server/vllm/.readthedocs.yaml

-# Read the Docs configuration file
-# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
-
-version: 2
-
-build:
-  os: ubuntu-22.04
-  tools:
-    python: "3.8"
-
-sphinx:
-   configuration: docs/source/conf.py
-
-# If using Sphinx, optionally build your docs in additional formats such as PDF
-formats:
-   - pdf
-
-# Optionally declare the Python requirements required to build your docs
-python:
-   install:
-   - requirements: docs/requirements-docs.txt

server/vllm/CONTRIBUTING.md

-# Contributing to vLLM
-
-Thank you for your interest in contributing to vLLM!
-Our community is open to everyone and welcomes all kinds of contributions, no matter how small or large.
-There are several ways you can contribute to the project:
-
-- Identify and report any issues or bugs.
-- Request or add a new model.
-- Suggest or implement new features.
-
-However, remember that contributions aren't just about code.
-We believe in the power of community support; thus, answering queries, assisting others, and enhancing the documentation are highly regarded and beneficial contributions.
-
-Finally, one of the most impactful ways to support us is by raising awareness about vLLM.
-Talk about it in your blog posts, highlighting how it's driving your incredible projects.
-Express your support on Twitter if vLLM aids you, or simply offer your appreciation by starring our repository.
-
-
-## Setup for development
-
-### Build from source
-
-```bash
-pip install -r requirements.txt
-pip install -e .  # This may take several minutes.
-```
-
-### Testing
-
-```bash
-pip install -r requirements-dev.txt
-
-# Static type checking
-mypy
-# Unit tests
-pytest tests/
-```
-**Note:** Currently, the repository does not pass the mypy tests.
-
-
-## Contributing Guidelines
-
-### Issue Reporting
-
-If you encounter a bug or have a feature request, please check our issues page first to see if someone else has already reported it.
-If not, please file a new issue, providing as much relevant information as possible.
-
-### Coding Style Guide
-
-In general, we adhere to [Google Python style guide](https://google.github.io/styleguide/pyguide.html) and [Google C++ style guide](https://google.github.io/styleguide/cppguide.html).
-
-We include a formatting script [`format.sh`](./format.sh) to format the code.
-
-### Pull Requests
-
-When submitting a pull request:
-
-1. Make sure your code has been rebased on top of the latest commit on the main branch.
-2. Ensure code is properly formatted by running [`format.sh`](./format.sh).
-3. Include a detailed description of the changes in the pull request.
-Explain why you made the changes you did.
-If your pull request fixes an open issue, please include a reference to it in the description.
-
-### Code Reviews
-
-All submissions, including submissions by project members, require a code review.
-To make the review process as smooth as possible, please:
-
-1. Keep your changes as concise as possible.
-If your pull request involves multiple unrelated changes, consider splitting it into separate pull requests.
-2. Respond to all comments within a reasonable time frame.
-If a comment isn't clear or you disagree with a suggestion, feel free to ask for clarification or discuss the suggestion.
-
-### Thank You
-
-Finally, thank you for taking the time to read these guidelines and for your interest in contributing to vLLM.
-Your contributions make vLLM a great tool for everyone!

server/vllm/LICENSE

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server/vllm/MANIFEST.in

-include LICENSE
-include requirements.txt
-
-recursive-include csrc *

server/vllm/README.md

-Branching out of 95248677014cb10a9dbaa2e72f688e1a6e6cf566

server/vllm/benchmarks/README.md

-# Benchmarking vLLM
-
-## Downloading the ShareGPT dataset
-
-You can download the dataset by running:
-```bash
-wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
-```

server/vllm/benchmarks/benchmark_latency.py

-"""Benchmark the latency of processing a single batch of requests."""
-import argparse
-import time
-
-import numpy as np
-import torch
-from tqdm import tqdm
-
-from vllm import LLM, SamplingParams
-
-
-def main(args: argparse.Namespace):
-    print(args)
-
-    # Process all the requests in a single batch if possible.
-    # NOTE(woosuk): If the request cannot be processed in a single batch,
-    # the engine will automatically process the request in multiple batches.
-    llm = LLM(
-        model=args.model,
-        tokenizer=args.tokenizer,
-        quantization=args.quantization,
-        tensor_parallel_size=args.tensor_parallel_size,
-        max_num_seqs=args.batch_size,
-        max_num_batched_tokens=args.batch_size * args.input_len,
-        trust_remote_code=args.trust_remote_code,
-        dtype=args.dtype,
-    )
-
-    sampling_params = SamplