs=0.1 - ) - assert time_running_stats.rate_ms == pytest.approx( - 1000 / (time.time() - time_running_stats.start_time), abs=0.1 - ) - - time_running_stats.update(2) - assert time_running_stats.count == 2 - assert time_running_stats.total == 3 - assert time_running_stats.last == 2 - assert time_running_stats.mean == 1.5 - assert time_running_stats.total_ms == 3000 - assert time_running_stats.last_ms == 2000 - assert time_running_stats.mean_ms == 1500 - time.sleep(1) - assert time_running_stats.rate == pytest.approx( - 3 / (time.time() - time_running_stats.start_time), abs=0.1 - ) - assert time_running_stats.rate_ms == pytest.approx( - 3000 / (time.time() - time_running_stats.start_time), abs=0.1 - ) diff --git a/tests/unit/test_config.py b/tests/unit/test_config.py deleted file mode 100644 index 316f13e4..00000000 --- a/tests/unit/test_config.py +++ /dev/null @@ -1,150 +0,0 @@ -import pytest - -from guidellm.config import ( - DatasetSettings, - Environment, - LoggingSettings, - OpenAISettings, - Settings, - print_config, - reload_settings, - settings, -) - - -@pytest.mark.smoke -def test_default_settings(): - settings = Settings() - assert settings.env == Environment.PROD - assert settings.logging == LoggingSettings() - assert settings.openai == OpenAISettings() - - -@pytest.mark.smoke -def test_settings_from_env_variables(mocker): - mocker.patch.dict( - "os.environ", - { - "GUIDELLM__env": "dev", - "GUIDELLM__logging__disabled": "true", - "GUIDELLM__OPENAI__API_KEY": "test_key", - "GUIDELLM__OPENAI__BASE_URL": "http://test.url", - }, - ) - - settings = Settings() - assert settings.env == Environment.DEV - assert settings.logging.disabled is True - assert settings.openai.api_key == "test_key" - assert settings.openai.base_url == "http://test.url" - - -@pytest.mark.sanity -def test_logging_settings(): - logging_settings = LoggingSettings( - disabled=True, - console_log_level="DEBUG", - log_file="app.log", - log_file_level="ERROR", - ) - assert logging_settings.disabled is True - assert logging_settings.console_log_level == "DEBUG" - assert logging_settings.log_file == "app.log" - assert logging_settings.log_file_level == "ERROR" - - -@pytest.mark.sanity -def test_openai_settings(): - openai_settings = OpenAISettings(api_key="test_api_key", base_url="http://test.api") - assert openai_settings.api_key == "test_api_key" - assert openai_settings.base_url == "http://test.api" - - -@pytest.mark.sanity -def test_generate_env_file(): - settings = Settings() - env_file_content = settings.generate_env_file() - assert "GUIDELLM__LOGGING__DISABLED" in env_file_content - assert "GUIDELLM__OPENAI__API_KEY" in env_file_content - - -@pytest.mark.sanity -def test_reload_settings(mocker): - mocker.patch.dict( - "os.environ", - { - "GUIDELLM__env": "staging", - "GUIDELLM__logging__disabled": "false", - }, - ) - reload_settings() - assert settings.env == Environment.STAGING - assert settings.logging.disabled is False - - -@pytest.mark.sanity -def test_print_config(capsys): - print_config() - captured = capsys.readouterr() - assert "Settings:" in captured.out - assert "GUIDELLM__LOGGING__DISABLED" in captured.out - assert "GUIDELLM__OPENAI__API_KEY" in captured.out - - -@pytest.mark.sanity -def test_dataset_settings_defaults(): - dataset_settings = DatasetSettings() - assert dataset_settings.preferred_data_columns == [ - "prompt", - "instruction", - "input", - "inputs", - "question", - "context", - "text", - "content", - "body", - "data", - ] - assert dataset_settings.preferred_data_splits == [ - "test", - "tst", - "validation", - "val", - "train", - ] - - -@pytest.mark.sanity -def test_openai_settings_defaults(): - openai_settings = OpenAISettings() - assert openai_settings.api_key is None - assert openai_settings.bearer_token is None - assert openai_settings.organization is None - assert openai_settings.project is None - assert openai_settings.base_url == "http://localhost:8000" - assert openai_settings.max_output_tokens == 16384 - - -@pytest.mark.sanity -def test_table_properties_defaults(): - settings = Settings() - assert settings.table_border_char == "=" - assert settings.table_headers_border_char == "-" - assert settings.table_column_separator_char == "|" - - -@pytest.mark.sanity -def test_settings_with_env_variables(mocker): - mocker.patch.dict( - "os.environ", - { - "GUIDELLM__DATASET__PREFERRED_DATA_COLUMNS": '["custom_column"]', - "GUIDELLM__OPENAI__API_KEY": "env_api_key", - "GUIDELLM__TABLE_BORDER_CHAR": "*", - }, - ) - settings = Settings() - assert settings.dataset.preferred_data_columns == ["custom_column"] - assert settings.openai.api_key == "env_api_key" - assert settings.table_border_char == "*" diff --git a/tests/unit/test_logger.py b/tests/unit/test_logger.py deleted file mode 100644 index 53e8b664..00000000 --- a/tests/unit/test_logger.py +++ /dev/null @@ -1,113 +0,0 @@ -from pathlib import Path - -import pytest - -from guidellm import configure_logger, logger -from guidellm.config import LoggingSettings - - -@pytest.fixture(autouse=True) -def reset_logger(): # noqa: PT004 - # Ensure logger is reset before each test - logger.remove() - yield - logger.remove() - - return logger - - -def test_default_logger_settings(capsys): - configure_logger(config=LoggingSettings()) - - # Default settings should log to console with INFO level and no file logging - logger.info("Info message") - logger.debug("Debug message") - logger.warning("Warning message") - logger.error("Error message") - - captured = capsys.readouterr() - assert captured.out.count("Warning message") == 1 - assert captured.out.count("Error message") == 1 - assert "Debug message" not in captured.out - - -def test_configure_logger_console_settings(capsys): - # Test configuring the logger to change console log level - config = LoggingSettings(console_log_level="DEBUG") - configure_logger(config=config) - logger.info("Info message") - logger.debug("Debug message") - - captured = capsys.readouterr() - assert captured.out.count("Info message") == 1 - assert captured.out.count("Debug message") == 1 - - -def test_configure_logger_file_settings(tmp_path): - # Test configuring the logger to log to a file - log_file = tmp_path / "test.log" - config = LoggingSettings(log_file=str(log_file), log_file_level="DEBUG") - configure_logger(config=config) - logger.info("Info message") - logger.debug("Debug message") - - with Path(log_file).open() as f: - log_contents = f.read() - assert log_contents.count('"message": "Info message"') == 1 - assert log_contents.count('"message": "Debug message"') == 1 - - -def test_configure_logger_console_and_file(capsys, tmp_path): - # Test configuring the logger to change both console and file settings - log_file = tmp_path / "test.log" - config = LoggingSettings( - console_log_level="ERROR", - log_file=str(log_file), - log_file_level="INFO", - ) - configure_logger(config=config) - logger.info("Info message") - logger.error("Error message") - - captured = capsys.readouterr() - assert "Info message" not in captured.out - assert captured.out.count("Error message") == 1 - - with Path(log_file).open() as f: - log_contents = f.read() - assert log_contents.count('"message": "Info message"') == 1 - assert log_contents.count('"message": "Error message"') == 1 - - -def test_environment_variable_override(monkeypatch, capsys, tmp_path): - configure_logger( - config=LoggingSettings( - console_log_level="ERROR", - log_file=str(tmp_path / "env_test.log"), - log_file_level="DEBUG", - ), - ) - logger.info("Info message") - logger.error("Error message") - logger.debug("Debug message") - - captured = capsys.readouterr() - assert "Info message" not in captured.out - assert captured.out.count("Error message") == 1 - assert "Debug message" not in captured.out - - with Path(tmp_path / "env_test.log").open() as f: - log_contents = f.read() - assert log_contents.count('"message": "Error message"') == 1 - assert log_contents.count('"message": "Info message"') == 1 - assert log_contents.count('"message": "Debug message"') == 1 - - -def test_logging_disabled(capsys): - configure_logger(config=LoggingSettings(disabled=True)) - logger.info("Info message") - logger.error("Error message") - - captured = capsys.readouterr() - assert not captured.out - assert not captured.err diff --git a/tox.ini b/tox.ini deleted file mode 100644 index 08fc27b9..00000000 --- a/tox.ini +++ /dev/null @@ -1,100 +0,0 @@ -[tox] -min_version = 4.0 -env_list = py38,py39,py310,py311,py312 - - -[testenv] -description = Run all tests -deps = - .[dev] -commands = - pytest tests/ {posargs} - - -[testenv:test-unit] -description = Run unit tests -deps = - .[dev] -commands = - python -m pytest tests/unit {posargs} - - -[testenv:test-integration] -description = Run integration tests -deps = - .[dev] -commands = - python -m pytest tests/integration {posargs} - - -[testenv:test-e2e] -description = Run end-to-end tests -deps = - .[dev] -commands = - python -m pytest tests/e2e {posargs} - - -[testenv:quality] -description = Run all quality checks -deps = - .[dev] -commands = - ruff check - python -m mdformat --check README.md DEVELOPING.md CONTRIBUTING.md CODE_OF_CONDUCT.md docs/ src/ tests/ - -[testenv:style] -description = Run style checks and fixes -deps = - .[dev] -commands = - ruff format - ruff check --fix - python -m mdformat README.md DEVELOPING.md CONTRIBUTING.md CODE_OF_CONDUCT.md docs/ src/ tests/ - - -[testenv:types] -description = Run type checks -deps = - .[dev] -commands = - mypy --check-untyped-defs - - -[testenv:links] -description = Run link checks for root and docs markdown files -deps = - .[dev] -commands = - mkdocs-linkcheck ./ - mkdocs-linkcheck docs/ - - -[testenv:build] -description = Build the project -deps = - build - setuptools - setuptools-git-versioning -setenv = - GUIDELLM_BUILD_TYPE = {env:GUIDELLM_BUILD_TYPE:dev} - GUIDELLM_BUILD_ITERATION = {env:GUIDELLM_BUILD_ITERATION:} -commands = - python -m build - - -[testenv:clean] -description = Clean up build, dist, and cache files -skip_install = true -deps = -commands = - rm -rf build - rm -rf dist - rm -rf *.egg-info - find . -type f -name "*.pyc" -delete - find . -type d -name "__pycache__" -exec rm -r {} + - rm -rf .mypy_cache - rm -rf .pytest_cache - rm -rf .tox - rm -rf .ruff_cache - rm -rf .coverage From 8b2b305507f29ce75dcb57e8b5538d82bbeafd0a Mon Sep 17 00:00:00 2001 From: Mark Kurtz Date: Thu, 15 May 2025 17:17:30 +0000 Subject: [PATCH 3/6] Removed everything with mike 2.1.3 --- .github/workflows/development.yml | 153 -------------------- .github/workflows/main.yml | 103 -------------- .github/workflows/nightly.yml | 124 ---------------- .github/workflows/release-candidate.yml | 117 --------------- .github/workflows/release.yml | 116 --------------- .gitignore | 180 ------------------------ 404.html | 71 ---------- index.html | 69 --------- 8 files changed, 933 deletions(-) delete mode 100644 .github/workflows/development.yml delete mode 100644 .github/workflows/main.yml delete mode 100644 .github/workflows/nightly.yml delete mode 100644 .github/workflows/release-candidate.yml delete mode 100644 .github/workflows/release.yml delete mode 100644 .gitignore delete mode 100644 404.html delete mode 100644 index.html diff --git a/.github/workflows/development.yml b/.github/workflows/development.yml deleted file mode 100644 index 3450c758..00000000 --- a/.github/workflows/development.yml +++ /dev/null @@ -1,153 +0,0 @@ -name: Development - -on: - pull_request: - types: [opened, synchronize, reopened] - -jobs: - quality-checks: - runs-on: ubuntu-latest - strategy: - matrix: - python: ["3.9", "3.13"] - steps: - - uses: actions/checkout@v4 - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: ${{ matrix.python }} - - name: Install dependencies - run: pip install tox - - name: Run quality checks - run: tox -e quality - - type-checks: - runs-on: ubuntu-latest - strategy: - matrix: - python: ["3.9", "3.13"] - steps: - - uses: actions/checkout@v4 - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: ${{ matrix.python }} - - name: Install dependencies - run: pip install tox - - name: Run quality checks - run: tox -e types - - link-checks: - runs-on: ubuntu-latest - strategy: - matrix: - python: ["3.9"] - steps: - - uses: actions/checkout@v4 - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: ${{ matrix.python }} - - name: Install dependencies - run: pip install tox - - name: Run link checks - run: tox -e links - - precommit-checks: - runs-on: ubuntu-latest - strategy: - matrix: - python: ["3.9"] - steps: - - uses: actions/checkout@v4 - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: ${{ matrix.python }} - - name: Install dependencies - run: pip install pre-commit - - name: Run pre-commit checks - run: pre-commit run --all-files - - unit-tests: - runs-on: ubuntu-latest - strategy: - matrix: - python: ["3.9", "3.13"] - steps: - - uses: actions/checkout@v4 - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: ${{ matrix.python }} - - name: Install dependencies - run: pip install tox - - name: Run unit tests - run: tox -e test-unit -- -m "smoke or sanity" - - integration-tests: - runs-on: ubuntu-latest - strategy: - matrix: - python: ["3.9", "3.13"] - steps: - - uses: actions/checkout@v4 - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: ${{ matrix.python }} - - name: Install dependencies - run: pip install tox - - name: Run integration tests - run: tox -e test-integration -- -m smoke - - build: - runs-on: ubuntu-latest - strategy: - matrix: - python: ["3.9"] - steps: - - name: Checkout code - uses: actions/checkout@v4 - with: - fetch-depth: 0 - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: ${{ matrix.python }} - - name: Install dependencies - run: pip install tox - - name: Build the package - run: | - export GUIDELLM_BUILD_TYPE=dev - export GUIDELLM_BUILD_ITERATION=${{ github.event.pull_request.number }} - tox -e build - - name: Upload build artifacts - id: artifact-upload - uses: actions/upload-artifact@v4 - with: - name: build-artifacts - path: dist/* - compression-level: 6 - if-no-files-found: error - retention-days: 30 - - name: Generate GitHub App token - id: app-token - uses: actions/create-github-app-token@v1 - with: - app-id: ${{ secrets.GH_NM_REDHAT_AUTOMATION_APP_ID }} - private-key: ${{ secrets.GH_NM_REDHAT_AUTOMATION_APP_PRIVATE_KEY }} - - name: Comment Install instructions - uses: actions/github-script@v7 - with: - github-token: ${{ steps.app-token.outputs.token }} - script: | - github.rest.issues.createComment({ - issue_number: context.issue.number, - owner: context.repo.owner, - repo: context.repo.repo, - body: `📦 **Build Artifacts Available** - The build artifacts (\`.whl\` and \`.tar.gz\`) have been successfully generated and are available for download: ${{ steps.artifact-upload.outputs.artifact-url }}. - They will be retained for **up to 30 days**. - ` - }) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml deleted file mode 100644 index fcd0a515..00000000 --- a/.github/workflows/main.yml +++ /dev/null @@ -1,103 +0,0 @@ -name: Main - -on: - push: - branches: - - main - -jobs: - quality-checks: - runs-on: ubuntu-latest - strategy: - matrix: - python: ["3.9", "3.10", "3.11", "3.12", "3.13"] - steps: - - uses: actions/checkout@v4 - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: ${{ matrix.python }} - - name: Install dependencies - run: pip install tox - - name: Run quality checks - run: tox -e quality - - type-checks: - runs-on: ubuntu-latest - strategy: - matrix: - python: ["3.9", "3.10", "3.11", "3.12", "3.13"] - steps: - - uses: actions/checkout@v4 - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: ${{ matrix.python }} - - name: Install dependencies - run: pip install tox - - name: Run quality checks - run: tox -e types - - link-checks: - runs-on: ubuntu-latest - strategy: - matrix: - python: ["3.9"] - steps: - - uses: actions/checkout@v4 - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: ${{ matrix.python }} - - name: Install dependencies - run: pip install tox - - name: Run link checks - run: tox -e links - - precommit-checks: - runs-on: ubuntu-latest - strategy: - matrix: - python: ["3.9"] - steps: - - uses: actions/checkout@v4 - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: ${{ matrix.python }} - - name: Install dependencies - run: pip install pre-commit - - name: Run pre-commit checks - run: pre-commit run --all-files - - unit-tests: - runs-on: ubuntu-latest - strategy: - matrix: - python: ["3.9", "3.13"] - steps: - - uses: actions/checkout@v4 - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: ${{ matrix.python }} - - name: Install dependencies - run: pip install tox - - name: Run unit tests - run: tox -e test-unit -- -m "smoke or sanity" - - integration-tests: - runs-on: ubuntu-latest - strategy: - matrix: - python: ["3.9", "3.13"] - steps: - - uses: actions/checkout@v4 - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: ${{ matrix.python }} - - name: Install dependencies - run: pip install tox - - name: Run integration tests - run: tox -e test-integration -- -m smoke diff --git a/.github/workflows/nightly.yml b/.github/workflows/nightly.yml deleted file mode 100644 index 2185d4b6..00000000 --- a/.github/workflows/nightly.yml +++ /dev/null @@ -1,124 +0,0 @@ -name: Nightly - -on: - schedule: - - cron: '0 0 * * *' # Runs at midnight every night - -jobs: - link-checks: - runs-on: ubuntu-latest - strategy: - matrix: - python: ["3.9"] - steps: - - uses: actions/checkout@v4 - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: ${{ matrix.python }} - - name: Install dependencies - run: pip install tox - - name: Run link checks - run: tox -e links - - unit-tests: - runs-on: ubuntu-latest - strategy: - matrix: - python: ["3.9", "3.13"] - steps: - - uses: actions/checkout@v4 - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: ${{ matrix.python }} - - name: Install dependencies - run: pip install tox - - name: Run unit tests - run: tox -e test-unit -- --cov=guidellm --cov-report=term-missing --cov-fail-under=75 - - integration-tests: - runs-on: ubuntu-latest - strategy: - matrix: - python: ["3.9", "3.13"] - steps: - - uses: actions/checkout@v4 - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: ${{ matrix.python }} - - name: Install dependencies - run: pip install tox - - name: Run integration tests - run: tox -e test-integration -- -m "smoke or sanity" - - e2e-tests: - runs-on: ubuntu-latest - strategy: - matrix: - python: ["3.9", "3.13"] - steps: - - uses: actions/checkout@v4 - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: ${{ matrix.python }} - - name: Install dependencies - run: pip install tox - - name: Run integration tests - run: tox -e test-e2e -- -m smoke - - build-and-publish: - needs: [unit-tests, integration-tests, e2e-tests] - runs-on: ubuntu-latest - strategy: - matrix: - python: ["3.9"] - steps: - - name: Checkout code - uses: actions/checkout@v4 - with: - fetch-depth: 0 - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: ${{ matrix.python }} - - name: Install dependencies - run: pip install tox - - name: Build the package - run: | - export GUIDELLM_BUILD_TYPE=nightly - tox -e build - - name: Find wheel artifact - id: find-asset-whl - run: | - echo "::set-output name=asset::$(find dist -name '*.whl')" - - name: Find tar.gz artifact - id: find-asset-targz - run: | - echo "::set-output name=asset::$(find dist -name '*.tar.gz')" - - name: Push wheel to PyPI - uses: neuralmagic/nm-actions/actions/publish-whl@v1.0.0 - with: - username: ${{ secrets.PYPI_PUBLIC_USER }} - password: ${{ secrets.PYPI_PUBLIC_AUTH }} - whl: ${{ steps.find-asset-whl.outputs.asset }} - - name: Push tar.gz to PyPI - uses: neuralmagic/nm-actions/actions/publish-whl@v1.0.0 - with: - username: ${{ secrets.PYPI_PUBLIC_USER }} - password: ${{ secrets.PYPI_PUBLIC_AUTH }} - whl: ${{ steps.find-asset-targz.outputs.asset }} - - name: Upload build artifacts - id: artifact-upload - uses: actions/upload-artifact@v4 - with: - name: nightly-build-artifacts - path: dist/* - compression-level: 6 - if-no-files-found: error - retention-days: 30 - - name: Log artifact location - run: | - echo "Artifacts uploaded to: ${{ steps.artifact-upload.outputs.artifact-url }}" diff --git a/.github/workflows/release-candidate.yml b/.github/workflows/release-candidate.yml deleted file mode 100644 index e5a3ceed..00000000 --- a/.github/workflows/release-candidate.yml +++ /dev/null @@ -1,117 +0,0 @@ -name: Release Candidate - -on: - push: - branches: - - 'release/*' - -jobs: - link-checks: - runs-on: ubuntu-latest - strategy: - matrix: - python: ["3.9"] - steps: - - uses: actions/checkout@v4 - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: ${{ matrix.python }} - - name: Install dependencies - run: pip install tox - - name: Run link checks - run: tox -e links - - unit-tests: - runs-on: ubuntu-latest - strategy: - matrix: - python: ["3.9", "3.10", "3.11", "3.12", "3.13"] - steps: - - uses: actions/checkout@v4 - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: ${{ matrix.python }} - - name: Install dependencies - run: pip install tox - - name: Run unit tests - run: tox -e test-unit - - integration-tests: - runs-on: ubuntu-latest - strategy: - matrix: - python: ["3.9", "3.10", "3.11", "3.12", "3.13"] - steps: - - uses: actions/checkout@v4 - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: ${{ matrix.python }} - - name: Install dependencies - run: pip install tox - - name: Run integration tests - run: tox -e test-integration - - e2e-tests: - runs-on: ubuntu-latest - strategy: - matrix: - python: ["3.9", "3.10", "3.11", "3.12", "3.13"] - steps: - - uses: actions/checkout@v4 - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: ${{ matrix.python }} - - name: Install dependencies - run: pip install tox - - name: Run end-to-end tests - run: tox -e test-e2e - - build-and-publish: - needs: [unit-tests, integration-tests, e2e-tests] - runs-on: ubuntu-latest - strategy: - matrix: - python: ["3.9"] - steps: - - name: Checkout code - uses: actions/checkout@v4 - with: - fetch-depth: 0 - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: ${{ matrix.python }} - - name: Install dependencies - run: pip install tox - - name: Build the package - run: | - export GUIDELLM_BUILD_TYPE=candidate - tox -e build - - name: Upload build artifacts - id: artifact-upload - uses: actions/upload-artifact@v4 - with: - name: release-candidate-artifacts - path: dist/* - compression-level: 6 - if-no-files-found: error - retention-days: 30 - - name: Log artifact location - run: | - echo "Artifacts uploaded to: ${{ steps.artifact-upload.outputs.artifact-url }}" - - name: Push wheel to PyPI - uses: neuralmagic/nm-actions/actions/publish-whl@v1.0.0 - with: - username: ${{ secrets.PYPI_PUBLIC_USER }} - password: ${{ secrets.PYPI_PUBLIC_AUTH }} - whl: $(find dist -name '*.whl') - - name: Push tar.gz to PyPI - uses: neuralmagic/nm-actions/actions/publish-whl@v1.0.0 - with: - username: ${{ secrets.PYPI_PUBLIC_USER }} - password: ${{ secrets.PYPI_PUBLIC_AUTH }} - whl: $(find dist -name '*.tar.gz') diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml deleted file mode 100644 index 5499b525..00000000 --- a/.github/workflows/release.yml +++ /dev/null @@ -1,116 +0,0 @@ -name: Release - -on: - push: - tags: - - 'v*.*.*' - -jobs: - build-and-publish: - runs-on: ubuntu-latest - strategy: - matrix: - python: ["3.9"] - steps: - - name: Checkout code - uses: actions/checkout@v4 - with: - fetch-depth: 0 - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: ${{ matrix.python }} - - name: Install dependencies - run: pip install tox - - name: Build the package - run: | - export GUIDELLM_BUILD_TYPE=release - tox -e build - - name: Upload build artifacts - id: artifact-upload - uses: actions/upload-artifact@v4 - with: - name: release-artifacts - path: dist/* - compression-level: 6 - if-no-files-found: error - retention-days: 90 - - name: Log artifact location - run: | - echo "Artifacts uploaded to: Artifacts uploaded to: ${{ steps.artifact-upload.outputs.artifact-url }}" - - name: Push wheel to PyPI - uses: neuralmagic/nm-actions/actions/publish-whl@v1.0.0 - with: - username: ${{ secrets.PYPI_PUBLIC_USER }} - password: ${{ secrets.PYPI_PUBLIC_AUTH }} - whl: $(find dist -name '*.whl') - - name: Push tar.gz to PyPI - uses: neuralmagic/nm-actions/actions/publish-whl@v1.0.0 - with: - username: ${{ secrets.PYPI_PUBLIC_USER }} - password: ${{ secrets.PYPI_PUBLIC_AUTH }} - whl: $(find dist -name '*.tar.gz') - - link-checks: - runs-on: ubuntu-latest - strategy: - matrix: - python: ["3.9"] - steps: - - uses: actions/checkout@v4 - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: ${{ matrix.python }} - - name: Install dependencies - run: pip install tox - - name: Run link checks - run: tox -e links - - unit-tests: - runs-on: ubuntu-latest - strategy: - matrix: - python: ["3.9", "3.10", "3.11", "3.12", "3.13"] - steps: - - uses: actions/checkout@v4 - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: ${{ matrix.python }} - - name: Install dependencies - run: pip install tox - - name: Run unit tests - run: tox -e test-unit - - integration-tests: - runs-on: ubuntu-latest - strategy: - matrix: - python: ["3.9", "3.10", "3.11", "3.12", "3.13"] - steps: - - uses: actions/checkout@v4 - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: ${{ matrix.python }} - - name: Install dependencies - run: pip install tox - - name: Run integration tests - run: tox -e test-integration - - e2e-tests: - runs-on: ubuntu-latest - strategy: - matrix: - python: ["3.9", "3.10", "3.11", "3.12", "3.13"] - steps: - - uses: actions/checkout@v4 - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: ${{ matrix.python }} - - name: Install dependencies - run: pip install tox - - name: Run end-to-end tests - run: tox -e test-e2e diff --git a/.gitignore b/.gitignore deleted file mode 100644 index d4186ed6..00000000 --- a/.gitignore +++ /dev/null @@ -1,180 +0,0 @@ -# build version files -src/guidellm/version.txt -src/guidellm/version.py - -# Output files -benchmarks.json -benchmarks.yaml -benchmarks.csv - -# Byte-compiled / optimized / DLL files -__pycache__/ -*.py[cod] -*$py.class - -# C extensions -*.so - -# Distribution / packaging -.Python -build/ -develop-eggs/ -dist/ -downloads/ -eggs/ -.eggs/ -lib/ -lib64/ -parts/ -sdist/ -var/ -wheels/ -share/python-wheels/ -*.egg-info/ -.installed.cfg -*.egg -MANIFEST - -# PyInstaller -# Usually these files are written by a python script from a template -# before PyInstaller builds the exe, so as to inject date/other infos into it. -*.manifest -*.spec - -# Installer logs -pip-log.txt -pip-delete-this-directory.txt - -# Unit test / coverage reports -htmlcov/ -.tox/ -.nox/ -.coverage -.coverage.* -.cache -nosetests.xml -coverage.xml -*.cover -*.py,cover -.hypothesis/ -.pytest_cache/ -cover/ - -# Translations -*.mo -*.pot - -# Django stuff: -*.log -local_settings.py -db.sqlite3 -db.sqlite3-journal - -# Flask stuff: -instance/ -.webassets-cache - -# Scrapy stuff: -.scrapy - -# Sphinx documentation -docs/_build/ - -# PyBuilder -.pybuilder/ -target/ - -# Jupyter Notebook -.ipynb_checkpoints - -# IPython -profile_default/ -ipython_config.py - -# pyenv -# For a library or package, you might want to ignore these files since the code is -# intended to run in multiple environments; otherwise, check them in: -# .python-version - -# pipenv -# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. -# However, in case of collaboration, if having platform-specific dependencies or dependencies -# having no cross-platform support, pipenv may install dependencies that don't work, or not -# install all needed dependencies. -#Pipfile.lock - -# poetry -# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. -# This is especially recommended for binary packages to ensure reproducibility, and is more -# commonly ignored for libraries. -# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control -#poetry.lock - -# pdm -# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. -#pdm.lock -# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it -# in version control. -# https://pdm.fming.dev/latest/usage/project/#working-with-version-control -.pdm.toml -.pdm-python -.pdm-build/ - -# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm -__pypackages__/ - -# Celery stuff -celerybeat-schedule -celerybeat.pid - -# SageMath parsed files -*.sage.py - -# Environments -.env -.venv -env/ -venv/ -ENV/ -env.bak/ -venv.bak/ - -# Spyder project settings -.spyderproject -.spyproject - -# Rope project settings -.ropeproject - -# mkdocs documentation -/site - -# mypy -.mypy_cache/ -.dmypy.json -dmypy.json - -# Pyre type checker -.pyre/ - -# pytype static type analyzer -.pytype/ - -# Cython debug symbols -cython_debug/ - -# PyCharm -# JetBrains specific template is maintained in a separate JetBrains.gitignore that can -# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore -# and can be added to the global gitignore or merged into this file. For a more nuclear -# option (not recommended) you can uncomment the following to ignore the entire idea folder. -#.idea/ - - -# MacOS files -.DS_Store - - -# Project specific files -*.json -*.yaml diff --git a/404.html b/404.html deleted file mode 100644 index 08ca73f1..00000000 --- a/404.html +++ /dev/null @@ -1,71 +0,0 @@ - - - - - - Page Not Found | GuideLLM - - - -

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404

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The page you are looking for doesn't exist or has been moved.

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© 2025 Neural Magic | GuideLLM

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- - diff --git a/index.html b/index.html deleted file mode 100644 index 9963684e..00000000 --- a/index.html +++ /dev/null @@ -1,69 +0,0 @@ - - - - - - GuideLLM - Neural Magic - - - -
-

GuideLLM

-

A framework for efficient large language model evaluation and benchmarking

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- -
-

Welcome to GuideLLM

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This is a placeholder for the GuideLLM project documentation site. The actual content will be added soon.

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About GuideLLM

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GuideLLM is a framework developed by Neural Magic for effective evaluation and benchmarking of large language models.

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Resources

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© 2025 Neural Magic | GuideLLM

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c})).subscribe(s=>e.innerHTML=s.join("").replace(/\s/g," ")),r.pipe(b(({mode:s})=>s==="search")).subscribe(s=>{switch(s.type){case"ArrowRight":e.innerText.length&&n.selectionStart===n.value.length&&(n.value=e.innerText);break}}),t.pipe(b(dr),m(({data:s})=>s)).pipe(w(s=>o.next(s)),_(()=>o.complete()),m(()=>({ref:e})))}function ui(e,{index$:t,keyboard$:r}){let o=xe();try{let n=ai(o.search,t),i=Se("search-query",e),a=Se("search-result",e);h(e,"click").pipe(b(({target:p})=>p instanceof Element&&!!p.closest("a"))).subscribe(()=>Je("search",!1)),r.pipe(b(({mode:p})=>p==="search")).subscribe(p=>{let c=Ie();switch(p.type){case"Enter":if(c===i){let l=new Map;for(let f of P(":first-child [href]",a)){let u=f.firstElementChild;l.set(f,parseFloat(u.getAttribute("data-md-score")))}if(l.size){let[[f]]=[...l].sort(([,u],[,d])=>d-u);f.click()}p.claim()}break;case"Escape":case"Tab":Je("search",!1),i.blur();break;case"ArrowUp":case"ArrowDown":if(typeof c=="undefined")i.focus();else{let 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n){let{childNodes:c}=x("span",null,p);s.replaceWith(...Array.from(c))}return{ref:e,nodes:n}}))}function fs(e,{viewport$:t,main$:r}){let o=e.closest(".md-grid"),n=o.offsetTop-o.parentElement.offsetTop;return N([r,t]).pipe(m(([{offset:i,height:a},{offset:{y:s}}])=>(a=a+Math.min(n,Math.max(0,s-i))-n,{height:a,locked:s>=i+n})),K((i,a)=>i.height===a.height&&i.locked===a.locked))}function Zr(e,o){var n=o,{header$:t}=n,r=so(n,["header$"]);let i=R(".md-sidebar__scrollwrap",e),{y:a}=De(i);return C(()=>{let s=new g,p=s.pipe(Z(),ie(!0)),c=s.pipe(Me(0,me));return c.pipe(re(t)).subscribe({next([{height:l},{height:f}]){i.style.height=`${l-2*a}px`,e.style.top=`${f}px`},complete(){i.style.height="",e.style.top=""}}),c.pipe(Ae()).subscribe(()=>{for(let l of P(".md-nav__link--active[href]",e)){if(!l.clientHeight)continue;let f=l.closest(".md-sidebar__scrollwrap");if(typeof f!="undefined"){let u=l.offsetTop-f.offsetTop,{height:d}=ce(f);f.scrollTo({top:u-d/2})}}}),ue(P("label[tabindex]",e)).pipe(ne(l=>h(l,"click").pipe(ve(se),m(()=>l),W(p)))).subscribe(l=>{let f=R(`[id="${l.htmlFor}"]`);R(`[aria-labelledby="${l.id}"]`).setAttribute("aria-expanded",`${f.checked}`)}),fs(e,r).pipe(w(l=>s.next(l)),_(()=>s.complete()),m(l=>$({ref:e},l)))})}function hi(e,t){if(typeof t!="undefined"){let r=`https://api.github.com/repos/${e}/${t}`;return st(je(`${r}/releases/latest`).pipe(de(()=>S),m(o=>({version:o.tag_name})),Ve({})),je(r).pipe(de(()=>S),m(o=>({stars:o.stargazers_count,forks:o.forks_count})),Ve({}))).pipe(m(([o,n])=>$($({},o),n)))}else{let r=`https://api.github.com/users/${e}`;return je(r).pipe(m(o=>({repositories:o.public_repos})),Ve({}))}}function bi(e,t){let r=`https://${e}/api/v4/projects/${encodeURIComponent(t)}`;return st(je(`${r}/releases/permalink/latest`).pipe(de(()=>S),m(({tag_name:o})=>({version:o})),Ve({})),je(r).pipe(de(()=>S),m(({star_count:o,forks_count:n})=>({stars:o,forks:n})),Ve({}))).pipe(m(([o,n])=>$($({},o),n)))}function vi(e){let t=e.match(/^.+github\.com\/([^/]+)\/?([^/]+)?/i);if(t){let[,r,o]=t;return hi(r,o)}if(t=e.match(/^.+?([^/]*gitlab[^/]+)\/(.+?)\/?$/i),t){let[,r,o]=t;return bi(r,o)}return S}var us;function ds(e){return us||(us=C(()=>{let t=__md_get("__source",sessionStorage);if(t)return I(t);if(ae("consent").length){let o=__md_get("__consent");if(!(o&&o.github))return S}return vi(e.href).pipe(w(o=>__md_set("__source",o,sessionStorage)))}).pipe(de(()=>S),b(t=>Object.keys(t).length>0),m(t=>({facts:t})),G(1)))}function gi(e){let t=R(":scope > :last-child",e);return C(()=>{let r=new g;return r.subscribe(({facts:o})=>{t.appendChild(_n(o)),t.classList.add("md-source__repository--active")}),ds(e).pipe(w(o=>r.next(o)),_(()=>r.complete()),m(o=>$({ref:e},o)))})}function hs(e,{viewport$:t,header$:r}){return ge(document.body).pipe(v(()=>mr(e,{header$:r,viewport$:t})),m(({offset:{y:o}})=>({hidden:o>=10})),te("hidden"))}function yi(e,t){return C(()=>{let r=new g;return r.subscribe({next({hidden:o}){e.hidden=o},complete(){e.hidden=!1}}),(B("navigation.tabs.sticky")?I({hidden:!1}):hs(e,t)).pipe(w(o=>r.next(o)),_(()=>r.complete()),m(o=>$({ref:e},o)))})}function bs(e,{viewport$:t,header$:r}){let o=new Map,n=P(".md-nav__link",e);for(let s of n){let p=decodeURIComponent(s.hash.substring(1)),c=fe(`[id="${p}"]`);typeof c!="undefined"&&o.set(s,c)}let i=r.pipe(te("height"),m(({height:s})=>{let p=Se("main"),c=R(":scope > :first-child",p);return s+.8*(c.offsetTop-p.offsetTop)}),pe());return ge(document.body).pipe(te("height"),v(s=>C(()=>{let p=[];return I([...o].reduce((c,[l,f])=>{for(;p.length&&o.get(p[p.length-1]).tagName>=f.tagName;)p.pop();let u=f.offsetTop;for(;!u&&f.parentElement;)f=f.parentElement,u=f.offsetTop;let 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Ei(e,{viewport$:t,header$:r,main$:o,target$:n}){let i=new g,a=i.pipe(Z(),ie(!0));return i.subscribe({next({hidden:s}){e.hidden=s,s?(e.setAttribute("tabindex","-1"),e.blur()):e.removeAttribute("tabindex")},complete(){e.style.top="",e.hidden=!0,e.removeAttribute("tabindex")}}),r.pipe(W(a),te("height")).subscribe(({height:s})=>{e.style.top=`${s+16}px`}),h(e,"click").subscribe(s=>{s.preventDefault(),window.scrollTo({top:0})}),vs(e,{viewport$:t,main$:o,target$:n}).pipe(w(s=>i.next(s)),_(()=>i.complete()),m(s=>$({ref:e},s)))}function wi({document$:e,viewport$:t}){e.pipe(v(()=>P(".md-ellipsis")),ne(r=>tt(r).pipe(W(e.pipe(Ce(1))),b(o=>o),m(()=>r),Te(1))),b(r=>r.offsetWidth{let o=r.innerText,n=r.closest("a")||r;return n.title=o,B("content.tooltips")?mt(n,{viewport$:t}).pipe(W(e.pipe(Ce(1))),_(()=>n.removeAttribute("title"))):S})).subscribe(),B("content.tooltips")&&e.pipe(v(()=>P(".md-status")),ne(r=>mt(r,{viewport$:t}))).subscribe()}function Ti({document$:e,tablet$:t}){e.pipe(v(()=>P(".md-toggle--indeterminate")),w(r=>{r.indeterminate=!0,r.checked=!1}),ne(r=>h(r,"change").pipe(Vr(()=>r.classList.contains("md-toggle--indeterminate")),m(()=>r))),re(t)).subscribe(([r,o])=>{r.classList.remove("md-toggle--indeterminate"),o&&(r.checked=!1)})}function gs(){return/(iPad|iPhone|iPod)/.test(navigator.userAgent)}function Si({document$:e}){e.pipe(v(()=>P("[data-md-scrollfix]")),w(t=>t.removeAttribute("data-md-scrollfix")),b(gs),ne(t=>h(t,"touchstart").pipe(m(()=>t)))).subscribe(t=>{let r=t.scrollTop;r===0?t.scrollTop=1:r+t.offsetHeight===t.scrollHeight&&(t.scrollTop=r-1)})}function Oi({viewport$:e,tablet$:t}){N([Ne("search"),t]).pipe(m(([r,o])=>r&&!o),v(r=>I(r).pipe(Ge(r?400:100))),re(e)).subscribe(([r,{offset:{y:o}}])=>{if(r)document.body.setAttribute("data-md-scrolllock",""),document.body.style.top=`-${o}px`;else{let 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URL("search/search_index.js",eo.base)}`).pipe(m(()=>__index),G(1)):je(new URL("search/search_index.json",eo.base))}document.documentElement.classList.remove("no-js");document.documentElement.classList.add("js");var ot=Go(),Ft=sn(),Ot=ln(Ft),to=an(),Oe=gn(),hr=$t("(min-width: 960px)"),Mi=$t("(min-width: 1220px)"),_i=mn(),eo=xe(),Ai=document.forms.namedItem("search")?ys():Ye,ro=new g;Zn({alert$:ro});var oo=new g;B("navigation.instant")&&oi({location$:Ft,viewport$:Oe,progress$:oo}).subscribe(ot);var Li;((Li=eo.version)==null?void 0:Li.provider)==="mike"&&ci({document$:ot});O(Ft,Ot).pipe(Ge(125)).subscribe(()=>{Je("drawer",!1),Je("search",!1)});to.pipe(b(({mode:e})=>e==="global")).subscribe(e=>{switch(e.type){case"p":case",":let t=fe("link[rel=prev]");typeof t!="undefined"&<(t);break;case"n":case".":let r=fe("link[rel=next]");typeof r!="undefined"&<(r);break;case"Enter":let o=Ie();o instanceof 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rt=Kn(Se("header"),{viewport$:Oe}),jt=ot.pipe(m(()=>Se("main")),v(e=>Gn(e,{viewport$:Oe,header$:rt})),G(1)),xs=O(...ae("consent").map(e=>En(e,{target$:Ot})),...ae("dialog").map(e=>qn(e,{alert$:ro})),...ae("palette").map(e=>Jn(e)),...ae("progress").map(e=>Xn(e,{progress$:oo})),...ae("search").map(e=>ui(e,{index$:Ai,keyboard$:to})),...ae("source").map(e=>gi(e))),Es=C(()=>O(...ae("announce").map(e=>xn(e)),...ae("content").map(e=>Nn(e,{viewport$:Oe,target$:Ot,print$:_i})),...ae("content").map(e=>B("search.highlight")?di(e,{index$:Ai,location$:Ft}):S),...ae("header").map(e=>Yn(e,{viewport$:Oe,header$:rt,main$:jt})),...ae("header-title").map(e=>Bn(e,{viewport$:Oe,header$:rt})),...ae("sidebar").map(e=>e.getAttribute("data-md-type")==="navigation"?zr(Mi,()=>Zr(e,{viewport$:Oe,header$:rt,main$:jt})):zr(hr,()=>Zr(e,{viewport$:Oe,header$:rt,main$:jt}))),...ae("tabs").map(e=>yi(e,{viewport$:Oe,header$:rt})),...ae("toc").map(e=>xi(e,{viewport$:Oe,header$:rt,main$:jt,target$:Ot})),...ae("top").map(e=>Ei(e,{viewport$:Oe,header$:rt,main$:jt,target$:Ot})))),Ci=ot.pipe(v(()=>Es),Re(xs),G(1));Ci.subscribe();window.document$=ot;window.location$=Ft;window.target$=Ot;window.keyboard$=to;window.viewport$=Oe;window.tablet$=hr;window.screen$=Mi;window.print$=_i;window.alert$=ro;window.progress$=oo;window.component$=Ci;})(); 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"src/templates/assets/javascripts/polyfills/index.ts"], + "sourcesContent": ["(function (global, factory) {\n typeof exports === 'object' && typeof module !== 'undefined' ? factory() :\n typeof define === 'function' && define.amd ? define(factory) :\n (factory());\n}(this, (function () { 'use strict';\n\n /**\n * Applies the :focus-visible polyfill at the given scope.\n * A scope in this case is either the top-level Document or a Shadow Root.\n *\n * @param {(Document|ShadowRoot)} scope\n * @see https://github.com/WICG/focus-visible\n */\n function applyFocusVisiblePolyfill(scope) {\n var hadKeyboardEvent = true;\n var hadFocusVisibleRecently = false;\n var hadFocusVisibleRecentlyTimeout = null;\n\n var inputTypesAllowlist = {\n text: true,\n search: true,\n url: true,\n tel: true,\n email: true,\n password: true,\n number: true,\n date: true,\n month: true,\n week: true,\n time: true,\n datetime: true,\n 'datetime-local': true\n };\n\n /**\n * Helper function for legacy browsers and iframes which sometimes focus\n * elements like document, body, and non-interactive SVG.\n * @param {Element} el\n */\n function isValidFocusTarget(el) {\n if (\n el &&\n el !== document &&\n el.nodeName !== 'HTML' &&\n el.nodeName !== 'BODY' &&\n 'classList' in el &&\n 'contains' in el.classList\n ) {\n return true;\n }\n return false;\n }\n\n /**\n * Computes whether the given element should automatically trigger the\n * `focus-visible` class being added, i.e. whether it should always match\n * `:focus-visible` when focused.\n * @param {Element} el\n * @return {boolean}\n */\n function focusTriggersKeyboardModality(el) {\n var type = el.type;\n var tagName = el.tagName;\n\n if (tagName === 'INPUT' && inputTypesAllowlist[type] && !el.readOnly) {\n return true;\n }\n\n if (tagName === 'TEXTAREA' && !el.readOnly) {\n return true;\n }\n\n if (el.isContentEditable) {\n return true;\n }\n\n return false;\n }\n\n /**\n * Add the `focus-visible` class to the given element if it was not added by\n * the author.\n * @param {Element} el\n */\n function addFocusVisibleClass(el) {\n if (el.classList.contains('focus-visible')) {\n return;\n }\n el.classList.add('focus-visible');\n el.setAttribute('data-focus-visible-added', '');\n }\n\n /**\n * Remove the `focus-visible` class from the given element if it was not\n * originally added by the author.\n * @param {Element} el\n */\n function removeFocusVisibleClass(el) {\n if (!el.hasAttribute('data-focus-visible-added')) {\n return;\n }\n el.classList.remove('focus-visible');\n el.removeAttribute('data-focus-visible-added');\n }\n\n /**\n * If the most recent user interaction was via the keyboard;\n * and the key press did not include a meta, alt/option, or control key;\n * then the modality is keyboard. Otherwise, the modality is not keyboard.\n * Apply `focus-visible` to any current active element and keep track\n * of our keyboard modality state with `hadKeyboardEvent`.\n * @param {KeyboardEvent} e\n */\n function onKeyDown(e) {\n if (e.metaKey || e.altKey || e.ctrlKey) {\n return;\n }\n\n if (isValidFocusTarget(scope.activeElement)) {\n addFocusVisibleClass(scope.activeElement);\n }\n\n hadKeyboardEvent = true;\n }\n\n /**\n * If at any point a user clicks with a pointing device, ensure that we change\n * the modality away from keyboard.\n * This avoids the situation where a user presses a key on an already focused\n * element, and then clicks on a different element, focusing it with a\n * pointing device, while we still think we're in keyboard modality.\n * @param {Event} e\n */\n function onPointerDown(e) {\n hadKeyboardEvent = false;\n }\n\n /**\n * On `focus`, add the `focus-visible` class to the target if:\n * - the target received focus as a result of keyboard navigation, or\n * - the event target is an element that will likely require interaction\n * via the keyboard (e.g. a text box)\n * @param {Event} e\n */\n function onFocus(e) {\n // Prevent IE from focusing the document or HTML element.\n if (!isValidFocusTarget(e.target)) {\n return;\n }\n\n if (hadKeyboardEvent || focusTriggersKeyboardModality(e.target)) {\n addFocusVisibleClass(e.target);\n }\n }\n\n /**\n * On `blur`, remove the `focus-visible` class from the target.\n * @param {Event} e\n */\n function onBlur(e) {\n if (!isValidFocusTarget(e.target)) {\n return;\n }\n\n if (\n e.target.classList.contains('focus-visible') ||\n e.target.hasAttribute('data-focus-visible-added')\n ) {\n // To detect a tab/window switch, we look for a blur event followed\n // rapidly by a visibility change.\n // If we don't see a visibility change within 100ms, it's probably a\n // regular focus change.\n hadFocusVisibleRecently = true;\n window.clearTimeout(hadFocusVisibleRecentlyTimeout);\n hadFocusVisibleRecentlyTimeout = window.setTimeout(function() {\n hadFocusVisibleRecently = false;\n }, 100);\n removeFocusVisibleClass(e.target);\n }\n }\n\n /**\n * If the user changes tabs, keep track of whether or not the previously\n * focused element had .focus-visible.\n * @param {Event} e\n */\n function onVisibilityChange(e) {\n if (document.visibilityState === 'hidden') {\n // If the tab becomes active again, the browser will handle calling focus\n // on the element (Safari actually calls it twice).\n // If this tab change caused a blur on an element with focus-visible,\n // re-apply the class when the user switches back to the tab.\n if (hadFocusVisibleRecently) {\n hadKeyboardEvent = true;\n }\n addInitialPointerMoveListeners();\n }\n }\n\n /**\n * Add a group of listeners to detect usage of any pointing devices.\n * These listeners will be added when the polyfill first loads, and anytime\n * the window is blurred, so that they are active when the window regains\n * focus.\n */\n function addInitialPointerMoveListeners() {\n document.addEventListener('mousemove', onInitialPointerMove);\n document.addEventListener('mousedown', onInitialPointerMove);\n document.addEventListener('mouseup', onInitialPointerMove);\n document.addEventListener('pointermove', onInitialPointerMove);\n document.addEventListener('pointerdown', onInitialPointerMove);\n document.addEventListener('pointerup', onInitialPointerMove);\n document.addEventListener('touchmove', onInitialPointerMove);\n document.addEventListener('touchstart', onInitialPointerMove);\n document.addEventListener('touchend', onInitialPointerMove);\n }\n\n function removeInitialPointerMoveListeners() {\n document.removeEventListener('mousemove', onInitialPointerMove);\n document.removeEventListener('mousedown', onInitialPointerMove);\n document.removeEventListener('mouseup', onInitialPointerMove);\n document.removeEventListener('pointermove', onInitialPointerMove);\n document.removeEventListener('pointerdown', onInitialPointerMove);\n document.removeEventListener('pointerup', onInitialPointerMove);\n document.removeEventListener('touchmove', onInitialPointerMove);\n document.removeEventListener('touchstart', onInitialPointerMove);\n document.removeEventListener('touchend', onInitialPointerMove);\n }\n\n /**\n * When the polfyill first loads, assume the user is in keyboard modality.\n * If any event is received from a pointing device (e.g. mouse, pointer,\n * touch), turn off keyboard modality.\n * This accounts for situations where focus enters the page from the URL bar.\n * @param {Event} e\n */\n function onInitialPointerMove(e) {\n // Work around a Safari quirk that fires a mousemove on whenever the\n // window blurs, even if you're tabbing out of the page. \u00AF\\_(\u30C4)_/\u00AF\n if (e.target.nodeName && e.target.nodeName.toLowerCase() === 'html') {\n return;\n }\n\n hadKeyboardEvent = false;\n removeInitialPointerMoveListeners();\n }\n\n // For some kinds of state, we are interested in changes at the global scope\n // only. For example, global pointer input, global key presses and global\n // visibility change should affect the state at every scope:\n document.addEventListener('keydown', onKeyDown, true);\n document.addEventListener('mousedown', onPointerDown, true);\n document.addEventListener('pointerdown', onPointerDown, true);\n document.addEventListener('touchstart', onPointerDown, true);\n document.addEventListener('visibilitychange', onVisibilityChange, true);\n\n addInitialPointerMoveListeners();\n\n // For focus and blur, we specifically care about state changes in the local\n // scope. This is because focus / blur events that originate from within a\n // shadow root are not re-dispatched from the host element if it was already\n // the active element in its own scope:\n scope.addEventListener('focus', onFocus, true);\n scope.addEventListener('blur', onBlur, true);\n\n // We detect that a node is a ShadowRoot by ensuring that it is a\n // DocumentFragment and also has a host property. This check covers native\n // implementation and polyfill implementation transparently. If we only cared\n // about the native implementation, we could just check if the scope was\n // an instance of a ShadowRoot.\n if (scope.nodeType === Node.DOCUMENT_FRAGMENT_NODE && scope.host) {\n // Since a ShadowRoot is a special kind of DocumentFragment, it does not\n // have a root element to add a class to. So, we add this attribute to the\n // host element instead:\n scope.host.setAttribute('data-js-focus-visible', '');\n } else if (scope.nodeType === Node.DOCUMENT_NODE) {\n document.documentElement.classList.add('js-focus-visible');\n document.documentElement.setAttribute('data-js-focus-visible', '');\n }\n }\n\n // It is important to wrap all references to global window and document in\n // these checks to support server-side rendering use cases\n // @see https://github.com/WICG/focus-visible/issues/199\n if (typeof window !== 'undefined' && typeof document !== 'undefined') {\n // Make the polyfill helper globally available. This can be used as a signal\n // to interested libraries that wish to coordinate with the polyfill for e.g.,\n // applying the polyfill to a shadow root:\n window.applyFocusVisiblePolyfill = applyFocusVisiblePolyfill;\n\n // Notify interested libraries of the polyfill's presence, in case the\n // polyfill was loaded lazily:\n var event;\n\n try {\n event = new CustomEvent('focus-visible-polyfill-ready');\n } catch (error) {\n // IE11 does not support using CustomEvent as a constructor directly:\n event = document.createEvent('CustomEvent');\n event.initCustomEvent('focus-visible-polyfill-ready', false, false, {});\n }\n\n window.dispatchEvent(event);\n }\n\n if (typeof document !== 'undefined') {\n // Apply the polyfill to the global document, so that no JavaScript\n // coordination is required to use the polyfill in the top-level document:\n applyFocusVisiblePolyfill(document);\n }\n\n})));\n", "/*!\n * escape-html\n * Copyright(c) 2012-2013 TJ Holowaychuk\n * Copyright(c) 2015 Andreas Lubbe\n * Copyright(c) 2015 Tiancheng \"Timothy\" Gu\n * MIT Licensed\n */\n\n'use strict';\n\n/**\n * Module variables.\n * @private\n */\n\nvar matchHtmlRegExp = /[\"'&<>]/;\n\n/**\n * Module exports.\n * @public\n */\n\nmodule.exports = escapeHtml;\n\n/**\n * Escape special characters in the given string of html.\n *\n * @param {string} string The string to escape for inserting into HTML\n * @return {string}\n * @public\n */\n\nfunction escapeHtml(string) {\n var str = '' + string;\n var match = matchHtmlRegExp.exec(str);\n\n if (!match) {\n return str;\n }\n\n var escape;\n var html = '';\n var index = 0;\n var lastIndex = 0;\n\n for (index = match.index; index < str.length; index++) {\n switch (str.charCodeAt(index)) {\n case 34: // \"\n escape = '"';\n break;\n case 38: // &\n escape = '&';\n break;\n case 39: // '\n escape = ''';\n break;\n case 60: // <\n escape = '<';\n break;\n case 62: // >\n escape = '>';\n break;\n default:\n continue;\n }\n\n if (lastIndex !== index) {\n html += str.substring(lastIndex, index);\n }\n\n lastIndex = index + 1;\n html += escape;\n }\n\n return lastIndex !== index\n ? html + str.substring(lastIndex, index)\n : html;\n}\n", "/*!\n * clipboard.js v2.0.11\n * https://clipboardjs.com/\n *\n * Licensed MIT \u00A9 Zeno Rocha\n */\n(function webpackUniversalModuleDefinition(root, factory) {\n\tif(typeof exports === 'object' && typeof module === 'object')\n\t\tmodule.exports = factory();\n\telse if(typeof define === 'function' && define.amd)\n\t\tdefine([], factory);\n\telse if(typeof exports === 'object')\n\t\texports[\"ClipboardJS\"] = factory();\n\telse\n\t\troot[\"ClipboardJS\"] = factory();\n})(this, function() {\nreturn /******/ (function() { // webpackBootstrap\n/******/ \tvar __webpack_modules__ = ({\n\n/***/ 686:\n/***/ (function(__unused_webpack_module, __webpack_exports__, __webpack_require__) {\n\n\"use strict\";\n\n// EXPORTS\n__webpack_require__.d(__webpack_exports__, {\n \"default\": function() { return /* binding */ clipboard; }\n});\n\n// EXTERNAL MODULE: ./node_modules/tiny-emitter/index.js\nvar tiny_emitter = __webpack_require__(279);\nvar tiny_emitter_default = /*#__PURE__*/__webpack_require__.n(tiny_emitter);\n// EXTERNAL MODULE: ./node_modules/good-listener/src/listen.js\nvar listen = __webpack_require__(370);\nvar listen_default = /*#__PURE__*/__webpack_require__.n(listen);\n// EXTERNAL MODULE: ./node_modules/select/src/select.js\nvar src_select = __webpack_require__(817);\nvar select_default = /*#__PURE__*/__webpack_require__.n(src_select);\n;// CONCATENATED MODULE: ./src/common/command.js\n/**\n * Executes a given operation type.\n * @param {String} type\n * @return {Boolean}\n */\nfunction command(type) {\n try {\n return document.execCommand(type);\n } catch (err) {\n return false;\n }\n}\n;// CONCATENATED MODULE: ./src/actions/cut.js\n\n\n/**\n * Cut action wrapper.\n * @param {String|HTMLElement} target\n * @return {String}\n */\n\nvar ClipboardActionCut = function ClipboardActionCut(target) {\n var selectedText = select_default()(target);\n command('cut');\n return selectedText;\n};\n\n/* harmony default export */ var actions_cut = (ClipboardActionCut);\n;// CONCATENATED MODULE: ./src/common/create-fake-element.js\n/**\n * Creates a fake textarea element with a value.\n * @param {String} value\n * @return {HTMLElement}\n */\nfunction createFakeElement(value) {\n var isRTL = document.documentElement.getAttribute('dir') === 'rtl';\n var fakeElement = document.createElement('textarea'); // Prevent zooming on iOS\n\n fakeElement.style.fontSize = '12pt'; // Reset box model\n\n fakeElement.style.border = '0';\n fakeElement.style.padding = '0';\n fakeElement.style.margin = '0'; // Move element out of screen horizontally\n\n fakeElement.style.position = 'absolute';\n fakeElement.style[isRTL ? 'right' : 'left'] = '-9999px'; // Move element to the same position vertically\n\n var yPosition = window.pageYOffset || document.documentElement.scrollTop;\n fakeElement.style.top = \"\".concat(yPosition, \"px\");\n fakeElement.setAttribute('readonly', '');\n fakeElement.value = value;\n return fakeElement;\n}\n;// CONCATENATED MODULE: ./src/actions/copy.js\n\n\n\n/**\n * Create fake copy action wrapper using a fake element.\n * @param {String} target\n * @param {Object} options\n * @return {String}\n */\n\nvar fakeCopyAction = function fakeCopyAction(value, options) {\n var fakeElement = createFakeElement(value);\n options.container.appendChild(fakeElement);\n var selectedText = select_default()(fakeElement);\n command('copy');\n fakeElement.remove();\n return selectedText;\n};\n/**\n * Copy action wrapper.\n * @param {String|HTMLElement} target\n * @param {Object} options\n * @return {String}\n */\n\n\nvar ClipboardActionCopy = function ClipboardActionCopy(target) {\n var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {\n container: document.body\n };\n var selectedText = '';\n\n if (typeof target === 'string') {\n selectedText = fakeCopyAction(target, options);\n } else if (target instanceof HTMLInputElement && !['text', 'search', 'url', 'tel', 'password'].includes(target === null || target === void 0 ? void 0 : target.type)) {\n // If input type doesn't support `setSelectionRange`. Simulate it. https://developer.mozilla.org/en-US/docs/Web/API/HTMLInputElement/setSelectionRange\n selectedText = fakeCopyAction(target.value, options);\n } else {\n selectedText = select_default()(target);\n command('copy');\n }\n\n return selectedText;\n};\n\n/* harmony default export */ var actions_copy = (ClipboardActionCopy);\n;// CONCATENATED MODULE: ./src/actions/default.js\nfunction _typeof(obj) { \"@babel/helpers - typeof\"; if (typeof Symbol === \"function\" && typeof Symbol.iterator === \"symbol\") { _typeof = function _typeof(obj) { return typeof obj; }; } else { _typeof = function _typeof(obj) { return obj && typeof Symbol === \"function\" && obj.constructor === Symbol && obj !== Symbol.prototype ? \"symbol\" : typeof obj; }; } return _typeof(obj); }\n\n\n\n/**\n * Inner function which performs selection from either `text` or `target`\n * properties and then executes copy or cut operations.\n * @param {Object} options\n */\n\nvar ClipboardActionDefault = function ClipboardActionDefault() {\n var options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};\n // Defines base properties passed from constructor.\n var _options$action = options.action,\n action = _options$action === void 0 ? 'copy' : _options$action,\n container = options.container,\n target = options.target,\n text = options.text; // Sets the `action` to be performed which can be either 'copy' or 'cut'.\n\n if (action !== 'copy' && action !== 'cut') {\n throw new Error('Invalid \"action\" value, use either \"copy\" or \"cut\"');\n } // Sets the `target` property using an element that will be have its content copied.\n\n\n if (target !== undefined) {\n if (target && _typeof(target) === 'object' && target.nodeType === 1) {\n if (action === 'copy' && target.hasAttribute('disabled')) {\n throw new Error('Invalid \"target\" attribute. Please use \"readonly\" instead of \"disabled\" attribute');\n }\n\n if (action === 'cut' && (target.hasAttribute('readonly') || target.hasAttribute('disabled'))) {\n throw new Error('Invalid \"target\" attribute. You can\\'t cut text from elements with \"readonly\" or \"disabled\" attributes');\n }\n } else {\n throw new Error('Invalid \"target\" value, use a valid Element');\n }\n } // Define selection strategy based on `text` property.\n\n\n if (text) {\n return actions_copy(text, {\n container: container\n });\n } // Defines which selection strategy based on `target` property.\n\n\n if (target) {\n return action === 'cut' ? actions_cut(target) : actions_copy(target, {\n container: container\n });\n }\n};\n\n/* harmony default export */ var actions_default = (ClipboardActionDefault);\n;// CONCATENATED MODULE: ./src/clipboard.js\nfunction clipboard_typeof(obj) { \"@babel/helpers - typeof\"; if (typeof Symbol === \"function\" && typeof Symbol.iterator === \"symbol\") { clipboard_typeof = function _typeof(obj) { return typeof obj; }; } else { clipboard_typeof = function _typeof(obj) { return obj && typeof Symbol === \"function\" && obj.constructor === Symbol && obj !== Symbol.prototype ? \"symbol\" : typeof obj; }; } return clipboard_typeof(obj); }\n\nfunction _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError(\"Cannot call a class as a function\"); } }\n\nfunction _defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if (\"value\" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } }\n\nfunction _createClass(Constructor, protoProps, staticProps) { if (protoProps) _defineProperties(Constructor.prototype, protoProps); if (staticProps) _defineProperties(Constructor, staticProps); return Constructor; }\n\nfunction _inherits(subClass, superClass) { if (typeof superClass !== \"function\" && superClass !== null) { throw new TypeError(\"Super expression must either be null or a function\"); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, writable: true, configurable: true } }); if (superClass) _setPrototypeOf(subClass, superClass); }\n\nfunction _setPrototypeOf(o, p) { _setPrototypeOf = Object.setPrototypeOf || function _setPrototypeOf(o, p) { o.__proto__ = p; return o; }; return _setPrototypeOf(o, p); }\n\nfunction _createSuper(Derived) { var hasNativeReflectConstruct = _isNativeReflectConstruct(); return function _createSuperInternal() { var Super = _getPrototypeOf(Derived), result; if (hasNativeReflectConstruct) { var NewTarget = _getPrototypeOf(this).constructor; result = Reflect.construct(Super, arguments, NewTarget); } else { result = Super.apply(this, arguments); } return _possibleConstructorReturn(this, result); }; }\n\nfunction _possibleConstructorReturn(self, call) { if (call && (clipboard_typeof(call) === \"object\" || typeof call === \"function\")) { return call; } return _assertThisInitialized(self); }\n\nfunction _assertThisInitialized(self) { if (self === void 0) { throw new ReferenceError(\"this hasn't been initialised - super() hasn't been called\"); } return self; }\n\nfunction _isNativeReflectConstruct() { if (typeof Reflect === \"undefined\" || !Reflect.construct) return false; if (Reflect.construct.sham) return false; if (typeof Proxy === \"function\") return true; try { Date.prototype.toString.call(Reflect.construct(Date, [], function () {})); return true; } catch (e) { return false; } }\n\nfunction _getPrototypeOf(o) { _getPrototypeOf = Object.setPrototypeOf ? Object.getPrototypeOf : function _getPrototypeOf(o) { return o.__proto__ || Object.getPrototypeOf(o); }; return _getPrototypeOf(o); }\n\n\n\n\n\n\n/**\n * Helper function to retrieve attribute value.\n * @param {String} suffix\n * @param {Element} element\n */\n\nfunction getAttributeValue(suffix, element) {\n var attribute = \"data-clipboard-\".concat(suffix);\n\n if (!element.hasAttribute(attribute)) {\n return;\n }\n\n return element.getAttribute(attribute);\n}\n/**\n * Base class which takes one or more elements, adds event listeners to them,\n * and instantiates a new `ClipboardAction` on each click.\n */\n\n\nvar Clipboard = /*#__PURE__*/function (_Emitter) {\n _inherits(Clipboard, _Emitter);\n\n var _super = _createSuper(Clipboard);\n\n /**\n * @param {String|HTMLElement|HTMLCollection|NodeList} trigger\n * @param {Object} options\n */\n function Clipboard(trigger, options) {\n var _this;\n\n _classCallCheck(this, Clipboard);\n\n _this = _super.call(this);\n\n _this.resolveOptions(options);\n\n _this.listenClick(trigger);\n\n return _this;\n }\n /**\n * Defines if attributes would be resolved using internal setter functions\n * or custom functions that were passed in the constructor.\n * @param {Object} options\n */\n\n\n _createClass(Clipboard, [{\n key: \"resolveOptions\",\n value: function resolveOptions() {\n var options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};\n this.action = typeof options.action === 'function' ? options.action : this.defaultAction;\n this.target = typeof options.target === 'function' ? options.target : this.defaultTarget;\n this.text = typeof options.text === 'function' ? options.text : this.defaultText;\n this.container = clipboard_typeof(options.container) === 'object' ? options.container : document.body;\n }\n /**\n * Adds a click event listener to the passed trigger.\n * @param {String|HTMLElement|HTMLCollection|NodeList} trigger\n */\n\n }, {\n key: \"listenClick\",\n value: function listenClick(trigger) {\n var _this2 = this;\n\n this.listener = listen_default()(trigger, 'click', function (e) {\n return _this2.onClick(e);\n });\n }\n /**\n * Defines a new `ClipboardAction` on each click event.\n * @param {Event} e\n */\n\n }, {\n key: \"onClick\",\n value: function onClick(e) {\n var trigger = e.delegateTarget || e.currentTarget;\n var action = this.action(trigger) || 'copy';\n var text = actions_default({\n action: action,\n container: this.container,\n target: this.target(trigger),\n text: this.text(trigger)\n }); // Fires an event based on the copy operation result.\n\n this.emit(text ? 'success' : 'error', {\n action: action,\n text: text,\n trigger: trigger,\n clearSelection: function clearSelection() {\n if (trigger) {\n trigger.focus();\n }\n\n window.getSelection().removeAllRanges();\n }\n });\n }\n /**\n * Default `action` lookup function.\n * @param {Element} trigger\n */\n\n }, {\n key: \"defaultAction\",\n value: function defaultAction(trigger) {\n return getAttributeValue('action', trigger);\n }\n /**\n * Default `target` lookup function.\n * @param {Element} trigger\n */\n\n }, {\n key: \"defaultTarget\",\n value: function defaultTarget(trigger) {\n var selector = getAttributeValue('target', trigger);\n\n if (selector) {\n return document.querySelector(selector);\n }\n }\n /**\n * Allow fire programmatically a copy action\n * @param {String|HTMLElement} target\n * @param {Object} options\n * @returns Text copied.\n */\n\n }, {\n key: \"defaultText\",\n\n /**\n * Default `text` lookup function.\n * @param {Element} trigger\n */\n value: function defaultText(trigger) {\n return getAttributeValue('text', trigger);\n }\n /**\n * Destroy lifecycle.\n */\n\n }, {\n key: \"destroy\",\n value: function destroy() {\n this.listener.destroy();\n }\n }], [{\n key: \"copy\",\n value: function copy(target) {\n var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {\n container: document.body\n };\n return actions_copy(target, options);\n }\n /**\n * Allow fire programmatically a cut action\n * @param {String|HTMLElement} target\n * @returns Text cutted.\n */\n\n }, {\n key: \"cut\",\n value: function cut(target) {\n return actions_cut(target);\n }\n /**\n * Returns the support of the given action, or all actions if no action is\n * given.\n * @param {String} [action]\n */\n\n }, {\n key: \"isSupported\",\n value: function isSupported() {\n var action = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : ['copy', 'cut'];\n var actions = typeof action === 'string' ? [action] : action;\n var support = !!document.queryCommandSupported;\n actions.forEach(function (action) {\n support = support && !!document.queryCommandSupported(action);\n });\n return support;\n }\n }]);\n\n return Clipboard;\n}((tiny_emitter_default()));\n\n/* harmony default export */ var clipboard = (Clipboard);\n\n/***/ }),\n\n/***/ 828:\n/***/ (function(module) {\n\nvar DOCUMENT_NODE_TYPE = 9;\n\n/**\n * A polyfill for Element.matches()\n */\nif (typeof Element !== 'undefined' && !Element.prototype.matches) {\n var proto = Element.prototype;\n\n proto.matches = proto.matchesSelector ||\n proto.mozMatchesSelector ||\n proto.msMatchesSelector ||\n proto.oMatchesSelector ||\n proto.webkitMatchesSelector;\n}\n\n/**\n * Finds the closest parent that matches a selector.\n *\n * @param {Element} element\n * @param {String} selector\n * @return {Function}\n */\nfunction closest (element, selector) {\n while (element && element.nodeType !== DOCUMENT_NODE_TYPE) {\n if (typeof element.matches === 'function' &&\n element.matches(selector)) {\n return element;\n }\n element = element.parentNode;\n }\n}\n\nmodule.exports = closest;\n\n\n/***/ }),\n\n/***/ 438:\n/***/ (function(module, __unused_webpack_exports, __webpack_require__) {\n\nvar closest = __webpack_require__(828);\n\n/**\n * Delegates event to a selector.\n *\n * @param {Element} element\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @param {Boolean} useCapture\n * @return {Object}\n */\nfunction _delegate(element, selector, type, callback, useCapture) {\n var listenerFn = listener.apply(this, arguments);\n\n element.addEventListener(type, listenerFn, useCapture);\n\n return {\n destroy: function() {\n element.removeEventListener(type, listenerFn, useCapture);\n }\n }\n}\n\n/**\n * Delegates event to a selector.\n *\n * @param {Element|String|Array} [elements]\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @param {Boolean} useCapture\n * @return {Object}\n */\nfunction delegate(elements, selector, type, callback, useCapture) {\n // Handle the regular Element usage\n if (typeof elements.addEventListener === 'function') {\n return _delegate.apply(null, arguments);\n }\n\n // Handle Element-less usage, it defaults to global delegation\n if (typeof type === 'function') {\n // Use `document` as the first parameter, then apply arguments\n // This is a short way to .unshift `arguments` without running into deoptimizations\n return _delegate.bind(null, document).apply(null, arguments);\n }\n\n // Handle Selector-based usage\n if (typeof elements === 'string') {\n elements = document.querySelectorAll(elements);\n }\n\n // Handle Array-like based usage\n return Array.prototype.map.call(elements, function (element) {\n return _delegate(element, selector, type, callback, useCapture);\n });\n}\n\n/**\n * Finds closest match and invokes callback.\n *\n * @param {Element} element\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @return {Function}\n */\nfunction listener(element, selector, type, callback) {\n return function(e) {\n e.delegateTarget = closest(e.target, selector);\n\n if (e.delegateTarget) {\n callback.call(element, e);\n }\n }\n}\n\nmodule.exports = delegate;\n\n\n/***/ }),\n\n/***/ 879:\n/***/ (function(__unused_webpack_module, exports) {\n\n/**\n * Check if argument is a HTML element.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.node = function(value) {\n return value !== undefined\n && value instanceof HTMLElement\n && value.nodeType === 1;\n};\n\n/**\n * Check if argument is a list of HTML elements.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.nodeList = function(value) {\n var type = Object.prototype.toString.call(value);\n\n return value !== undefined\n && (type === '[object NodeList]' || type === '[object HTMLCollection]')\n && ('length' in value)\n && (value.length === 0 || exports.node(value[0]));\n};\n\n/**\n * Check if argument is a string.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.string = function(value) {\n return typeof value === 'string'\n || value instanceof String;\n};\n\n/**\n * Check if argument is a function.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.fn = function(value) {\n var type = Object.prototype.toString.call(value);\n\n return type === '[object Function]';\n};\n\n\n/***/ }),\n\n/***/ 370:\n/***/ (function(module, __unused_webpack_exports, __webpack_require__) {\n\nvar is = __webpack_require__(879);\nvar delegate = __webpack_require__(438);\n\n/**\n * Validates all params and calls the right\n * listener function based on its target type.\n *\n * @param {String|HTMLElement|HTMLCollection|NodeList} target\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listen(target, type, callback) {\n if (!target && !type && !callback) {\n throw new Error('Missing required arguments');\n }\n\n if (!is.string(type)) {\n throw new TypeError('Second argument must be a String');\n }\n\n if (!is.fn(callback)) {\n throw new TypeError('Third argument must be a Function');\n }\n\n if (is.node(target)) {\n return listenNode(target, type, callback);\n }\n else if (is.nodeList(target)) {\n return listenNodeList(target, type, callback);\n }\n else if (is.string(target)) {\n return listenSelector(target, type, callback);\n }\n else {\n throw new TypeError('First argument must be a String, HTMLElement, HTMLCollection, or NodeList');\n }\n}\n\n/**\n * Adds an event listener to a HTML element\n * and returns a remove listener function.\n *\n * @param {HTMLElement} node\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenNode(node, type, callback) {\n node.addEventListener(type, callback);\n\n return {\n destroy: function() {\n node.removeEventListener(type, callback);\n }\n }\n}\n\n/**\n * Add an event listener to a list of HTML elements\n * and returns a remove listener function.\n *\n * @param {NodeList|HTMLCollection} nodeList\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenNodeList(nodeList, type, callback) {\n Array.prototype.forEach.call(nodeList, function(node) {\n node.addEventListener(type, callback);\n });\n\n return {\n destroy: function() {\n Array.prototype.forEach.call(nodeList, function(node) {\n node.removeEventListener(type, callback);\n });\n }\n }\n}\n\n/**\n * Add an event listener to a selector\n * and returns a remove listener function.\n *\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenSelector(selector, type, callback) {\n return delegate(document.body, selector, type, callback);\n}\n\nmodule.exports = listen;\n\n\n/***/ }),\n\n/***/ 817:\n/***/ (function(module) {\n\nfunction select(element) {\n var selectedText;\n\n if (element.nodeName === 'SELECT') {\n element.focus();\n\n selectedText = element.value;\n }\n else if (element.nodeName === 'INPUT' || element.nodeName === 'TEXTAREA') {\n var isReadOnly = element.hasAttribute('readonly');\n\n if (!isReadOnly) {\n element.setAttribute('readonly', '');\n }\n\n element.select();\n element.setSelectionRange(0, element.value.length);\n\n if (!isReadOnly) {\n element.removeAttribute('readonly');\n }\n\n selectedText = element.value;\n }\n else {\n if (element.hasAttribute('contenteditable')) {\n element.focus();\n }\n\n var selection = window.getSelection();\n var range = document.createRange();\n\n range.selectNodeContents(element);\n selection.removeAllRanges();\n selection.addRange(range);\n\n selectedText = selection.toString();\n }\n\n return selectedText;\n}\n\nmodule.exports = select;\n\n\n/***/ }),\n\n/***/ 279:\n/***/ (function(module) {\n\nfunction E () {\n // Keep this empty so it's easier to inherit from\n // (via https://github.com/lipsmack from https://github.com/scottcorgan/tiny-emitter/issues/3)\n}\n\nE.prototype = {\n on: function (name, callback, ctx) {\n var e = this.e || (this.e = {});\n\n (e[name] || (e[name] = [])).push({\n fn: callback,\n ctx: ctx\n });\n\n return this;\n },\n\n once: function (name, callback, ctx) {\n var self = this;\n function listener () {\n self.off(name, listener);\n callback.apply(ctx, arguments);\n };\n\n listener._ = callback\n return this.on(name, listener, ctx);\n },\n\n emit: function (name) {\n var data = [].slice.call(arguments, 1);\n var evtArr = ((this.e || (this.e = {}))[name] || []).slice();\n var i = 0;\n var len = evtArr.length;\n\n for (i; i < len; i++) {\n evtArr[i].fn.apply(evtArr[i].ctx, data);\n }\n\n return this;\n },\n\n off: function (name, callback) {\n var e = this.e || (this.e = {});\n var evts = e[name];\n var liveEvents = [];\n\n if (evts && callback) {\n for (var i = 0, len = evts.length; i < len; i++) {\n if (evts[i].fn !== callback && evts[i].fn._ !== callback)\n liveEvents.push(evts[i]);\n }\n }\n\n // Remove event from queue to prevent memory leak\n // Suggested by https://github.com/lazd\n // Ref: https://github.com/scottcorgan/tiny-emitter/commit/c6ebfaa9bc973b33d110a84a307742b7cf94c953#commitcomment-5024910\n\n (liveEvents.length)\n ? e[name] = liveEvents\n : delete e[name];\n\n return this;\n }\n};\n\nmodule.exports = E;\nmodule.exports.TinyEmitter = E;\n\n\n/***/ })\n\n/******/ \t});\n/************************************************************************/\n/******/ \t// The module cache\n/******/ \tvar __webpack_module_cache__ = {};\n/******/ \t\n/******/ \t// The require function\n/******/ \tfunction __webpack_require__(moduleId) {\n/******/ \t\t// Check if module is in cache\n/******/ \t\tif(__webpack_module_cache__[moduleId]) {\n/******/ \t\t\treturn __webpack_module_cache__[moduleId].exports;\n/******/ \t\t}\n/******/ \t\t// Create a new module (and put it into the cache)\n/******/ \t\tvar module = __webpack_module_cache__[moduleId] = {\n/******/ \t\t\t// no module.id needed\n/******/ \t\t\t// no module.loaded needed\n/******/ \t\t\texports: {}\n/******/ \t\t};\n/******/ \t\n/******/ \t\t// Execute the module function\n/******/ \t\t__webpack_modules__[moduleId](module, module.exports, __webpack_require__);\n/******/ \t\n/******/ \t\t// Return the exports of the module\n/******/ \t\treturn module.exports;\n/******/ \t}\n/******/ \t\n/************************************************************************/\n/******/ \t/* webpack/runtime/compat get default export */\n/******/ \t!function() {\n/******/ \t\t// getDefaultExport function for compatibility with non-harmony modules\n/******/ \t\t__webpack_require__.n = function(module) {\n/******/ \t\t\tvar getter = module && module.__esModule ?\n/******/ \t\t\t\tfunction() { return module['default']; } :\n/******/ \t\t\t\tfunction() { return module; };\n/******/ \t\t\t__webpack_require__.d(getter, { a: getter });\n/******/ \t\t\treturn getter;\n/******/ \t\t};\n/******/ \t}();\n/******/ \t\n/******/ \t/* webpack/runtime/define property getters */\n/******/ \t!function() {\n/******/ \t\t// define getter functions for harmony exports\n/******/ \t\t__webpack_require__.d = function(exports, definition) {\n/******/ \t\t\tfor(var key in definition) {\n/******/ \t\t\t\tif(__webpack_require__.o(definition, key) && !__webpack_require__.o(exports, key)) {\n/******/ \t\t\t\t\tObject.defineProperty(exports, key, { enumerable: true, get: definition[key] });\n/******/ \t\t\t\t}\n/******/ \t\t\t}\n/******/ \t\t};\n/******/ \t}();\n/******/ \t\n/******/ \t/* webpack/runtime/hasOwnProperty shorthand */\n/******/ \t!function() {\n/******/ \t\t__webpack_require__.o = function(obj, prop) { return Object.prototype.hasOwnProperty.call(obj, prop); }\n/******/ \t}();\n/******/ \t\n/************************************************************************/\n/******/ \t// module exports must be returned from runtime so entry inlining is disabled\n/******/ \t// startup\n/******/ \t// Load entry module and return exports\n/******/ \treturn __webpack_require__(686);\n/******/ })()\n.default;\n});", "/*\n * Copyright (c) 2016-2025 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport \"focus-visible\"\n\nimport {\n EMPTY,\n NEVER,\n Observable,\n Subject,\n defer,\n delay,\n filter,\n map,\n merge,\n mergeWith,\n shareReplay,\n switchMap\n} from \"rxjs\"\n\nimport { configuration, feature } from \"./_\"\nimport {\n at,\n getActiveElement,\n getOptionalElement,\n requestJSON,\n setLocation,\n setToggle,\n watchDocument,\n watchKeyboard,\n watchLocation,\n watchLocationTarget,\n watchMedia,\n watchPrint,\n watchScript,\n watchViewport\n} from \"./browser\"\nimport {\n getComponentElement,\n getComponentElements,\n mountAnnounce,\n mountBackToTop,\n mountConsent,\n mountContent,\n mountDialog,\n mountHeader,\n mountHeaderTitle,\n mountPalette,\n mountProgress,\n mountSearch,\n mountSearchHiglight,\n mountSidebar,\n mountSource,\n mountTableOfContents,\n mountTabs,\n watchHeader,\n watchMain\n} from \"./components\"\nimport {\n SearchIndex,\n setupClipboardJS,\n setupInstantNavigation,\n setupVersionSelector\n} from \"./integrations\"\nimport {\n patchEllipsis,\n patchIndeterminate,\n patchScrollfix,\n patchScrolllock\n} from \"./patches\"\nimport \"./polyfills\"\n\n/* ----------------------------------------------------------------------------\n * Functions - @todo refactor\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch search index\n *\n * @returns Search index observable\n */\nfunction fetchSearchIndex(): Observable {\n if (location.protocol === \"file:\") {\n return watchScript(\n `${new URL(\"search/search_index.js\", config.base)}`\n )\n .pipe(\n // @ts-ignore - @todo fix typings\n map(() => __index),\n shareReplay(1)\n )\n } else {\n return requestJSON(\n new URL(\"search/search_index.json\", config.base)\n )\n }\n}\n\n/* ----------------------------------------------------------------------------\n * Application\n * ------------------------------------------------------------------------- */\n\n/* Yay, JavaScript is available */\ndocument.documentElement.classList.remove(\"no-js\")\ndocument.documentElement.classList.add(\"js\")\n\n/* Set up navigation observables and subjects */\nconst document$ = watchDocument()\nconst location$ = watchLocation()\nconst target$ = watchLocationTarget(location$)\nconst keyboard$ = watchKeyboard()\n\n/* Set up media observables */\nconst viewport$ = watchViewport()\nconst tablet$ = watchMedia(\"(min-width: 960px)\")\nconst screen$ = watchMedia(\"(min-width: 1220px)\")\nconst print$ = watchPrint()\n\n/* Retrieve search index, if search is enabled */\nconst config = configuration()\nconst index$ = document.forms.namedItem(\"search\")\n ? fetchSearchIndex()\n : NEVER\n\n/* Set up Clipboard.js integration */\nconst alert$ = new Subject()\nsetupClipboardJS({ alert$ })\n\n/* Set up progress indicator */\nconst progress$ = new Subject()\n\n/* Set up instant navigation, if enabled */\nif (feature(\"navigation.instant\"))\n setupInstantNavigation({ location$, viewport$, progress$ })\n .subscribe(document$)\n\n/* Set up version selector */\nif (config.version?.provider === \"mike\")\n setupVersionSelector({ document$ })\n\n/* Always close drawer and search on navigation */\nmerge(location$, target$)\n .pipe(\n delay(125)\n )\n .subscribe(() => {\n setToggle(\"drawer\", false)\n setToggle(\"search\", false)\n })\n\n/* Set up global keyboard handlers */\nkeyboard$\n .pipe(\n filter(({ mode }) => mode === \"global\")\n )\n .subscribe(key => {\n switch (key.type) {\n\n /* Go to previous page */\n case \"p\":\n case \",\":\n const prev = getOptionalElement(\"link[rel=prev]\")\n if (typeof prev !== \"undefined\")\n setLocation(prev)\n break\n\n /* Go to next page */\n case \"n\":\n case \".\":\n const next = getOptionalElement(\"link[rel=next]\")\n if (typeof next !== \"undefined\")\n setLocation(next)\n break\n\n /* Expand navigation, see https://bit.ly/3ZjG5io */\n case \"Enter\":\n const active = getActiveElement()\n if (active instanceof HTMLLabelElement)\n active.click()\n }\n })\n\n/* Set up patches */\npatchEllipsis({ viewport$, document$ })\npatchIndeterminate({ document$, tablet$ })\npatchScrollfix({ document$ })\npatchScrolllock({ viewport$, tablet$ })\n\n/* Set up header and main area observable */\nconst header$ = watchHeader(getComponentElement(\"header\"), { viewport$ })\nconst main$ = document$\n .pipe(\n map(() => getComponentElement(\"main\")),\n switchMap(el => watchMain(el, { viewport$, header$ })),\n shareReplay(1)\n )\n\n/* Set up control component observables */\nconst control$ = merge(\n\n /* Consent */\n ...getComponentElements(\"consent\")\n .map(el => mountConsent(el, { target$ })),\n\n /* Dialog */\n ...getComponentElements(\"dialog\")\n .map(el => mountDialog(el, { alert$ })),\n\n /* Color palette */\n ...getComponentElements(\"palette\")\n .map(el => mountPalette(el)),\n\n /* Progress bar */\n ...getComponentElements(\"progress\")\n .map(el => mountProgress(el, { progress$ })),\n\n /* Search */\n ...getComponentElements(\"search\")\n .map(el => mountSearch(el, { index$, keyboard$ })),\n\n /* Repository information */\n ...getComponentElements(\"source\")\n .map(el => mountSource(el))\n)\n\n/* Set up content component observables */\nconst content$ = defer(() => merge(\n\n /* Announcement bar */\n ...getComponentElements(\"announce\")\n .map(el => mountAnnounce(el)),\n\n /* Content */\n ...getComponentElements(\"content\")\n .map(el => mountContent(el, { viewport$, target$, print$ })),\n\n /* Search highlighting */\n ...getComponentElements(\"content\")\n .map(el => feature(\"search.highlight\")\n ? mountSearchHiglight(el, { index$, location$ })\n : EMPTY\n ),\n\n /* Header */\n ...getComponentElements(\"header\")\n .map(el => mountHeader(el, { viewport$, header$, main$ })),\n\n /* Header title */\n ...getComponentElements(\"header-title\")\n .map(el => mountHeaderTitle(el, { viewport$, header$ })),\n\n /* Sidebar */\n ...getComponentElements(\"sidebar\")\n .map(el => el.getAttribute(\"data-md-type\") === \"navigation\"\n ? at(screen$, () => mountSidebar(el, { viewport$, header$, main$ }))\n : at(tablet$, () => mountSidebar(el, { viewport$, header$, main$ }))\n ),\n\n /* Navigation tabs */\n ...getComponentElements(\"tabs\")\n .map(el => mountTabs(el, { viewport$, header$ })),\n\n /* Table of contents */\n ...getComponentElements(\"toc\")\n .map(el => mountTableOfContents(el, {\n viewport$, header$, main$, target$\n })),\n\n /* Back-to-top button */\n ...getComponentElements(\"top\")\n .map(el => mountBackToTop(el, { viewport$, header$, main$, target$ }))\n))\n\n/* Set up component observables */\nconst component$ = document$\n .pipe(\n switchMap(() => content$),\n mergeWith(control$),\n shareReplay(1)\n )\n\n/* Subscribe to all components */\ncomponent$.subscribe()\n\n/* ----------------------------------------------------------------------------\n * Exports\n * ------------------------------------------------------------------------- */\n\nwindow.document$ = document$ /* Document observable */\nwindow.location$ = location$ /* Location subject */\nwindow.target$ = target$ /* Location target observable */\nwindow.keyboard$ = keyboard$ /* Keyboard observable */\nwindow.viewport$ = viewport$ /* Viewport observable */\nwindow.tablet$ = tablet$ /* Media tablet observable */\nwindow.screen$ = screen$ /* Media screen observable */\nwindow.print$ = print$ /* Media print observable */\nwindow.alert$ = alert$ /* Alert subject */\nwindow.progress$ = progress$ /* Progress indicator subject */\nwindow.component$ = component$ /* Component observable */\n", "/******************************************************************************\nCopyright (c) Microsoft Corporation.\n\nPermission to use, copy, modify, and/or distribute this software for any\npurpose with or without fee is hereby granted.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH\nREGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY\nAND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT,\nINDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM\nLOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR\nOTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR\nPERFORMANCE OF THIS SOFTWARE.\n***************************************************************************** */\n/* global Reflect, Promise, SuppressedError, Symbol, Iterator */\n\nvar extendStatics = function(d, b) {\n extendStatics = Object.setPrototypeOf ||\n ({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) ||\n function (d, b) { for (var p in b) if (Object.prototype.hasOwnProperty.call(b, p)) d[p] = b[p]; };\n return extendStatics(d, b);\n};\n\nexport function __extends(d, b) {\n if (typeof b !== \"function\" && b !== null)\n throw new TypeError(\"Class extends value \" + String(b) + \" is not a constructor or null\");\n extendStatics(d, b);\n function __() { this.constructor = d; }\n d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());\n}\n\nexport var __assign = function() {\n __assign = Object.assign || function __assign(t) {\n for (var s, i = 1, n = arguments.length; i < n; i++) {\n s = arguments[i];\n for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p)) t[p] = s[p];\n }\n return t;\n }\n return __assign.apply(this, arguments);\n}\n\nexport function __rest(s, e) {\n var t = {};\n for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p) && e.indexOf(p) < 0)\n t[p] = s[p];\n if (s != null && typeof Object.getOwnPropertySymbols === \"function\")\n for (var i = 0, p = Object.getOwnPropertySymbols(s); i < p.length; i++) {\n if (e.indexOf(p[i]) < 0 && Object.prototype.propertyIsEnumerable.call(s, p[i]))\n t[p[i]] = s[p[i]];\n }\n return t;\n}\n\nexport function __decorate(decorators, target, key, desc) {\n var c = arguments.length, r = c < 3 ? target : desc === null ? desc = Object.getOwnPropertyDescriptor(target, key) : desc, d;\n if (typeof Reflect === \"object\" && typeof Reflect.decorate === \"function\") r = Reflect.decorate(decorators, target, key, desc);\n else for (var i = decorators.length - 1; i >= 0; i--) if (d = decorators[i]) r = (c < 3 ? d(r) : c > 3 ? d(target, key, r) : d(target, key)) || r;\n return c > 3 && r && Object.defineProperty(target, key, r), r;\n}\n\nexport function __param(paramIndex, decorator) {\n return function (target, key) { decorator(target, key, paramIndex); }\n}\n\nexport function __esDecorate(ctor, descriptorIn, decorators, contextIn, initializers, extraInitializers) {\n function accept(f) { if (f !== void 0 && typeof f !== \"function\") throw new TypeError(\"Function expected\"); return f; }\n var kind = contextIn.kind, key = kind === \"getter\" ? \"get\" : kind === \"setter\" ? \"set\" : \"value\";\n var target = !descriptorIn && ctor ? contextIn[\"static\"] ? ctor : ctor.prototype : null;\n var descriptor = descriptorIn || (target ? Object.getOwnPropertyDescriptor(target, contextIn.name) : {});\n var _, done = false;\n for (var i = decorators.length - 1; i >= 0; i--) {\n var context = {};\n for (var p in contextIn) context[p] = p === \"access\" ? {} : contextIn[p];\n for (var p in contextIn.access) context.access[p] = contextIn.access[p];\n context.addInitializer = function (f) { if (done) throw new TypeError(\"Cannot add initializers after decoration has completed\"); extraInitializers.push(accept(f || null)); };\n var result = (0, decorators[i])(kind === \"accessor\" ? { get: descriptor.get, set: descriptor.set } : descriptor[key], context);\n if (kind === \"accessor\") {\n if (result === void 0) continue;\n if (result === null || typeof result !== \"object\") throw new TypeError(\"Object expected\");\n if (_ = accept(result.get)) descriptor.get = _;\n if (_ = accept(result.set)) descriptor.set = _;\n if (_ = accept(result.init)) initializers.unshift(_);\n }\n else if (_ = accept(result)) {\n if (kind === \"field\") initializers.unshift(_);\n else descriptor[key] = _;\n }\n }\n if (target) Object.defineProperty(target, contextIn.name, descriptor);\n done = true;\n};\n\nexport function __runInitializers(thisArg, initializers, value) {\n var useValue = arguments.length > 2;\n for (var i = 0; i < initializers.length; i++) {\n value = useValue ? initializers[i].call(thisArg, value) : initializers[i].call(thisArg);\n }\n return useValue ? value : void 0;\n};\n\nexport function __propKey(x) {\n return typeof x === \"symbol\" ? x : \"\".concat(x);\n};\n\nexport function __setFunctionName(f, name, prefix) {\n if (typeof name === \"symbol\") name = name.description ? \"[\".concat(name.description, \"]\") : \"\";\n return Object.defineProperty(f, \"name\", { configurable: true, value: prefix ? \"\".concat(prefix, \" \", name) : name });\n};\n\nexport function __metadata(metadataKey, metadataValue) {\n if (typeof Reflect === \"object\" && typeof Reflect.metadata === \"function\") return Reflect.metadata(metadataKey, metadataValue);\n}\n\nexport function __awaiter(thisArg, _arguments, P, generator) {\n function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); }\n return new (P || (P = Promise))(function (resolve, reject) {\n function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }\n function rejected(value) { try { step(generator[\"throw\"](value)); } catch (e) { reject(e); } }\n function step(result) { result.done ? resolve(result.value) : adopt(result.value).then(fulfilled, rejected); }\n step((generator = generator.apply(thisArg, _arguments || [])).next());\n });\n}\n\nexport function __generator(thisArg, body) {\n var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g = Object.create((typeof Iterator === \"function\" ? Iterator : Object).prototype);\n return g.next = verb(0), g[\"throw\"] = verb(1), g[\"return\"] = verb(2), typeof Symbol === \"function\" && (g[Symbol.iterator] = function() { return this; }), g;\n function verb(n) { return function (v) { return step([n, v]); }; }\n function step(op) {\n if (f) throw new TypeError(\"Generator is already executing.\");\n while (g && (g = 0, op[0] && (_ = 0)), _) try {\n if (f = 1, y && (t = op[0] & 2 ? y[\"return\"] : op[0] ? y[\"throw\"] || ((t = y[\"return\"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;\n if (y = 0, t) op = [op[0] & 2, t.value];\n switch (op[0]) {\n case 0: case 1: t = op; break;\n case 4: _.label++; return { value: op[1], done: false };\n case 5: _.label++; y = op[1]; op = [0]; continue;\n case 7: op = _.ops.pop(); _.trys.pop(); continue;\n default:\n if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }\n if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }\n if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }\n if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }\n if (t[2]) _.ops.pop();\n _.trys.pop(); continue;\n }\n op = body.call(thisArg, _);\n } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }\n if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };\n }\n}\n\nexport var __createBinding = Object.create ? (function(o, m, k, k2) {\n if (k2 === undefined) k2 = k;\n var desc = Object.getOwnPropertyDescriptor(m, k);\n if (!desc || (\"get\" in desc ? !m.__esModule : desc.writable || desc.configurable)) {\n desc = { enumerable: true, get: function() { return m[k]; } };\n }\n Object.defineProperty(o, k2, desc);\n}) : (function(o, m, k, k2) {\n if (k2 === undefined) k2 = k;\n o[k2] = m[k];\n});\n\nexport function __exportStar(m, o) {\n for (var p in m) if (p !== \"default\" && !Object.prototype.hasOwnProperty.call(o, p)) __createBinding(o, m, p);\n}\n\nexport function __values(o) {\n var s = typeof Symbol === \"function\" && Symbol.iterator, m = s && o[s], i = 0;\n if (m) return m.call(o);\n if (o && typeof o.length === \"number\") return {\n next: function () {\n if (o && i >= o.length) o = void 0;\n return { value: o && o[i++], done: !o };\n }\n };\n throw new TypeError(s ? \"Object is not iterable.\" : \"Symbol.iterator is not defined.\");\n}\n\nexport function __read(o, n) {\n var m = typeof Symbol === \"function\" && o[Symbol.iterator];\n if (!m) return o;\n var i = m.call(o), r, ar = [], e;\n try {\n while ((n === void 0 || n-- > 0) && !(r = i.next()).done) ar.push(r.value);\n }\n catch (error) { e = { error: error }; }\n finally {\n try {\n if (r && !r.done && (m = i[\"return\"])) m.call(i);\n }\n finally { if (e) throw e.error; }\n }\n return ar;\n}\n\n/** @deprecated */\nexport function __spread() {\n for (var ar = [], i = 0; i < arguments.length; i++)\n ar = ar.concat(__read(arguments[i]));\n return ar;\n}\n\n/** @deprecated */\nexport function __spreadArrays() {\n for (var s = 0, i = 0, il = arguments.length; i < il; i++) s += arguments[i].length;\n for (var r = Array(s), k = 0, i = 0; i < il; i++)\n for (var a = arguments[i], j = 0, jl = a.length; j < jl; j++, k++)\n r[k] = a[j];\n return r;\n}\n\nexport function __spreadArray(to, from, pack) {\n if (pack || arguments.length === 2) for (var i = 0, l = from.length, ar; i < l; i++) {\n if (ar || !(i in from)) {\n if (!ar) ar = Array.prototype.slice.call(from, 0, i);\n ar[i] = from[i];\n }\n }\n return to.concat(ar || Array.prototype.slice.call(from));\n}\n\nexport function __await(v) {\n return this instanceof __await ? (this.v = v, this) : new __await(v);\n}\n\nexport function __asyncGenerator(thisArg, _arguments, generator) {\n if (!Symbol.asyncIterator) throw new TypeError(\"Symbol.asyncIterator is not defined.\");\n var g = generator.apply(thisArg, _arguments || []), i, q = [];\n return i = Object.create((typeof AsyncIterator === \"function\" ? AsyncIterator : Object).prototype), verb(\"next\"), verb(\"throw\"), verb(\"return\", awaitReturn), i[Symbol.asyncIterator] = function () { return this; }, i;\n function awaitReturn(f) { return function (v) { return Promise.resolve(v).then(f, reject); }; }\n function verb(n, f) { if (g[n]) { i[n] = function (v) { return new Promise(function (a, b) { q.push([n, v, a, b]) > 1 || resume(n, v); }); }; if (f) i[n] = f(i[n]); } }\n function resume(n, v) { try { step(g[n](v)); } catch (e) { settle(q[0][3], e); } }\n function step(r) { r.value instanceof __await ? Promise.resolve(r.value.v).then(fulfill, reject) : settle(q[0][2], r); }\n function fulfill(value) { resume(\"next\", value); }\n function reject(value) { resume(\"throw\", value); }\n function settle(f, v) { if (f(v), q.shift(), q.length) resume(q[0][0], q[0][1]); }\n}\n\nexport function __asyncDelegator(o) {\n var i, p;\n return i = {}, verb(\"next\"), verb(\"throw\", function (e) { throw e; }), verb(\"return\"), i[Symbol.iterator] = function () { return this; }, i;\n function verb(n, f) { i[n] = o[n] ? function (v) { return (p = !p) ? { value: __await(o[n](v)), done: false } : f ? f(v) : v; } : f; }\n}\n\nexport function __asyncValues(o) {\n if (!Symbol.asyncIterator) throw new TypeError(\"Symbol.asyncIterator is not defined.\");\n var m = o[Symbol.asyncIterator], i;\n return m ? m.call(o) : (o = typeof __values === \"function\" ? __values(o) : o[Symbol.iterator](), i = {}, verb(\"next\"), verb(\"throw\"), verb(\"return\"), i[Symbol.asyncIterator] = function () { return this; }, i);\n function verb(n) { i[n] = o[n] && function (v) { return new Promise(function (resolve, reject) { v = o[n](v), settle(resolve, reject, v.done, v.value); }); }; }\n function settle(resolve, reject, d, v) { Promise.resolve(v).then(function(v) { resolve({ value: v, done: d }); }, reject); }\n}\n\nexport function __makeTemplateObject(cooked, raw) {\n if (Object.defineProperty) { Object.defineProperty(cooked, \"raw\", { value: raw }); } else { cooked.raw = raw; }\n return cooked;\n};\n\nvar __setModuleDefault = Object.create ? (function(o, v) {\n Object.defineProperty(o, \"default\", { enumerable: true, value: v });\n}) : function(o, v) {\n o[\"default\"] = v;\n};\n\nexport function __importStar(mod) {\n if (mod && mod.__esModule) return mod;\n var result = {};\n if (mod != null) for (var k in mod) if (k !== \"default\" && Object.prototype.hasOwnProperty.call(mod, k)) __createBinding(result, mod, k);\n __setModuleDefault(result, mod);\n return result;\n}\n\nexport function __importDefault(mod) {\n return (mod && mod.__esModule) ? mod : { default: mod };\n}\n\nexport function __classPrivateFieldGet(receiver, state, kind, f) {\n if (kind === \"a\" && !f) throw new TypeError(\"Private accessor was defined without a getter\");\n if (typeof state === \"function\" ? receiver !== state || !f : !state.has(receiver)) throw new TypeError(\"Cannot read private member from an object whose class did not declare it\");\n return kind === \"m\" ? f : kind === \"a\" ? f.call(receiver) : f ? f.value : state.get(receiver);\n}\n\nexport function __classPrivateFieldSet(receiver, state, value, kind, f) {\n if (kind === \"m\") throw new TypeError(\"Private method is not writable\");\n if (kind === \"a\" && !f) throw new TypeError(\"Private accessor was defined without a setter\");\n if (typeof state === \"function\" ? receiver !== state || !f : !state.has(receiver)) throw new TypeError(\"Cannot write private member to an object whose class did not declare it\");\n return (kind === \"a\" ? f.call(receiver, value) : f ? f.value = value : state.set(receiver, value)), value;\n}\n\nexport function __classPrivateFieldIn(state, receiver) {\n if (receiver === null || (typeof receiver !== \"object\" && typeof receiver !== \"function\")) throw new TypeError(\"Cannot use 'in' operator on non-object\");\n return typeof state === \"function\" ? receiver === state : state.has(receiver);\n}\n\nexport function __addDisposableResource(env, value, async) {\n if (value !== null && value !== void 0) {\n if (typeof value !== \"object\" && typeof value !== \"function\") throw new TypeError(\"Object expected.\");\n var dispose, inner;\n if (async) {\n if (!Symbol.asyncDispose) throw new TypeError(\"Symbol.asyncDispose is not defined.\");\n dispose = value[Symbol.asyncDispose];\n }\n if (dispose === void 0) {\n if (!Symbol.dispose) throw new TypeError(\"Symbol.dispose is not defined.\");\n dispose = value[Symbol.dispose];\n if (async) inner = dispose;\n }\n if (typeof dispose !== \"function\") throw new TypeError(\"Object not disposable.\");\n if (inner) dispose = function() { try { inner.call(this); } catch (e) { return Promise.reject(e); } };\n env.stack.push({ value: value, dispose: dispose, async: async });\n }\n else if (async) {\n env.stack.push({ async: true });\n }\n return value;\n}\n\nvar _SuppressedError = typeof SuppressedError === \"function\" ? SuppressedError : function (error, suppressed, message) {\n var e = new Error(message);\n return e.name = \"SuppressedError\", e.error = error, e.suppressed = suppressed, e;\n};\n\nexport function __disposeResources(env) {\n function fail(e) {\n env.error = env.hasError ? new _SuppressedError(e, env.error, \"An error was suppressed during disposal.\") : e;\n env.hasError = true;\n }\n var r, s = 0;\n function next() {\n while (r = env.stack.pop()) {\n try {\n if (!r.async && s === 1) return s = 0, env.stack.push(r), Promise.resolve().then(next);\n if (r.dispose) {\n var result = r.dispose.call(r.value);\n if (r.async) return s |= 2, Promise.resolve(result).then(next, function(e) { fail(e); return next(); });\n }\n else s |= 1;\n }\n catch (e) {\n fail(e);\n }\n }\n if (s === 1) return env.hasError ? Promise.reject(env.error) : Promise.resolve();\n if (env.hasError) throw env.error;\n }\n return next();\n}\n\nexport default {\n __extends,\n __assign,\n __rest,\n __decorate,\n __param,\n __metadata,\n __awaiter,\n __generator,\n __createBinding,\n __exportStar,\n __values,\n __read,\n __spread,\n __spreadArrays,\n __spreadArray,\n __await,\n __asyncGenerator,\n __asyncDelegator,\n __asyncValues,\n __makeTemplateObject,\n __importStar,\n __importDefault,\n __classPrivateFieldGet,\n __classPrivateFieldSet,\n __classPrivateFieldIn,\n __addDisposableResource,\n __disposeResources,\n};\n", "/**\n * Returns true if the object is a function.\n * @param value The value to check\n */\nexport function isFunction(value: any): value is (...args: any[]) => any {\n return typeof value === 'function';\n}\n", "/**\n * Used to create Error subclasses until the community moves away from ES5.\n *\n * This is because compiling from TypeScript down to ES5 has issues with subclassing Errors\n * as well as other built-in types: https://github.com/Microsoft/TypeScript/issues/12123\n *\n * @param createImpl A factory function to create the actual constructor implementation. The returned\n * function should be a named function that calls `_super` internally.\n */\nexport function createErrorClass(createImpl: (_super: any) => any): T {\n const _super = (instance: any) => {\n Error.call(instance);\n instance.stack = new Error().stack;\n };\n\n const ctorFunc = createImpl(_super);\n ctorFunc.prototype = Object.create(Error.prototype);\n ctorFunc.prototype.constructor = ctorFunc;\n return ctorFunc;\n}\n", "import { createErrorClass } from './createErrorClass';\n\nexport interface UnsubscriptionError extends Error {\n readonly errors: any[];\n}\n\nexport interface UnsubscriptionErrorCtor {\n /**\n * @deprecated Internal implementation detail. Do not construct error instances.\n * Cannot be tagged as internal: https://github.com/ReactiveX/rxjs/issues/6269\n */\n new (errors: any[]): UnsubscriptionError;\n}\n\n/**\n * An error thrown when one or more errors have occurred during the\n * `unsubscribe` of a {@link Subscription}.\n */\nexport const UnsubscriptionError: UnsubscriptionErrorCtor = createErrorClass(\n (_super) =>\n function UnsubscriptionErrorImpl(this: any, errors: (Error | string)[]) {\n _super(this);\n this.message = errors\n ? `${errors.length} errors occurred during unsubscription:\n${errors.map((err, i) => `${i + 1}) ${err.toString()}`).join('\\n ')}`\n : '';\n this.name = 'UnsubscriptionError';\n this.errors = errors;\n }\n);\n", "/**\n * Removes an item from an array, mutating it.\n * @param arr The array to remove the item from\n * @param item The item to remove\n */\nexport function arrRemove(arr: T[] | undefined | null, item: T) {\n if (arr) {\n const index = arr.indexOf(item);\n 0 <= index && arr.splice(index, 1);\n }\n}\n", "import { isFunction } from './util/isFunction';\nimport { UnsubscriptionError } from './util/UnsubscriptionError';\nimport { SubscriptionLike, TeardownLogic, Unsubscribable } from './types';\nimport { arrRemove } from './util/arrRemove';\n\n/**\n * Represents a disposable resource, such as the execution of an Observable. A\n * Subscription has one important method, `unsubscribe`, that takes no argument\n * and just disposes the resource held by the subscription.\n *\n * Additionally, subscriptions may be grouped together through the `add()`\n * method, which will attach a child Subscription to the current Subscription.\n * When a Subscription is unsubscribed, all its children (and its grandchildren)\n * will be unsubscribed as well.\n */\nexport class Subscription implements SubscriptionLike {\n public static EMPTY = (() => {\n const empty = new Subscription();\n empty.closed = true;\n return empty;\n })();\n\n /**\n * A flag to indicate whether this Subscription has already been unsubscribed.\n */\n public closed = false;\n\n private _parentage: Subscription[] | Subscription | null = null;\n\n /**\n * The list of registered finalizers to execute upon unsubscription. Adding and removing from this\n * list occurs in the {@link #add} and {@link #remove} methods.\n */\n private _finalizers: Exclude[] | null = null;\n\n /**\n * @param initialTeardown A function executed first as part of the finalization\n * process that is kicked off when {@link #unsubscribe} is called.\n */\n constructor(private initialTeardown?: () => void) {}\n\n /**\n * Disposes the resources held by the subscription. May, for instance, cancel\n * an ongoing Observable execution or cancel any other type of work that\n * started when the Subscription was created.\n */\n unsubscribe(): void {\n let errors: any[] | undefined;\n\n if (!this.closed) {\n this.closed = true;\n\n // Remove this from it's parents.\n const { _parentage } = this;\n if (_parentage) {\n this._parentage = null;\n if (Array.isArray(_parentage)) {\n for (const parent of _parentage) {\n parent.remove(this);\n }\n } else {\n _parentage.remove(this);\n }\n }\n\n const { initialTeardown: initialFinalizer } = this;\n if (isFunction(initialFinalizer)) {\n try {\n initialFinalizer();\n } catch (e) {\n errors = e instanceof UnsubscriptionError ? e.errors : [e];\n }\n }\n\n const { _finalizers } = this;\n if (_finalizers) {\n this._finalizers = null;\n for (const finalizer of _finalizers) {\n try {\n execFinalizer(finalizer);\n } catch (err) {\n errors = errors ?? [];\n if (err instanceof UnsubscriptionError) {\n errors = [...errors, ...err.errors];\n } else {\n errors.push(err);\n }\n }\n }\n }\n\n if (errors) {\n throw new UnsubscriptionError(errors);\n }\n }\n }\n\n /**\n * Adds a finalizer to this subscription, so that finalization will be unsubscribed/called\n * when this subscription is unsubscribed. If this subscription is already {@link #closed},\n * because it has already been unsubscribed, then whatever finalizer is passed to it\n * will automatically be executed (unless the finalizer itself is also a closed subscription).\n *\n * Closed Subscriptions cannot be added as finalizers to any subscription. Adding a closed\n * subscription to a any subscription will result in no operation. (A noop).\n *\n * Adding a subscription to itself, or adding `null` or `undefined` will not perform any\n * operation at all. (A noop).\n *\n * `Subscription` instances that are added to this instance will automatically remove themselves\n * if they are unsubscribed. Functions and {@link Unsubscribable} objects that you wish to remove\n * will need to be removed manually with {@link #remove}\n *\n * @param teardown The finalization logic to add to this subscription.\n */\n add(teardown: TeardownLogic): void {\n // Only add the finalizer if it's not undefined\n // and don't add a subscription to itself.\n if (teardown && teardown !== this) {\n if (this.closed) {\n // If this subscription is already closed,\n // execute whatever finalizer is handed to it automatically.\n execFinalizer(teardown);\n } else {\n if (teardown instanceof Subscription) {\n // We don't add closed subscriptions, and we don't add the same subscription\n // twice. Subscription unsubscribe is idempotent.\n if (teardown.closed || teardown._hasParent(this)) {\n return;\n }\n teardown._addParent(this);\n }\n (this._finalizers = this._finalizers ?? []).push(teardown);\n }\n }\n }\n\n /**\n * Checks to see if a this subscription already has a particular parent.\n * This will signal that this subscription has already been added to the parent in question.\n * @param parent the parent to check for\n */\n private _hasParent(parent: Subscription) {\n const { _parentage } = this;\n return _parentage === parent || (Array.isArray(_parentage) && _parentage.includes(parent));\n }\n\n /**\n * Adds a parent to this subscription so it can be removed from the parent if it\n * unsubscribes on it's own.\n *\n * NOTE: THIS ASSUMES THAT {@link _hasParent} HAS ALREADY BEEN CHECKED.\n * @param parent The parent subscription to add\n */\n private _addParent(parent: Subscription) {\n const { _parentage } = this;\n this._parentage = Array.isArray(_parentage) ? (_parentage.push(parent), _parentage) : _parentage ? [_parentage, parent] : parent;\n }\n\n /**\n * Called on a child when it is removed via {@link #remove}.\n * @param parent The parent to remove\n */\n private _removeParent(parent: Subscription) {\n const { _parentage } = this;\n if (_parentage === parent) {\n this._parentage = null;\n } else if (Array.isArray(_parentage)) {\n arrRemove(_parentage, parent);\n }\n }\n\n /**\n * Removes a finalizer from this subscription that was previously added with the {@link #add} method.\n *\n * Note that `Subscription` instances, when unsubscribed, will automatically remove themselves\n * from every other `Subscription` they have been added to. This means that using the `remove` method\n * is not a common thing and should be used thoughtfully.\n *\n * If you add the same finalizer instance of a function or an unsubscribable object to a `Subscription` instance\n * more than once, you will need to call `remove` the same number of times to remove all instances.\n *\n * All finalizer instances are removed to free up memory upon unsubscription.\n *\n * @param teardown The finalizer to remove from this subscription\n */\n remove(teardown: Exclude): void {\n const { _finalizers } = this;\n _finalizers && arrRemove(_finalizers, teardown);\n\n if (teardown instanceof Subscription) {\n teardown._removeParent(this);\n }\n }\n}\n\nexport const EMPTY_SUBSCRIPTION = Subscription.EMPTY;\n\nexport function isSubscription(value: any): value is Subscription {\n return (\n value instanceof Subscription ||\n (value && 'closed' in value && isFunction(value.remove) && isFunction(value.add) && isFunction(value.unsubscribe))\n );\n}\n\nfunction execFinalizer(finalizer: Unsubscribable | (() => void)) {\n if (isFunction(finalizer)) {\n finalizer();\n } else {\n finalizer.unsubscribe();\n }\n}\n", "import { Subscriber } from './Subscriber';\nimport { ObservableNotification } from './types';\n\n/**\n * The {@link GlobalConfig} object for RxJS. It is used to configure things\n * like how to react on unhandled errors.\n */\nexport const config: GlobalConfig = {\n onUnhandledError: null,\n onStoppedNotification: null,\n Promise: undefined,\n useDeprecatedSynchronousErrorHandling: false,\n useDeprecatedNextContext: false,\n};\n\n/**\n * The global configuration object for RxJS, used to configure things\n * like how to react on unhandled errors. Accessible via {@link config}\n * object.\n */\nexport interface GlobalConfig {\n /**\n * A registration point for unhandled errors from RxJS. These are errors that\n * cannot were not handled by consuming code in the usual subscription path. For\n * example, if you have this configured, and you subscribe to an observable without\n * providing an error handler, errors from that subscription will end up here. This\n * will _always_ be called asynchronously on another job in the runtime. This is because\n * we do not want errors thrown in this user-configured handler to interfere with the\n * behavior of the library.\n */\n onUnhandledError: ((err: any) => void) | null;\n\n /**\n * A registration point for notifications that cannot be sent to subscribers because they\n * have completed, errored or have been explicitly unsubscribed. By default, next, complete\n * and error notifications sent to stopped subscribers are noops. However, sometimes callers\n * might want a different behavior. For example, with sources that attempt to report errors\n * to stopped subscribers, a caller can configure RxJS to throw an unhandled error instead.\n * This will _always_ be called asynchronously on another job in the runtime. This is because\n * we do not want errors thrown in this user-configured handler to interfere with the\n * behavior of the library.\n */\n onStoppedNotification: ((notification: ObservableNotification, subscriber: Subscriber) => void) | null;\n\n /**\n * The promise constructor used by default for {@link Observable#toPromise toPromise} and {@link Observable#forEach forEach}\n * methods.\n *\n * @deprecated As of version 8, RxJS will no longer support this sort of injection of a\n * Promise constructor. If you need a Promise implementation other than native promises,\n * please polyfill/patch Promise as you see appropriate. Will be removed in v8.\n */\n Promise?: PromiseConstructorLike;\n\n /**\n * If true, turns on synchronous error rethrowing, which is a deprecated behavior\n * in v6 and higher. This behavior enables bad patterns like wrapping a subscribe\n * call in a try/catch block. It also enables producer interference, a nasty bug\n * where a multicast can be broken for all observers by a downstream consumer with\n * an unhandled error. DO NOT USE THIS FLAG UNLESS IT'S NEEDED TO BUY TIME\n * FOR MIGRATION REASONS.\n *\n * @deprecated As of version 8, RxJS will no longer support synchronous throwing\n * of unhandled errors. All errors will be thrown on a separate call stack to prevent bad\n * behaviors described above. Will be removed in v8.\n */\n useDeprecatedSynchronousErrorHandling: boolean;\n\n /**\n * If true, enables an as-of-yet undocumented feature from v5: The ability to access\n * `unsubscribe()` via `this` context in `next` functions created in observers passed\n * to `subscribe`.\n *\n * This is being removed because the performance was severely problematic, and it could also cause\n * issues when types other than POJOs are passed to subscribe as subscribers, as they will likely have\n * their `this` context overwritten.\n *\n * @deprecated As of version 8, RxJS will no longer support altering the\n * context of next functions provided as part of an observer to Subscribe. Instead,\n * you will have access to a subscription or a signal or token that will allow you to do things like\n * unsubscribe and test closed status. Will be removed in v8.\n */\n useDeprecatedNextContext: boolean;\n}\n", "import type { TimerHandle } from './timerHandle';\ntype SetTimeoutFunction = (handler: () => void, timeout?: number, ...args: any[]) => TimerHandle;\ntype ClearTimeoutFunction = (handle: TimerHandle) => void;\n\ninterface TimeoutProvider {\n setTimeout: SetTimeoutFunction;\n clearTimeout: ClearTimeoutFunction;\n delegate:\n | {\n setTimeout: SetTimeoutFunction;\n clearTimeout: ClearTimeoutFunction;\n }\n | undefined;\n}\n\nexport const timeoutProvider: TimeoutProvider = {\n // When accessing the delegate, use the variable rather than `this` so that\n // the functions can be called without being bound to the provider.\n setTimeout(handler: () => void, timeout?: number, ...args) {\n const { delegate } = timeoutProvider;\n if (delegate?.setTimeout) {\n return delegate.setTimeout(handler, timeout, ...args);\n }\n return setTimeout(handler, timeout, ...args);\n },\n clearTimeout(handle) {\n const { delegate } = timeoutProvider;\n return (delegate?.clearTimeout || clearTimeout)(handle as any);\n },\n delegate: undefined,\n};\n", "import { config } from '../config';\nimport { timeoutProvider } from '../scheduler/timeoutProvider';\n\n/**\n * Handles an error on another job either with the user-configured {@link onUnhandledError},\n * or by throwing it on that new job so it can be picked up by `window.onerror`, `process.on('error')`, etc.\n *\n * This should be called whenever there is an error that is out-of-band with the subscription\n * or when an error hits a terminal boundary of the subscription and no error handler was provided.\n *\n * @param err the error to report\n */\nexport function reportUnhandledError(err: any) {\n timeoutProvider.setTimeout(() => {\n const { onUnhandledError } = config;\n if (onUnhandledError) {\n // Execute the user-configured error handler.\n onUnhandledError(err);\n } else {\n // Throw so it is picked up by the runtime's uncaught error mechanism.\n throw err;\n }\n });\n}\n", "/* tslint:disable:no-empty */\nexport function noop() { }\n", "import { CompleteNotification, NextNotification, ErrorNotification } from './types';\n\n/**\n * A completion object optimized for memory use and created to be the\n * same \"shape\" as other notifications in v8.\n * @internal\n */\nexport const COMPLETE_NOTIFICATION = (() => createNotification('C', undefined, undefined) as CompleteNotification)();\n\n/**\n * Internal use only. Creates an optimized error notification that is the same \"shape\"\n * as other notifications.\n * @internal\n */\nexport function errorNotification(error: any): ErrorNotification {\n return createNotification('E', undefined, error) as any;\n}\n\n/**\n * Internal use only. Creates an optimized next notification that is the same \"shape\"\n * as other notifications.\n * @internal\n */\nexport function nextNotification(value: T) {\n return createNotification('N', value, undefined) as NextNotification;\n}\n\n/**\n * Ensures that all notifications created internally have the same \"shape\" in v8.\n *\n * TODO: This is only exported to support a crazy legacy test in `groupBy`.\n * @internal\n */\nexport function createNotification(kind: 'N' | 'E' | 'C', value: any, error: any) {\n return {\n kind,\n value,\n error,\n };\n}\n", "import { config } from '../config';\n\nlet context: { errorThrown: boolean; error: any } | null = null;\n\n/**\n * Handles dealing with errors for super-gross mode. Creates a context, in which\n * any synchronously thrown errors will be passed to {@link captureError}. Which\n * will record the error such that it will be rethrown after the call back is complete.\n * TODO: Remove in v8\n * @param cb An immediately executed function.\n */\nexport function errorContext(cb: () => void) {\n if (config.useDeprecatedSynchronousErrorHandling) {\n const isRoot = !context;\n if (isRoot) {\n context = { errorThrown: false, error: null };\n }\n cb();\n if (isRoot) {\n const { errorThrown, error } = context!;\n context = null;\n if (errorThrown) {\n throw error;\n }\n }\n } else {\n // This is the general non-deprecated path for everyone that\n // isn't crazy enough to use super-gross mode (useDeprecatedSynchronousErrorHandling)\n cb();\n }\n}\n\n/**\n * Captures errors only in super-gross mode.\n * @param err the error to capture\n */\nexport function captureError(err: any) {\n if (config.useDeprecatedSynchronousErrorHandling && context) {\n context.errorThrown = true;\n context.error = err;\n }\n}\n", "import { isFunction } from './util/isFunction';\nimport { Observer, ObservableNotification } from './types';\nimport { isSubscription, Subscription } from './Subscription';\nimport { config } from './config';\nimport { reportUnhandledError } from './util/reportUnhandledError';\nimport { noop } from './util/noop';\nimport { nextNotification, errorNotification, COMPLETE_NOTIFICATION } from './NotificationFactories';\nimport { timeoutProvider } from './scheduler/timeoutProvider';\nimport { captureError } from './util/errorContext';\n\n/**\n * Implements the {@link Observer} interface and extends the\n * {@link Subscription} class. While the {@link Observer} is the public API for\n * consuming the values of an {@link Observable}, all Observers get converted to\n * a Subscriber, in order to provide Subscription-like capabilities such as\n * `unsubscribe`. Subscriber is a common type in RxJS, and crucial for\n * implementing operators, but it is rarely used as a public API.\n */\nexport class Subscriber extends Subscription implements Observer {\n /**\n * A static factory for a Subscriber, given a (potentially partial) definition\n * of an Observer.\n * @param next The `next` callback of an Observer.\n * @param error The `error` callback of an\n * Observer.\n * @param complete The `complete` callback of an\n * Observer.\n * @return A Subscriber wrapping the (partially defined)\n * Observer represented by the given arguments.\n * @deprecated Do not use. Will be removed in v8. There is no replacement for this\n * method, and there is no reason to be creating instances of `Subscriber` directly.\n * If you have a specific use case, please file an issue.\n */\n static create(next?: (x?: T) => void, error?: (e?: any) => void, complete?: () => void): Subscriber {\n return new SafeSubscriber(next, error, complete);\n }\n\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n protected isStopped: boolean = false;\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n protected destination: Subscriber | Observer; // this `any` is the escape hatch to erase extra type param (e.g. R)\n\n /**\n * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n * There is no reason to directly create an instance of Subscriber. This type is exported for typings reasons.\n */\n constructor(destination?: Subscriber | Observer) {\n super();\n if (destination) {\n this.destination = destination;\n // Automatically chain subscriptions together here.\n // if destination is a Subscription, then it is a Subscriber.\n if (isSubscription(destination)) {\n destination.add(this);\n }\n } else {\n this.destination = EMPTY_OBSERVER;\n }\n }\n\n /**\n * The {@link Observer} callback to receive notifications of type `next` from\n * the Observable, with a value. The Observable may call this method 0 or more\n * times.\n * @param value The `next` value.\n */\n next(value: T): void {\n if (this.isStopped) {\n handleStoppedNotification(nextNotification(value), this);\n } else {\n this._next(value!);\n }\n }\n\n /**\n * The {@link Observer} callback to receive notifications of type `error` from\n * the Observable, with an attached `Error`. Notifies the Observer that\n * the Observable has experienced an error condition.\n * @param err The `error` exception.\n */\n error(err?: any): void {\n if (this.isStopped) {\n handleStoppedNotification(errorNotification(err), this);\n } else {\n this.isStopped = true;\n this._error(err);\n }\n }\n\n /**\n * The {@link Observer} callback to receive a valueless notification of type\n * `complete` from the Observable. Notifies the Observer that the Observable\n * has finished sending push-based notifications.\n */\n complete(): void {\n if (this.isStopped) {\n handleStoppedNotification(COMPLETE_NOTIFICATION, this);\n } else {\n this.isStopped = true;\n this._complete();\n }\n }\n\n unsubscribe(): void {\n if (!this.closed) {\n this.isStopped = true;\n super.unsubscribe();\n this.destination = null!;\n }\n }\n\n protected _next(value: T): void {\n this.destination.next(value);\n }\n\n protected _error(err: any): void {\n try {\n this.destination.error(err);\n } finally {\n this.unsubscribe();\n }\n }\n\n protected _complete(): void {\n try {\n this.destination.complete();\n } finally {\n this.unsubscribe();\n }\n }\n}\n\n/**\n * This bind is captured here because we want to be able to have\n * compatibility with monoid libraries that tend to use a method named\n * `bind`. In particular, a library called Monio requires this.\n */\nconst _bind = Function.prototype.bind;\n\nfunction bind any>(fn: Fn, thisArg: any): Fn {\n return _bind.call(fn, thisArg);\n}\n\n/**\n * Internal optimization only, DO NOT EXPOSE.\n * @internal\n */\nclass ConsumerObserver implements Observer {\n constructor(private partialObserver: Partial>) {}\n\n next(value: T): void {\n const { partialObserver } = this;\n if (partialObserver.next) {\n try {\n partialObserver.next(value);\n } catch (error) {\n handleUnhandledError(error);\n }\n }\n }\n\n error(err: any): void {\n const { partialObserver } = this;\n if (partialObserver.error) {\n try {\n partialObserver.error(err);\n } catch (error) {\n handleUnhandledError(error);\n }\n } else {\n handleUnhandledError(err);\n }\n }\n\n complete(): void {\n const { partialObserver } = this;\n if (partialObserver.complete) {\n try {\n partialObserver.complete();\n } catch (error) {\n handleUnhandledError(error);\n }\n }\n }\n}\n\nexport class SafeSubscriber extends Subscriber {\n constructor(\n observerOrNext?: Partial> | ((value: T) => void) | null,\n error?: ((e?: any) => void) | null,\n complete?: (() => void) | null\n ) {\n super();\n\n let partialObserver: Partial>;\n if (isFunction(observerOrNext) || !observerOrNext) {\n // The first argument is a function, not an observer. The next\n // two arguments *could* be observers, or they could be empty.\n partialObserver = {\n next: (observerOrNext ?? undefined) as ((value: T) => void) | undefined,\n error: error ?? undefined,\n complete: complete ?? undefined,\n };\n } else {\n // The first argument is a partial observer.\n let context: any;\n if (this && config.useDeprecatedNextContext) {\n // This is a deprecated path that made `this.unsubscribe()` available in\n // next handler functions passed to subscribe. This only exists behind a flag\n // now, as it is *very* slow.\n context = Object.create(observerOrNext);\n context.unsubscribe = () => this.unsubscribe();\n partialObserver = {\n next: observerOrNext.next && bind(observerOrNext.next, context),\n error: observerOrNext.error && bind(observerOrNext.error, context),\n complete: observerOrNext.complete && bind(observerOrNext.complete, context),\n };\n } else {\n // The \"normal\" path. Just use the partial observer directly.\n partialObserver = observerOrNext;\n }\n }\n\n // Wrap the partial observer to ensure it's a full observer, and\n // make sure proper error handling is accounted for.\n this.destination = new ConsumerObserver(partialObserver);\n }\n}\n\nfunction handleUnhandledError(error: any) {\n if (config.useDeprecatedSynchronousErrorHandling) {\n captureError(error);\n } else {\n // Ideal path, we report this as an unhandled error,\n // which is thrown on a new call stack.\n reportUnhandledError(error);\n }\n}\n\n/**\n * An error handler used when no error handler was supplied\n * to the SafeSubscriber -- meaning no error handler was supplied\n * do the `subscribe` call on our observable.\n * @param err The error to handle\n */\nfunction defaultErrorHandler(err: any) {\n throw err;\n}\n\n/**\n * A handler for notifications that cannot be sent to a stopped subscriber.\n * @param notification The notification being sent.\n * @param subscriber The stopped subscriber.\n */\nfunction handleStoppedNotification(notification: ObservableNotification, subscriber: Subscriber) {\n const { onStoppedNotification } = config;\n onStoppedNotification && timeoutProvider.setTimeout(() => onStoppedNotification(notification, subscriber));\n}\n\n/**\n * The observer used as a stub for subscriptions where the user did not\n * pass any arguments to `subscribe`. Comes with the default error handling\n * behavior.\n */\nexport const EMPTY_OBSERVER: Readonly> & { closed: true } = {\n closed: true,\n next: noop,\n error: defaultErrorHandler,\n complete: noop,\n};\n", "/**\n * Symbol.observable or a string \"@@observable\". Used for interop\n *\n * @deprecated We will no longer be exporting this symbol in upcoming versions of RxJS.\n * Instead polyfill and use Symbol.observable directly *or* use https://www.npmjs.com/package/symbol-observable\n */\nexport const observable: string | symbol = (() => (typeof Symbol === 'function' && Symbol.observable) || '@@observable')();\n", "/**\n * This function takes one parameter and just returns it. Simply put,\n * this is like `(x: T): T => x`.\n *\n * ## Examples\n *\n * This is useful in some cases when using things like `mergeMap`\n *\n * ```ts\n * import { interval, take, map, range, mergeMap, identity } from 'rxjs';\n *\n * const source$ = interval(1000).pipe(take(5));\n *\n * const result$ = source$.pipe(\n * map(i => range(i)),\n * mergeMap(identity) // same as mergeMap(x => x)\n * );\n *\n * result$.subscribe({\n * next: console.log\n * });\n * ```\n *\n * Or when you want to selectively apply an operator\n *\n * ```ts\n * import { interval, take, identity } from 'rxjs';\n *\n * const shouldLimit = () => Math.random() < 0.5;\n *\n * const source$ = interval(1000);\n *\n * const result$ = source$.pipe(shouldLimit() ? take(5) : identity);\n *\n * result$.subscribe({\n * next: console.log\n * });\n * ```\n *\n * @param x Any value that is returned by this function\n * @returns The value passed as the first parameter to this function\n */\nexport function identity(x: T): T {\n return x;\n}\n", "import { identity } from './identity';\nimport { UnaryFunction } from '../types';\n\nexport function pipe(): typeof identity;\nexport function pipe(fn1: UnaryFunction): UnaryFunction;\nexport function pipe(fn1: UnaryFunction, fn2: UnaryFunction): UnaryFunction;\nexport function pipe(fn1: UnaryFunction, fn2: UnaryFunction, fn3: UnaryFunction): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction,\n fn6: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction,\n fn6: UnaryFunction,\n fn7: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction,\n fn6: UnaryFunction,\n fn7: UnaryFunction,\n fn8: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction,\n fn6: UnaryFunction,\n fn7: UnaryFunction,\n fn8: UnaryFunction,\n fn9: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction,\n fn6: UnaryFunction,\n fn7: UnaryFunction,\n fn8: UnaryFunction,\n fn9: UnaryFunction,\n ...fns: UnaryFunction[]\n): UnaryFunction;\n\n/**\n * pipe() can be called on one or more functions, each of which can take one argument (\"UnaryFunction\")\n * and uses it to return a value.\n * It returns a function that takes one argument, passes it to the first UnaryFunction, and then\n * passes the result to the next one, passes that result to the next one, and so on. \n */\nexport function pipe(...fns: Array>): UnaryFunction {\n return pipeFromArray(fns);\n}\n\n/** @internal */\nexport function pipeFromArray(fns: Array>): UnaryFunction {\n if (fns.length === 0) {\n return identity as UnaryFunction;\n }\n\n if (fns.length === 1) {\n return fns[0];\n }\n\n return function piped(input: T): R {\n return fns.reduce((prev: any, fn: UnaryFunction) => fn(prev), input as any);\n };\n}\n", "import { Operator } from './Operator';\nimport { SafeSubscriber, Subscriber } from './Subscriber';\nimport { isSubscription, Subscription } from './Subscription';\nimport { TeardownLogic, OperatorFunction, Subscribable, Observer } from './types';\nimport { observable as Symbol_observable } from './symbol/observable';\nimport { pipeFromArray } from './util/pipe';\nimport { config } from './config';\nimport { isFunction } from './util/isFunction';\nimport { errorContext } from './util/errorContext';\n\n/**\n * A representation of any set of values over any amount of time. This is the most basic building block\n * of RxJS.\n */\nexport class Observable implements Subscribable {\n /**\n * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n */\n source: Observable | undefined;\n\n /**\n * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n */\n operator: Operator | undefined;\n\n /**\n * @param subscribe The function that is called when the Observable is\n * initially subscribed to. This function is given a Subscriber, to which new values\n * can be `next`ed, or an `error` method can be called to raise an error, or\n * `complete` can be called to notify of a successful completion.\n */\n constructor(subscribe?: (this: Observable, subscriber: Subscriber) => TeardownLogic) {\n if (subscribe) {\n this._subscribe = subscribe;\n }\n }\n\n // HACK: Since TypeScript inherits static properties too, we have to\n // fight against TypeScript here so Subject can have a different static create signature\n /**\n * Creates a new Observable by calling the Observable constructor\n * @param subscribe the subscriber function to be passed to the Observable constructor\n * @return A new observable.\n * @deprecated Use `new Observable()` instead. Will be removed in v8.\n */\n static create: (...args: any[]) => any = (subscribe?: (subscriber: Subscriber) => TeardownLogic) => {\n return new Observable(subscribe);\n };\n\n /**\n * Creates a new Observable, with this Observable instance as the source, and the passed\n * operator defined as the new observable's operator.\n * @param operator the operator defining the operation to take on the observable\n * @return A new observable with the Operator applied.\n * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n * If you have implemented an operator using `lift`, it is recommended that you create an\n * operator by simply returning `new Observable()` directly. See \"Creating new operators from\n * scratch\" section here: https://rxjs.dev/guide/operators\n */\n lift(operator?: Operator): Observable {\n const observable = new Observable();\n observable.source = this;\n observable.operator = operator;\n return observable;\n }\n\n subscribe(observerOrNext?: Partial> | ((value: T) => void)): Subscription;\n /** @deprecated Instead of passing separate callback arguments, use an observer argument. Signatures taking separate callback arguments will be removed in v8. Details: https://rxjs.dev/deprecations/subscribe-arguments */\n subscribe(next?: ((value: T) => void) | null, error?: ((error: any) => void) | null, complete?: (() => void) | null): Subscription;\n /**\n * Invokes an execution of an Observable and registers Observer handlers for notifications it will emit.\n *\n * Use it when you have all these Observables, but still nothing is happening.\n *\n * `subscribe` is not a regular operator, but a method that calls Observable's internal `subscribe` function. It\n * might be for example a function that you passed to Observable's constructor, but most of the time it is\n * a library implementation, which defines what will be emitted by an Observable, and when it be will emitted. This means\n * that calling `subscribe` is actually the moment when Observable starts its work, not when it is created, as it is often\n * the thought.\n *\n * Apart from starting the execution of an Observable, this method allows you to listen for values\n * that an Observable emits, as well as for when it completes or errors. You can achieve this in two\n * of the following ways.\n *\n * The first way is creating an object that implements {@link Observer} interface. It should have methods\n * defined by that interface, but note that it should be just a regular JavaScript object, which you can create\n * yourself in any way you want (ES6 class, classic function constructor, object literal etc.). In particular, do\n * not attempt to use any RxJS implementation details to create Observers - you don't need them. Remember also\n * that your object does not have to implement all methods. If you find yourself creating a method that doesn't\n * do anything, you can simply omit it. Note however, if the `error` method is not provided and an error happens,\n * it will be thrown asynchronously. Errors thrown asynchronously cannot be caught using `try`/`catch`. Instead,\n * use the {@link onUnhandledError} configuration option or use a runtime handler (like `window.onerror` or\n * `process.on('error)`) to be notified of unhandled errors. Because of this, it's recommended that you provide\n * an `error` method to avoid missing thrown errors.\n *\n * The second way is to give up on Observer object altogether and simply provide callback functions in place of its methods.\n * This means you can provide three functions as arguments to `subscribe`, where the first function is equivalent\n * of a `next` method, the second of an `error` method and the third of a `complete` method. Just as in case of an Observer,\n * if you do not need to listen for something, you can omit a function by passing `undefined` or `null`,\n * since `subscribe` recognizes these functions by where they were placed in function call. When it comes\n * to the `error` function, as with an Observer, if not provided, errors emitted by an Observable will be thrown asynchronously.\n *\n * You can, however, subscribe with no parameters at all. This may be the case where you're not interested in terminal events\n * and you also handled emissions internally by using operators (e.g. using `tap`).\n *\n * Whichever style of calling `subscribe` you use, in both cases it returns a Subscription object.\n * This object allows you to call `unsubscribe` on it, which in turn will stop the work that an Observable does and will clean\n * up all resources that an Observable used. Note that cancelling a subscription will not call `complete` callback\n * provided to `subscribe` function, which is reserved for a regular completion signal that comes from an Observable.\n *\n * Remember that callbacks provided to `subscribe` are not guaranteed to be called asynchronously.\n * It is an Observable itself that decides when these functions will be called. For example {@link of}\n * by default emits all its values synchronously. Always check documentation for how given Observable\n * will behave when subscribed and if its default behavior can be modified with a `scheduler`.\n *\n * #### Examples\n *\n * Subscribe with an {@link guide/observer Observer}\n *\n * ```ts\n * import { of } from 'rxjs';\n *\n * const sumObserver = {\n * sum: 0,\n * next(value) {\n * console.log('Adding: ' + value);\n * this.sum = this.sum + value;\n * },\n * error() {\n * // We actually could just remove this method,\n * // since we do not really care about errors right now.\n * },\n * complete() {\n * console.log('Sum equals: ' + this.sum);\n * }\n * };\n *\n * of(1, 2, 3) // Synchronously emits 1, 2, 3 and then completes.\n * .subscribe(sumObserver);\n *\n * // Logs:\n * // 'Adding: 1'\n * // 'Adding: 2'\n * // 'Adding: 3'\n * // 'Sum equals: 6'\n * ```\n *\n * Subscribe with functions ({@link deprecations/subscribe-arguments deprecated})\n *\n * ```ts\n * import { of } from 'rxjs'\n *\n * let sum = 0;\n *\n * of(1, 2, 3).subscribe(\n * value => {\n * console.log('Adding: ' + value);\n * sum = sum + value;\n * },\n * undefined,\n * () => console.log('Sum equals: ' + sum)\n * );\n *\n * // Logs:\n * // 'Adding: 1'\n * // 'Adding: 2'\n * // 'Adding: 3'\n * // 'Sum equals: 6'\n * ```\n *\n * Cancel a subscription\n *\n * ```ts\n * import { interval } from 'rxjs';\n *\n * const subscription = interval(1000).subscribe({\n * next(num) {\n * console.log(num)\n * },\n * complete() {\n * // Will not be called, even when cancelling subscription.\n * console.log('completed!');\n * }\n * });\n *\n * setTimeout(() => {\n * subscription.unsubscribe();\n * console.log('unsubscribed!');\n * }, 2500);\n *\n * // Logs:\n * // 0 after 1s\n * // 1 after 2s\n * // 'unsubscribed!' after 2.5s\n * ```\n *\n * @param observerOrNext Either an {@link Observer} with some or all callback methods,\n * or the `next` handler that is called for each value emitted from the subscribed Observable.\n * @param error A handler for a terminal event resulting from an error. If no error handler is provided,\n * the error will be thrown asynchronously as unhandled.\n * @param complete A handler for a terminal event resulting from successful completion.\n * @return A subscription reference to the registered handlers.\n */\n subscribe(\n observerOrNext?: Partial> | ((value: T) => void) | null,\n error?: ((error: any) => void) | null,\n complete?: (() => void) | null\n ): Subscription {\n const subscriber = isSubscriber(observerOrNext) ? observerOrNext : new SafeSubscriber(observerOrNext, error, complete);\n\n errorContext(() => {\n const { operator, source } = this;\n subscriber.add(\n operator\n ? // We're dealing with a subscription in the\n // operator chain to one of our lifted operators.\n operator.call(subscriber, source)\n : source\n ? // If `source` has a value, but `operator` does not, something that\n // had intimate knowledge of our API, like our `Subject`, must have\n // set it. We're going to just call `_subscribe` directly.\n this._subscribe(subscriber)\n : // In all other cases, we're likely wrapping a user-provided initializer\n // function, so we need to catch errors and handle them appropriately.\n this._trySubscribe(subscriber)\n );\n });\n\n return subscriber;\n }\n\n /** @internal */\n protected _trySubscribe(sink: Subscriber): TeardownLogic {\n try {\n return this._subscribe(sink);\n } catch (err) {\n // We don't need to return anything in this case,\n // because it's just going to try to `add()` to a subscription\n // above.\n sink.error(err);\n }\n }\n\n /**\n * Used as a NON-CANCELLABLE means of subscribing to an observable, for use with\n * APIs that expect promises, like `async/await`. You cannot unsubscribe from this.\n *\n * **WARNING**: Only use this with observables you *know* will complete. If the source\n * observable does not complete, you will end up with a promise that is hung up, and\n * potentially all of the state of an async function hanging out in memory. To avoid\n * this situation, look into adding something like {@link timeout}, {@link take},\n * {@link takeWhile}, or {@link takeUntil} amongst others.\n *\n * #### Example\n *\n * ```ts\n * import { interval, take } from 'rxjs';\n *\n * const source$ = interval(1000).pipe(take(4));\n *\n * async function getTotal() {\n * let total = 0;\n *\n * await source$.forEach(value => {\n * total += value;\n * console.log('observable -> ' + value);\n * });\n *\n * return total;\n * }\n *\n * getTotal().then(\n * total => console.log('Total: ' + total)\n * );\n *\n * // Expected:\n * // 'observable -> 0'\n * // 'observable -> 1'\n * // 'observable -> 2'\n * // 'observable -> 3'\n * // 'Total: 6'\n * ```\n *\n * @param next A handler for each value emitted by the observable.\n * @return A promise that either resolves on observable completion or\n * rejects with the handled error.\n */\n forEach(next: (value: T) => void): Promise;\n\n /**\n * @param next a handler for each value emitted by the observable\n * @param promiseCtor a constructor function used to instantiate the Promise\n * @return a promise that either resolves on observable completion or\n * rejects with the handled error\n * @deprecated Passing a Promise constructor will no longer be available\n * in upcoming versions of RxJS. This is because it adds weight to the library, for very\n * little benefit. If you need this functionality, it is recommended that you either\n * polyfill Promise, or you create an adapter to convert the returned native promise\n * to whatever promise implementation you wanted. Will be removed in v8.\n */\n forEach(next: (value: T) => void, promiseCtor: PromiseConstructorLike): Promise;\n\n forEach(next: (value: T) => void, promiseCtor?: PromiseConstructorLike): Promise {\n promiseCtor = getPromiseCtor(promiseCtor);\n\n return new promiseCtor((resolve, reject) => {\n const subscriber = new SafeSubscriber({\n next: (value) => {\n try {\n next(value);\n } catch (err) {\n reject(err);\n subscriber.unsubscribe();\n }\n },\n error: reject,\n complete: resolve,\n });\n this.subscribe(subscriber);\n }) as Promise;\n }\n\n /** @internal */\n protected _subscribe(subscriber: Subscriber): TeardownLogic {\n return this.source?.subscribe(subscriber);\n }\n\n /**\n * An interop point defined by the es7-observable spec https://github.com/zenparsing/es-observable\n * @return This instance of the observable.\n */\n [Symbol_observable]() {\n return this;\n }\n\n /* tslint:disable:max-line-length */\n pipe(): Observable;\n pipe(op1: OperatorFunction): Observable;\n pipe(op1: OperatorFunction, op2: OperatorFunction): Observable;\n pipe(op1: OperatorFunction, op2: OperatorFunction, op3: OperatorFunction): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction,\n op6: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction,\n op6: OperatorFunction,\n op7: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction,\n op6: OperatorFunction,\n op7: OperatorFunction,\n op8: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction,\n op6: OperatorFunction,\n op7: OperatorFunction,\n op8: OperatorFunction,\n op9: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction,\n op6: OperatorFunction,\n op7: OperatorFunction,\n op8: OperatorFunction,\n op9: OperatorFunction,\n ...operations: OperatorFunction[]\n ): Observable;\n /* tslint:enable:max-line-length */\n\n /**\n * Used to stitch together functional operators into a chain.\n *\n * ## Example\n *\n * ```ts\n * import { interval, filter, map, scan } from 'rxjs';\n *\n * interval(1000)\n * .pipe(\n * filter(x => x % 2 === 0),\n * map(x => x + x),\n * scan((acc, x) => acc + x)\n * )\n * .subscribe(x => console.log(x));\n * ```\n *\n * @return The Observable result of all the operators having been called\n * in the order they were passed in.\n */\n pipe(...operations: OperatorFunction[]): Observable {\n return pipeFromArray(operations)(this);\n }\n\n /* tslint:disable:max-line-length */\n /** @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise */\n toPromise(): Promise;\n /** @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise */\n toPromise(PromiseCtor: typeof Promise): Promise;\n /** @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise */\n toPromise(PromiseCtor: PromiseConstructorLike): Promise;\n /* tslint:enable:max-line-length */\n\n /**\n * Subscribe to this Observable and get a Promise resolving on\n * `complete` with the last emission (if any).\n *\n * **WARNING**: Only use this with observables you *know* will complete. If the source\n * observable does not complete, you will end up with a promise that is hung up, and\n * potentially all of the state of an async function hanging out in memory. To avoid\n * this situation, look into adding something like {@link timeout}, {@link take},\n * {@link takeWhile}, or {@link takeUntil} amongst others.\n *\n * @param [promiseCtor] a constructor function used to instantiate\n * the Promise\n * @return A Promise that resolves with the last value emit, or\n * rejects on an error. If there were no emissions, Promise\n * resolves with undefined.\n * @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise\n */\n toPromise(promiseCtor?: PromiseConstructorLike): Promise {\n promiseCtor = getPromiseCtor(promiseCtor);\n\n return new promiseCtor((resolve, reject) => {\n let value: T | undefined;\n this.subscribe(\n (x: T) => (value = x),\n (err: any) => reject(err),\n () => resolve(value)\n );\n }) as Promise;\n }\n}\n\n/**\n * Decides between a passed promise constructor from consuming code,\n * A default configured promise constructor, and the native promise\n * constructor and returns it. If nothing can be found, it will throw\n * an error.\n * @param promiseCtor The optional promise constructor to passed by consuming code\n */\nfunction getPromiseCtor(promiseCtor: PromiseConstructorLike | undefined) {\n return promiseCtor ?? config.Promise ?? Promise;\n}\n\nfunction isObserver(value: any): value is Observer {\n return value && isFunction(value.next) && isFunction(value.error) && isFunction(value.complete);\n}\n\nfunction isSubscriber(value: any): value is Subscriber {\n return (value && value instanceof Subscriber) || (isObserver(value) && isSubscription(value));\n}\n", "import { Observable } from '../Observable';\nimport { Subscriber } from '../Subscriber';\nimport { OperatorFunction } from '../types';\nimport { isFunction } from './isFunction';\n\n/**\n * Used to determine if an object is an Observable with a lift function.\n */\nexport function hasLift(source: any): source is { lift: InstanceType['lift'] } {\n return isFunction(source?.lift);\n}\n\n/**\n * Creates an `OperatorFunction`. Used to define operators throughout the library in a concise way.\n * @param init The logic to connect the liftedSource to the subscriber at the moment of subscription.\n */\nexport function operate(\n init: (liftedSource: Observable, subscriber: Subscriber) => (() => void) | void\n): OperatorFunction {\n return (source: Observable) => {\n if (hasLift(source)) {\n return source.lift(function (this: Subscriber, liftedSource: Observable) {\n try {\n return init(liftedSource, this);\n } catch (err) {\n this.error(err);\n }\n });\n }\n throw new TypeError('Unable to lift unknown Observable type');\n };\n}\n", "import { Subscriber } from '../Subscriber';\n\n/**\n * Creates an instance of an `OperatorSubscriber`.\n * @param destination The downstream subscriber.\n * @param onNext Handles next values, only called if this subscriber is not stopped or closed. Any\n * error that occurs in this function is caught and sent to the `error` method of this subscriber.\n * @param onError Handles errors from the subscription, any errors that occur in this handler are caught\n * and send to the `destination` error handler.\n * @param onComplete Handles completion notification from the subscription. Any errors that occur in\n * this handler are sent to the `destination` error handler.\n * @param onFinalize Additional teardown logic here. This will only be called on teardown if the\n * subscriber itself is not already closed. This is called after all other teardown logic is executed.\n */\nexport function createOperatorSubscriber(\n destination: Subscriber,\n onNext?: (value: T) => void,\n onComplete?: () => void,\n onError?: (err: any) => void,\n onFinalize?: () => void\n): Subscriber {\n return new OperatorSubscriber(destination, onNext, onComplete, onError, onFinalize);\n}\n\n/**\n * A generic helper for allowing operators to be created with a Subscriber and\n * use closures to capture necessary state from the operator function itself.\n */\nexport class OperatorSubscriber extends Subscriber {\n /**\n * Creates an instance of an `OperatorSubscriber`.\n * @param destination The downstream subscriber.\n * @param onNext Handles next values, only called if this subscriber is not stopped or closed. Any\n * error that occurs in this function is caught and sent to the `error` method of this subscriber.\n * @param onError Handles errors from the subscription, any errors that occur in this handler are caught\n * and send to the `destination` error handler.\n * @param onComplete Handles completion notification from the subscription. Any errors that occur in\n * this handler are sent to the `destination` error handler.\n * @param onFinalize Additional finalization logic here. This will only be called on finalization if the\n * subscriber itself is not already closed. This is called after all other finalization logic is executed.\n * @param shouldUnsubscribe An optional check to see if an unsubscribe call should truly unsubscribe.\n * NOTE: This currently **ONLY** exists to support the strange behavior of {@link groupBy}, where unsubscription\n * to the resulting observable does not actually disconnect from the source if there are active subscriptions\n * to any grouped observable. (DO NOT EXPOSE OR USE EXTERNALLY!!!)\n */\n constructor(\n destination: Subscriber,\n onNext?: (value: T) => void,\n onComplete?: () => void,\n onError?: (err: any) => void,\n private onFinalize?: () => void,\n private shouldUnsubscribe?: () => boolean\n ) {\n // It's important - for performance reasons - that all of this class's\n // members are initialized and that they are always initialized in the same\n // order. This will ensure that all OperatorSubscriber instances have the\n // same hidden class in V8. This, in turn, will help keep the number of\n // hidden classes involved in property accesses within the base class as\n // low as possible. If the number of hidden classes involved exceeds four,\n // the property accesses will become megamorphic and performance penalties\n // will be incurred - i.e. inline caches won't be used.\n //\n // The reasons for ensuring all instances have the same hidden class are\n // further discussed in this blog post from Benedikt Meurer:\n // https://benediktmeurer.de/2018/03/23/impact-of-polymorphism-on-component-based-frameworks-like-react/\n super(destination);\n this._next = onNext\n ? function (this: OperatorSubscriber, value: T) {\n try {\n onNext(value);\n } catch (err) {\n destination.error(err);\n }\n }\n : super._next;\n this._error = onError\n ? function (this: OperatorSubscriber, err: any) {\n try {\n onError(err);\n } catch (err) {\n // Send any errors that occur down stream.\n destination.error(err);\n } finally {\n // Ensure finalization.\n this.unsubscribe();\n }\n }\n : super._error;\n this._complete = onComplete\n ? function (this: OperatorSubscriber) {\n try {\n onComplete();\n } catch (err) {\n // Send any errors that occur down stream.\n destination.error(err);\n } finally {\n // Ensure finalization.\n this.unsubscribe();\n }\n }\n : super._complete;\n }\n\n unsubscribe() {\n if (!this.shouldUnsubscribe || this.shouldUnsubscribe()) {\n const { closed } = this;\n super.unsubscribe();\n // Execute additional teardown if we have any and we didn't already do so.\n !closed && this.onFinalize?.();\n }\n }\n}\n", "import { Subscription } from '../Subscription';\n\ninterface AnimationFrameProvider {\n schedule(callback: FrameRequestCallback): Subscription;\n requestAnimationFrame: typeof requestAnimationFrame;\n cancelAnimationFrame: typeof cancelAnimationFrame;\n delegate:\n | {\n requestAnimationFrame: typeof requestAnimationFrame;\n cancelAnimationFrame: typeof cancelAnimationFrame;\n }\n | undefined;\n}\n\nexport const animationFrameProvider: AnimationFrameProvider = {\n // When accessing the delegate, use the variable rather than `this` so that\n // the functions can be called without being bound to the provider.\n schedule(callback) {\n let request = requestAnimationFrame;\n let cancel: typeof cancelAnimationFrame | undefined = cancelAnimationFrame;\n const { delegate } = animationFrameProvider;\n if (delegate) {\n request = delegate.requestAnimationFrame;\n cancel = delegate.cancelAnimationFrame;\n }\n const handle = request((timestamp) => {\n // Clear the cancel function. The request has been fulfilled, so\n // attempting to cancel the request upon unsubscription would be\n // pointless.\n cancel = undefined;\n callback(timestamp);\n });\n return new Subscription(() => cancel?.(handle));\n },\n requestAnimationFrame(...args) {\n const { delegate } = animationFrameProvider;\n return (delegate?.requestAnimationFrame || requestAnimationFrame)(...args);\n },\n cancelAnimationFrame(...args) {\n const { delegate } = animationFrameProvider;\n return (delegate?.cancelAnimationFrame || cancelAnimationFrame)(...args);\n },\n delegate: undefined,\n};\n", "import { createErrorClass } from './createErrorClass';\n\nexport interface ObjectUnsubscribedError extends Error {}\n\nexport interface ObjectUnsubscribedErrorCtor {\n /**\n * @deprecated Internal implementation detail. Do not construct error instances.\n * Cannot be tagged as internal: https://github.com/ReactiveX/rxjs/issues/6269\n */\n new (): ObjectUnsubscribedError;\n}\n\n/**\n * An error thrown when an action is invalid because the object has been\n * unsubscribed.\n *\n * @see {@link Subject}\n * @see {@link BehaviorSubject}\n *\n * @class ObjectUnsubscribedError\n */\nexport const ObjectUnsubscribedError: ObjectUnsubscribedErrorCtor = createErrorClass(\n (_super) =>\n function ObjectUnsubscribedErrorImpl(this: any) {\n _super(this);\n this.name = 'ObjectUnsubscribedError';\n this.message = 'object unsubscribed';\n }\n);\n", "import { Operator } from './Operator';\nimport { Observable } from './Observable';\nimport { Subscriber } from './Subscriber';\nimport { Subscription, EMPTY_SUBSCRIPTION } from './Subscription';\nimport { Observer, SubscriptionLike, TeardownLogic } from './types';\nimport { ObjectUnsubscribedError } from './util/ObjectUnsubscribedError';\nimport { arrRemove } from './util/arrRemove';\nimport { errorContext } from './util/errorContext';\n\n/**\n * A Subject is a special type of Observable that allows values to be\n * multicasted to many Observers. Subjects are like EventEmitters.\n *\n * Every Subject is an Observable and an Observer. You can subscribe to a\n * Subject, and you can call next to feed values as well as error and complete.\n */\nexport class Subject extends Observable implements SubscriptionLike {\n closed = false;\n\n private currentObservers: Observer[] | null = null;\n\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n observers: Observer[] = [];\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n isStopped = false;\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n hasError = false;\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n thrownError: any = null;\n\n /**\n * Creates a \"subject\" by basically gluing an observer to an observable.\n *\n * @deprecated Recommended you do not use. Will be removed at some point in the future. Plans for replacement still under discussion.\n */\n static create: (...args: any[]) => any = (destination: Observer, source: Observable): AnonymousSubject => {\n return new AnonymousSubject(destination, source);\n };\n\n constructor() {\n // NOTE: This must be here to obscure Observable's constructor.\n super();\n }\n\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n lift(operator: Operator): Observable {\n const subject = new AnonymousSubject(this, this);\n subject.operator = operator as any;\n return subject as any;\n }\n\n /** @internal */\n protected _throwIfClosed() {\n if (this.closed) {\n throw new ObjectUnsubscribedError();\n }\n }\n\n next(value: T) {\n errorContext(() => {\n this._throwIfClosed();\n if (!this.isStopped) {\n if (!this.currentObservers) {\n this.currentObservers = Array.from(this.observers);\n }\n for (const observer of this.currentObservers) {\n observer.next(value);\n }\n }\n });\n }\n\n error(err: any) {\n errorContext(() => {\n this._throwIfClosed();\n if (!this.isStopped) {\n this.hasError = this.isStopped = true;\n this.thrownError = err;\n const { observers } = this;\n while (observers.length) {\n observers.shift()!.error(err);\n }\n }\n });\n }\n\n complete() {\n errorContext(() => {\n this._throwIfClosed();\n if (!this.isStopped) {\n this.isStopped = true;\n const { observers } = this;\n while (observers.length) {\n observers.shift()!.complete();\n }\n }\n });\n }\n\n unsubscribe() {\n this.isStopped = this.closed = true;\n this.observers = this.currentObservers = null!;\n }\n\n get observed() {\n return this.observers?.length > 0;\n }\n\n /** @internal */\n protected _trySubscribe(subscriber: Subscriber): TeardownLogic {\n this._throwIfClosed();\n return super._trySubscribe(subscriber);\n }\n\n /** @internal */\n protected _subscribe(subscriber: Subscriber): Subscription {\n this._throwIfClosed();\n this._checkFinalizedStatuses(subscriber);\n return this._innerSubscribe(subscriber);\n }\n\n /** @internal */\n protected _innerSubscribe(subscriber: Subscriber) {\n const { hasError, isStopped, observers } = this;\n if (hasError || isStopped) {\n return EMPTY_SUBSCRIPTION;\n }\n this.currentObservers = null;\n observers.push(subscriber);\n return new Subscription(() => {\n this.currentObservers = null;\n arrRemove(observers, subscriber);\n });\n }\n\n /** @internal */\n protected _checkFinalizedStatuses(subscriber: Subscriber) {\n const { hasError, thrownError, isStopped } = this;\n if (hasError) {\n subscriber.error(thrownError);\n } else if (isStopped) {\n subscriber.complete();\n }\n }\n\n /**\n * Creates a new Observable with this Subject as the source. You can do this\n * to create custom Observer-side logic of the Subject and conceal it from\n * code that uses the Observable.\n * @return Observable that this Subject casts to.\n */\n asObservable(): Observable {\n const observable: any = new Observable();\n observable.source = this;\n return observable;\n }\n}\n\nexport class AnonymousSubject extends Subject {\n constructor(\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n public destination?: Observer,\n source?: Observable\n ) {\n super();\n this.source = source;\n }\n\n next(value: T) {\n this.destination?.next?.(value);\n }\n\n error(err: any) {\n this.destination?.error?.(err);\n }\n\n complete() {\n this.destination?.complete?.();\n }\n\n /** @internal */\n protected _subscribe(subscriber: Subscriber): Subscription {\n return this.source?.subscribe(subscriber) ?? EMPTY_SUBSCRIPTION;\n }\n}\n", "import { Subject } from './Subject';\nimport { Subscriber } from './Subscriber';\nimport { Subscription } from './Subscription';\n\n/**\n * A variant of Subject that requires an initial value and emits its current\n * value whenever it is subscribed to.\n */\nexport class BehaviorSubject extends Subject {\n constructor(private _value: T) {\n super();\n }\n\n get value(): T {\n return this.getValue();\n }\n\n /** @internal */\n protected _subscribe(subscriber: Subscriber): Subscription {\n const subscription = super._subscribe(subscriber);\n !subscription.closed && subscriber.next(this._value);\n return subscription;\n }\n\n getValue(): T {\n const { hasError, thrownError, _value } = this;\n if (hasError) {\n throw thrownError;\n }\n this._throwIfClosed();\n return _value;\n }\n\n next(value: T): void {\n super.next((this._value = value));\n }\n}\n", "import { TimestampProvider } from '../types';\n\ninterface DateTimestampProvider extends TimestampProvider {\n delegate: TimestampProvider | undefined;\n}\n\nexport const dateTimestampProvider: DateTimestampProvider = {\n now() {\n // Use the variable rather than `this` so that the function can be called\n // without being bound to the provider.\n return (dateTimestampProvider.delegate || Date).now();\n },\n delegate: undefined,\n};\n", "import { Subject } from './Subject';\nimport { TimestampProvider } from './types';\nimport { Subscriber } from './Subscriber';\nimport { Subscription } from './Subscription';\nimport { dateTimestampProvider } from './scheduler/dateTimestampProvider';\n\n/**\n * A variant of {@link Subject} that \"replays\" old values to new subscribers by emitting them when they first subscribe.\n *\n * `ReplaySubject` has an internal buffer that will store a specified number of values that it has observed. Like `Subject`,\n * `ReplaySubject` \"observes\" values by having them passed to its `next` method. When it observes a value, it will store that\n * value for a time determined by the configuration of the `ReplaySubject`, as passed to its constructor.\n *\n * When a new subscriber subscribes to the `ReplaySubject` instance, it will synchronously emit all values in its buffer in\n * a First-In-First-Out (FIFO) manner. The `ReplaySubject` will also complete, if it has observed completion; and it will\n * error if it has observed an error.\n *\n * There are two main configuration items to be concerned with:\n *\n * 1. `bufferSize` - This will determine how many items are stored in the buffer, defaults to infinite.\n * 2. `windowTime` - The amount of time to hold a value in the buffer before removing it from the buffer.\n *\n * Both configurations may exist simultaneously. So if you would like to buffer a maximum of 3 values, as long as the values\n * are less than 2 seconds old, you could do so with a `new ReplaySubject(3, 2000)`.\n *\n * ### Differences with BehaviorSubject\n *\n * `BehaviorSubject` is similar to `new ReplaySubject(1)`, with a couple of exceptions:\n *\n * 1. `BehaviorSubject` comes \"primed\" with a single value upon construction.\n * 2. `ReplaySubject` will replay values, even after observing an error, where `BehaviorSubject` will not.\n *\n * @see {@link Subject}\n * @see {@link BehaviorSubject}\n * @see {@link shareReplay}\n */\nexport class ReplaySubject extends Subject {\n private _buffer: (T | number)[] = [];\n private _infiniteTimeWindow = true;\n\n /**\n * @param _bufferSize The size of the buffer to replay on subscription\n * @param _windowTime The amount of time the buffered items will stay buffered\n * @param _timestampProvider An object with a `now()` method that provides the current timestamp. This is used to\n * calculate the amount of time something has been buffered.\n */\n constructor(\n private _bufferSize = Infinity,\n private _windowTime = Infinity,\n private _timestampProvider: TimestampProvider = dateTimestampProvider\n ) {\n super();\n this._infiniteTimeWindow = _windowTime === Infinity;\n this._bufferSize = Math.max(1, _bufferSize);\n this._windowTime = Math.max(1, _windowTime);\n }\n\n next(value: T): void {\n const { isStopped, _buffer, _infiniteTimeWindow, _timestampProvider, _windowTime } = this;\n if (!isStopped) {\n _buffer.push(value);\n !_infiniteTimeWindow && _buffer.push(_timestampProvider.now() + _windowTime);\n }\n this._trimBuffer();\n super.next(value);\n }\n\n /** @internal */\n protected _subscribe(subscriber: Subscriber): Subscription {\n this._throwIfClosed();\n this._trimBuffer();\n\n const subscription = this._innerSubscribe(subscriber);\n\n const { _infiniteTimeWindow, _buffer } = this;\n // We use a copy here, so reentrant code does not mutate our array while we're\n // emitting it to a new subscriber.\n const copy = _buffer.slice();\n for (let i = 0; i < copy.length && !subscriber.closed; i += _infiniteTimeWindow ? 1 : 2) {\n subscriber.next(copy[i] as T);\n }\n\n this._checkFinalizedStatuses(subscriber);\n\n return subscription;\n }\n\n private _trimBuffer() {\n const { _bufferSize, _timestampProvider, _buffer, _infiniteTimeWindow } = this;\n // If we don't have an infinite buffer size, and we're over the length,\n // use splice to truncate the old buffer values off. Note that we have to\n // double the size for instances where we're not using an infinite time window\n // because we're storing the values and the timestamps in the same array.\n const adjustedBufferSize = (_infiniteTimeWindow ? 1 : 2) * _bufferSize;\n _bufferSize < Infinity && adjustedBufferSize < _buffer.length && _buffer.splice(0, _buffer.length - adjustedBufferSize);\n\n // Now, if we're not in an infinite time window, remove all values where the time is\n // older than what is allowed.\n if (!_infiniteTimeWindow) {\n const now = _timestampProvider.now();\n let last = 0;\n // Search the array for the first timestamp that isn't expired and\n // truncate the buffer up to that point.\n for (let i = 1; i < _buffer.length && (_buffer[i] as number) <= now; i += 2) {\n last = i;\n }\n last && _buffer.splice(0, last + 1);\n }\n }\n}\n", "import { Scheduler } from '../Scheduler';\nimport { Subscription } from '../Subscription';\nimport { SchedulerAction } from '../types';\n\n/**\n * A unit of work to be executed in a `scheduler`. An action is typically\n * created from within a {@link SchedulerLike} and an RxJS user does not need to concern\n * themselves about creating and manipulating an Action.\n *\n * ```ts\n * class Action extends Subscription {\n * new (scheduler: Scheduler, work: (state?: T) => void);\n * schedule(state?: T, delay: number = 0): Subscription;\n * }\n * ```\n */\nexport class Action extends Subscription {\n constructor(scheduler: Scheduler, work: (this: SchedulerAction, state?: T) => void) {\n super();\n }\n /**\n * Schedules this action on its parent {@link SchedulerLike} for execution. May be passed\n * some context object, `state`. May happen at some point in the future,\n * according to the `delay` parameter, if specified.\n * @param state Some contextual data that the `work` function uses when called by the\n * Scheduler.\n * @param delay Time to wait before executing the work, where the time unit is implicit\n * and defined by the Scheduler.\n * @return A subscription in order to be able to unsubscribe the scheduled work.\n */\n public schedule(state?: T, delay: number = 0): Subscription {\n return this;\n }\n}\n", "import type { TimerHandle } from './timerHandle';\ntype SetIntervalFunction = (handler: () => void, timeout?: number, ...args: any[]) => TimerHandle;\ntype ClearIntervalFunction = (handle: TimerHandle) => void;\n\ninterface IntervalProvider {\n setInterval: SetIntervalFunction;\n clearInterval: ClearIntervalFunction;\n delegate:\n | {\n setInterval: SetIntervalFunction;\n clearInterval: ClearIntervalFunction;\n }\n | undefined;\n}\n\nexport const intervalProvider: IntervalProvider = {\n // When accessing the delegate, use the variable rather than `this` so that\n // the functions can be called without being bound to the provider.\n setInterval(handler: () => void, timeout?: number, ...args) {\n const { delegate } = intervalProvider;\n if (delegate?.setInterval) {\n return delegate.setInterval(handler, timeout, ...args);\n }\n return setInterval(handler, timeout, ...args);\n },\n clearInterval(handle) {\n const { delegate } = intervalProvider;\n return (delegate?.clearInterval || clearInterval)(handle as any);\n },\n delegate: undefined,\n};\n", "import { Action } from './Action';\nimport { SchedulerAction } from '../types';\nimport { Subscription } from '../Subscription';\nimport { AsyncScheduler } from './AsyncScheduler';\nimport { intervalProvider } from './intervalProvider';\nimport { arrRemove } from '../util/arrRemove';\nimport { TimerHandle } from './timerHandle';\n\nexport class AsyncAction extends Action {\n public id: TimerHandle | undefined;\n public state?: T;\n // @ts-ignore: Property has no initializer and is not definitely assigned\n public delay: number;\n protected pending: boolean = false;\n\n constructor(protected scheduler: AsyncScheduler, protected work: (this: SchedulerAction, state?: T) => void) {\n super(scheduler, work);\n }\n\n public schedule(state?: T, delay: number = 0): Subscription {\n if (this.closed) {\n return this;\n }\n\n // Always replace the current state with the new state.\n this.state = state;\n\n const id = this.id;\n const scheduler = this.scheduler;\n\n //\n // Important implementation note:\n //\n // Actions only execute once by default, unless rescheduled from within the\n // scheduled callback. This allows us to implement single and repeat\n // actions via the same code path, without adding API surface area, as well\n // as mimic traditional recursion but across asynchronous boundaries.\n //\n // However, JS runtimes and timers distinguish between intervals achieved by\n // serial `setTimeout` calls vs. a single `setInterval` call. An interval of\n // serial `setTimeout` calls can be individually delayed, which delays\n // scheduling the next `setTimeout`, and so on. `setInterval` attempts to\n // guarantee the interval callback will be invoked more precisely to the\n // interval period, regardless of load.\n //\n // Therefore, we use `setInterval` to schedule single and repeat actions.\n // If the action reschedules itself with the same delay, the interval is not\n // canceled. If the action doesn't reschedule, or reschedules with a\n // different delay, the interval will be canceled after scheduled callback\n // execution.\n //\n if (id != null) {\n this.id = this.recycleAsyncId(scheduler, id, delay);\n }\n\n // Set the pending flag indicating that this action has been scheduled, or\n // has recursively rescheduled itself.\n this.pending = true;\n\n this.delay = delay;\n // If this action has already an async Id, don't request a new one.\n this.id = this.id ?? this.requestAsyncId(scheduler, this.id, delay);\n\n return this;\n }\n\n protected requestAsyncId(scheduler: AsyncScheduler, _id?: TimerHandle, delay: number = 0): TimerHandle {\n return intervalProvider.setInterval(scheduler.flush.bind(scheduler, this), delay);\n }\n\n protected recycleAsyncId(_scheduler: AsyncScheduler, id?: TimerHandle, delay: number | null = 0): TimerHandle | undefined {\n // If this action is rescheduled with the same delay time, don't clear the interval id.\n if (delay != null && this.delay === delay && this.pending === false) {\n return id;\n }\n // Otherwise, if the action's delay time is different from the current delay,\n // or the action has been rescheduled before it's executed, clear the interval id\n if (id != null) {\n intervalProvider.clearInterval(id);\n }\n\n return undefined;\n }\n\n /**\n * Immediately executes this action and the `work` it contains.\n */\n public execute(state: T, delay: number): any {\n if (this.closed) {\n return new Error('executing a cancelled action');\n }\n\n this.pending = false;\n const error = this._execute(state, delay);\n if (error) {\n return error;\n } else if (this.pending === false && this.id != null) {\n // Dequeue if the action didn't reschedule itself. Don't call\n // unsubscribe(), because the action could reschedule later.\n // For example:\n // ```\n // scheduler.schedule(function doWork(counter) {\n // /* ... I'm a busy worker bee ... */\n // var originalAction = this;\n // /* wait 100ms before rescheduling the action */\n // setTimeout(function () {\n // originalAction.schedule(counter + 1);\n // }, 100);\n // }, 1000);\n // ```\n this.id = this.recycleAsyncId(this.scheduler, this.id, null);\n }\n }\n\n protected _execute(state: T, _delay: number): any {\n let errored: boolean = false;\n let errorValue: any;\n try {\n this.work(state);\n } catch (e) {\n errored = true;\n // HACK: Since code elsewhere is relying on the \"truthiness\" of the\n // return here, we can't have it return \"\" or 0 or false.\n // TODO: Clean this up when we refactor schedulers mid-version-8 or so.\n errorValue = e ? e : new Error('Scheduled action threw falsy error');\n }\n if (errored) {\n this.unsubscribe();\n return errorValue;\n }\n }\n\n unsubscribe() {\n if (!this.closed) {\n const { id, scheduler } = this;\n const { actions } = scheduler;\n\n this.work = this.state = this.scheduler = null!;\n this.pending = false;\n\n arrRemove(actions, this);\n if (id != null) {\n this.id = this.recycleAsyncId(scheduler, id, null);\n }\n\n this.delay = null!;\n super.unsubscribe();\n }\n }\n}\n", "import { Action } from './scheduler/Action';\nimport { Subscription } from './Subscription';\nimport { SchedulerLike, SchedulerAction } from './types';\nimport { dateTimestampProvider } from './scheduler/dateTimestampProvider';\n\n/**\n * An execution context and a data structure to order tasks and schedule their\n * execution. Provides a notion of (potentially virtual) time, through the\n * `now()` getter method.\n *\n * Each unit of work in a Scheduler is called an `Action`.\n *\n * ```ts\n * class Scheduler {\n * now(): number;\n * schedule(work, delay?, state?): Subscription;\n * }\n * ```\n *\n * @deprecated Scheduler is an internal implementation detail of RxJS, and\n * should not be used directly. Rather, create your own class and implement\n * {@link SchedulerLike}. Will be made internal in v8.\n */\nexport class Scheduler implements SchedulerLike {\n public static now: () => number = dateTimestampProvider.now;\n\n constructor(private schedulerActionCtor: typeof Action, now: () => number = Scheduler.now) {\n this.now = now;\n }\n\n /**\n * A getter method that returns a number representing the current time\n * (at the time this function was called) according to the scheduler's own\n * internal clock.\n * @return A number that represents the current time. May or may not\n * have a relation to wall-clock time. May or may not refer to a time unit\n * (e.g. milliseconds).\n */\n public now: () => number;\n\n /**\n * Schedules a function, `work`, for execution. May happen at some point in\n * the future, according to the `delay` parameter, if specified. May be passed\n * some context object, `state`, which will be passed to the `work` function.\n *\n * The given arguments will be processed an stored as an Action object in a\n * queue of actions.\n *\n * @param work A function representing a task, or some unit of work to be\n * executed by the Scheduler.\n * @param delay Time to wait before executing the work, where the time unit is\n * implicit and defined by the Scheduler itself.\n * @param state Some contextual data that the `work` function uses when called\n * by the Scheduler.\n * @return A subscription in order to be able to unsubscribe the scheduled work.\n */\n public schedule(work: (this: SchedulerAction, state?: T) => void, delay: number = 0, state?: T): Subscription {\n return new this.schedulerActionCtor(this, work).schedule(state, delay);\n }\n}\n", "import { Scheduler } from '../Scheduler';\nimport { Action } from './Action';\nimport { AsyncAction } from './AsyncAction';\nimport { TimerHandle } from './timerHandle';\n\nexport class AsyncScheduler extends Scheduler {\n public actions: Array> = [];\n /**\n * A flag to indicate whether the Scheduler is currently executing a batch of\n * queued actions.\n * @internal\n */\n public _active: boolean = false;\n /**\n * An internal ID used to track the latest asynchronous task such as those\n * coming from `setTimeout`, `setInterval`, `requestAnimationFrame`, and\n * others.\n * @internal\n */\n public _scheduled: TimerHandle | undefined;\n\n constructor(SchedulerAction: typeof Action, now: () => number = Scheduler.now) {\n super(SchedulerAction, now);\n }\n\n public flush(action: AsyncAction): void {\n const { actions } = this;\n\n if (this._active) {\n actions.push(action);\n return;\n }\n\n let error: any;\n this._active = true;\n\n do {\n if ((error = action.execute(action.state, action.delay))) {\n break;\n }\n } while ((action = actions.shift()!)); // exhaust the scheduler queue\n\n this._active = false;\n\n if (error) {\n while ((action = actions.shift()!)) {\n action.unsubscribe();\n }\n throw error;\n }\n }\n}\n", "import { AsyncAction } from './AsyncAction';\nimport { AsyncScheduler } from './AsyncScheduler';\n\n/**\n *\n * Async Scheduler\n *\n * Schedule task as if you used setTimeout(task, duration)\n *\n * `async` scheduler schedules tasks asynchronously, by putting them on the JavaScript\n * event loop queue. It is best used to delay tasks in time or to schedule tasks repeating\n * in intervals.\n *\n * If you just want to \"defer\" task, that is to perform it right after currently\n * executing synchronous code ends (commonly achieved by `setTimeout(deferredTask, 0)`),\n * better choice will be the {@link asapScheduler} scheduler.\n *\n * ## Examples\n * Use async scheduler to delay task\n * ```ts\n * import { asyncScheduler } from 'rxjs';\n *\n * const task = () => console.log('it works!');\n *\n * asyncScheduler.schedule(task, 2000);\n *\n * // After 2 seconds logs:\n * // \"it works!\"\n * ```\n *\n * Use async scheduler to repeat task in intervals\n * ```ts\n * import { asyncScheduler } from 'rxjs';\n *\n * function task(state) {\n * console.log(state);\n * this.schedule(state + 1, 1000); // `this` references currently executing Action,\n * // which we reschedule with new state and delay\n * }\n *\n * asyncScheduler.schedule(task, 3000, 0);\n *\n * // Logs:\n * // 0 after 3s\n * // 1 after 4s\n * // 2 after 5s\n * // 3 after 6s\n * ```\n */\n\nexport const asyncScheduler = new AsyncScheduler(AsyncAction);\n\n/**\n * @deprecated Renamed to {@link asyncScheduler}. Will be removed in v8.\n */\nexport const async = asyncScheduler;\n", "import { AsyncAction } from './AsyncAction';\nimport { Subscription } from '../Subscription';\nimport { QueueScheduler } from './QueueScheduler';\nimport { SchedulerAction } from '../types';\nimport { TimerHandle } from './timerHandle';\n\nexport class QueueAction extends AsyncAction {\n constructor(protected scheduler: QueueScheduler, protected work: (this: SchedulerAction, state?: T) => void) {\n super(scheduler, work);\n }\n\n public schedule(state?: T, delay: number = 0): Subscription {\n if (delay > 0) {\n return super.schedule(state, delay);\n }\n this.delay = delay;\n this.state = state;\n this.scheduler.flush(this);\n return this;\n }\n\n public execute(state: T, delay: number): any {\n return delay > 0 || this.closed ? super.execute(state, delay) : this._execute(state, delay);\n }\n\n protected requestAsyncId(scheduler: QueueScheduler, id?: TimerHandle, delay: number = 0): TimerHandle {\n // If delay exists and is greater than 0, or if the delay is null (the\n // action wasn't rescheduled) but was originally scheduled as an async\n // action, then recycle as an async action.\n\n if ((delay != null && delay > 0) || (delay == null && this.delay > 0)) {\n return super.requestAsyncId(scheduler, id, delay);\n }\n\n // Otherwise flush the scheduler starting with this action.\n scheduler.flush(this);\n\n // HACK: In the past, this was returning `void`. However, `void` isn't a valid\n // `TimerHandle`, and generally the return value here isn't really used. So the\n // compromise is to return `0` which is both \"falsy\" and a valid `TimerHandle`,\n // as opposed to refactoring every other instanceo of `requestAsyncId`.\n return 0;\n }\n}\n", "import { AsyncScheduler } from './AsyncScheduler';\n\nexport class QueueScheduler extends AsyncScheduler {\n}\n", "import { QueueAction } from './QueueAction';\nimport { QueueScheduler } from './QueueScheduler';\n\n/**\n *\n * Queue Scheduler\n *\n * Put every next task on a queue, instead of executing it immediately\n *\n * `queue` scheduler, when used with delay, behaves the same as {@link asyncScheduler} scheduler.\n *\n * When used without delay, it schedules given task synchronously - executes it right when\n * it is scheduled. However when called recursively, that is when inside the scheduled task,\n * another task is scheduled with queue scheduler, instead of executing immediately as well,\n * that task will be put on a queue and wait for current one to finish.\n *\n * This means that when you execute task with `queue` scheduler, you are sure it will end\n * before any other task scheduled with that scheduler will start.\n *\n * ## Examples\n * Schedule recursively first, then do something\n * ```ts\n * import { queueScheduler } from 'rxjs';\n *\n * queueScheduler.schedule(() => {\n * queueScheduler.schedule(() => console.log('second')); // will not happen now, but will be put on a queue\n *\n * console.log('first');\n * });\n *\n * // Logs:\n * // \"first\"\n * // \"second\"\n * ```\n *\n * Reschedule itself recursively\n * ```ts\n * import { queueScheduler } from 'rxjs';\n *\n * queueScheduler.schedule(function(state) {\n * if (state !== 0) {\n * console.log('before', state);\n * this.schedule(state - 1); // `this` references currently executing Action,\n * // which we reschedule with new state\n * console.log('after', state);\n * }\n * }, 0, 3);\n *\n * // In scheduler that runs recursively, you would expect:\n * // \"before\", 3\n * // \"before\", 2\n * // \"before\", 1\n * // \"after\", 1\n * // \"after\", 2\n * // \"after\", 3\n *\n * // But with queue it logs:\n * // \"before\", 3\n * // \"after\", 3\n * // \"before\", 2\n * // \"after\", 2\n * // \"before\", 1\n * // \"after\", 1\n * ```\n */\n\nexport const queueScheduler = new QueueScheduler(QueueAction);\n\n/**\n * @deprecated Renamed to {@link queueScheduler}. Will be removed in v8.\n */\nexport const queue = queueScheduler;\n", "import { AsyncAction } from './AsyncAction';\nimport { AnimationFrameScheduler } from './AnimationFrameScheduler';\nimport { SchedulerAction } from '../types';\nimport { animationFrameProvider } from './animationFrameProvider';\nimport { TimerHandle } from './timerHandle';\n\nexport class AnimationFrameAction extends AsyncAction {\n constructor(protected scheduler: AnimationFrameScheduler, protected work: (this: SchedulerAction, state?: T) => void) {\n super(scheduler, work);\n }\n\n protected requestAsyncId(scheduler: AnimationFrameScheduler, id?: TimerHandle, delay: number = 0): TimerHandle {\n // If delay is greater than 0, request as an async action.\n if (delay !== null && delay > 0) {\n return super.requestAsyncId(scheduler, id, delay);\n }\n // Push the action to the end of the scheduler queue.\n scheduler.actions.push(this);\n // If an animation frame has already been requested, don't request another\n // one. If an animation frame hasn't been requested yet, request one. Return\n // the current animation frame request id.\n return scheduler._scheduled || (scheduler._scheduled = animationFrameProvider.requestAnimationFrame(() => scheduler.flush(undefined)));\n }\n\n protected recycleAsyncId(scheduler: AnimationFrameScheduler, id?: TimerHandle, delay: number = 0): TimerHandle | undefined {\n // If delay exists and is greater than 0, or if the delay is null (the\n // action wasn't rescheduled) but was originally scheduled as an async\n // action, then recycle as an async action.\n if (delay != null ? delay > 0 : this.delay > 0) {\n return super.recycleAsyncId(scheduler, id, delay);\n }\n // If the scheduler queue has no remaining actions with the same async id,\n // cancel the requested animation frame and set the scheduled flag to\n // undefined so the next AnimationFrameAction will request its own.\n const { actions } = scheduler;\n if (id != null && id === scheduler._scheduled && actions[actions.length - 1]?.id !== id) {\n animationFrameProvider.cancelAnimationFrame(id as number);\n scheduler._scheduled = undefined;\n }\n // Return undefined so the action knows to request a new async id if it's rescheduled.\n return undefined;\n }\n}\n", "import { AsyncAction } from './AsyncAction';\nimport { AsyncScheduler } from './AsyncScheduler';\n\nexport class AnimationFrameScheduler extends AsyncScheduler {\n public flush(action?: AsyncAction): void {\n this._active = true;\n // The async id that effects a call to flush is stored in _scheduled.\n // Before executing an action, it's necessary to check the action's async\n // id to determine whether it's supposed to be executed in the current\n // flush.\n // Previous implementations of this method used a count to determine this,\n // but that was unsound, as actions that are unsubscribed - i.e. cancelled -\n // are removed from the actions array and that can shift actions that are\n // scheduled to be executed in a subsequent flush into positions at which\n // they are executed within the current flush.\n let flushId;\n if (action) {\n flushId = action.id;\n } else {\n flushId = this._scheduled;\n this._scheduled = undefined;\n }\n\n const { actions } = this;\n let error: any;\n action = action || actions.shift()!;\n\n do {\n if ((error = action.execute(action.state, action.delay))) {\n break;\n }\n } while ((action = actions[0]) && action.id === flushId && actions.shift());\n\n this._active = false;\n\n if (error) {\n while ((action = actions[0]) && action.id === flushId && actions.shift()) {\n action.unsubscribe();\n }\n throw error;\n }\n }\n}\n", "import { AnimationFrameAction } from './AnimationFrameAction';\nimport { AnimationFrameScheduler } from './AnimationFrameScheduler';\n\n/**\n *\n * Animation Frame Scheduler\n *\n * Perform task when `window.requestAnimationFrame` would fire\n *\n * When `animationFrame` scheduler is used with delay, it will fall back to {@link asyncScheduler} scheduler\n * behaviour.\n *\n * Without delay, `animationFrame` scheduler can be used to create smooth browser animations.\n * It makes sure scheduled task will happen just before next browser content repaint,\n * thus performing animations as efficiently as possible.\n *\n * ## Example\n * Schedule div height animation\n * ```ts\n * // html:

\n * import { animationFrameScheduler } from 'rxjs';\n *\n * const div = document.querySelector('div');\n *\n * animationFrameScheduler.schedule(function(height) {\n * div.style.height = height + \"px\";\n *\n * this.schedule(height + 1); // `this` references currently executing Action,\n * // which we reschedule with new state\n * }, 0, 0);\n *\n * // You will see a div element growing in height\n * ```\n */\n\nexport const animationFrameScheduler = new AnimationFrameScheduler(AnimationFrameAction);\n\n/**\n * @deprecated Renamed to {@link animationFrameScheduler}. Will be removed in v8.\n */\nexport const animationFrame = animationFrameScheduler;\n", "import { Observable } from '../Observable';\nimport { SchedulerLike } from '../types';\n\n/**\n * A simple Observable that emits no items to the Observer and immediately\n * emits a complete notification.\n *\n * Just emits 'complete', and nothing else.\n *\n * ![](empty.png)\n *\n * A simple Observable that only emits the complete notification. It can be used\n * for composing with other Observables, such as in a {@link mergeMap}.\n *\n * ## Examples\n *\n * Log complete notification\n *\n * ```ts\n * import { EMPTY } from 'rxjs';\n *\n * EMPTY.subscribe({\n * next: () => console.log('Next'),\n * complete: () => console.log('Complete!')\n * });\n *\n * // Outputs\n * // Complete!\n * ```\n *\n * Emit the number 7, then complete\n *\n * ```ts\n * import { EMPTY, startWith } from 'rxjs';\n *\n * const result = EMPTY.pipe(startWith(7));\n * result.subscribe(x => console.log(x));\n *\n * // Outputs\n * // 7\n * ```\n *\n * Map and flatten only odd numbers to the sequence `'a'`, `'b'`, `'c'`\n *\n * ```ts\n * import { interval, mergeMap, of, EMPTY } from 'rxjs';\n *\n * const interval$ = interval(1000);\n * const result = interval$.pipe(\n * mergeMap(x => x % 2 === 1 ? of('a', 'b', 'c') : EMPTY),\n * );\n * result.subscribe(x => console.log(x));\n *\n * // Results in the following to the console:\n * // x is equal to the count on the interval, e.g. (0, 1, 2, 3, ...)\n * // x will occur every 1000ms\n * // if x % 2 is equal to 1, print a, b, c (each on its own)\n * // if x % 2 is not equal to 1, nothing will be output\n * ```\n *\n * @see {@link Observable}\n * @see {@link NEVER}\n * @see {@link of}\n * @see {@link throwError}\n */\nexport const EMPTY = new Observable((subscriber) => subscriber.complete());\n\n/**\n * @param scheduler A {@link SchedulerLike} to use for scheduling\n * the emission of the complete notification.\n * @deprecated Replaced with the {@link EMPTY} constant or {@link scheduled} (e.g. `scheduled([], scheduler)`). Will be removed in v8.\n */\nexport function empty(scheduler?: SchedulerLike) {\n return scheduler ? emptyScheduled(scheduler) : EMPTY;\n}\n\nfunction emptyScheduled(scheduler: SchedulerLike) {\n return new Observable((subscriber) => scheduler.schedule(() => subscriber.complete()));\n}\n", "import { SchedulerLike } from '../types';\nimport { isFunction } from './isFunction';\n\nexport function isScheduler(value: any): value is SchedulerLike {\n return value && isFunction(value.schedule);\n}\n", "import { SchedulerLike } from '../types';\nimport { isFunction } from './isFunction';\nimport { isScheduler } from './isScheduler';\n\nfunction last(arr: T[]): T | undefined {\n return arr[arr.length - 1];\n}\n\nexport function popResultSelector(args: any[]): ((...args: unknown[]) => unknown) | undefined {\n return isFunction(last(args)) ? args.pop() : undefined;\n}\n\nexport function popScheduler(args: any[]): SchedulerLike | undefined {\n return isScheduler(last(args)) ? args.pop() : undefined;\n}\n\nexport function popNumber(args: any[], defaultValue: number): number {\n return typeof last(args) === 'number' ? args.pop()! : defaultValue;\n}\n", "export const isArrayLike = ((x: any): x is ArrayLike => x && typeof x.length === 'number' && typeof x !== 'function');", "import { isFunction } from \"./isFunction\";\n\n/**\n * Tests to see if the object is \"thennable\".\n * @param value the object to test\n */\nexport function isPromise(value: any): value is PromiseLike {\n return isFunction(value?.then);\n}\n", "import { InteropObservable } from '../types';\nimport { observable as Symbol_observable } from '../symbol/observable';\nimport { isFunction } from './isFunction';\n\n/** Identifies an input as being Observable (but not necessary an Rx Observable) */\nexport function isInteropObservable(input: any): input is InteropObservable {\n return isFunction(input[Symbol_observable]);\n}\n", "import { isFunction } from './isFunction';\n\nexport function isAsyncIterable(obj: any): obj is AsyncIterable {\n return Symbol.asyncIterator && isFunction(obj?.[Symbol.asyncIterator]);\n}\n", "/**\n * Creates the TypeError to throw if an invalid object is passed to `from` or `scheduled`.\n * @param input The object that was passed.\n */\nexport function createInvalidObservableTypeError(input: any) {\n // TODO: We should create error codes that can be looked up, so this can be less verbose.\n return new TypeError(\n `You provided ${\n input !== null && typeof input === 'object' ? 'an invalid object' : `'${input}'`\n } where a stream was expected. You can provide an Observable, Promise, ReadableStream, Array, AsyncIterable, or Iterable.`\n );\n}\n", "export function getSymbolIterator(): symbol {\n if (typeof Symbol !== 'function' || !Symbol.iterator) {\n return '@@iterator' as any;\n }\n\n return Symbol.iterator;\n}\n\nexport const iterator = getSymbolIterator();\n", "import { iterator as Symbol_iterator } from '../symbol/iterator';\nimport { isFunction } from './isFunction';\n\n/** Identifies an input as being an Iterable */\nexport function isIterable(input: any): input is Iterable {\n return isFunction(input?.[Symbol_iterator]);\n}\n", "import { ReadableStreamLike } from '../types';\nimport { isFunction } from './isFunction';\n\nexport async function* readableStreamLikeToAsyncGenerator(readableStream: ReadableStreamLike): AsyncGenerator {\n const reader = readableStream.getReader();\n try {\n while (true) {\n const { value, done } = await reader.read();\n if (done) {\n return;\n }\n yield value!;\n }\n } finally {\n reader.releaseLock();\n }\n}\n\nexport function isReadableStreamLike(obj: any): obj is ReadableStreamLike {\n // We don't want to use instanceof checks because they would return\n // false for instances from another Realm, like an

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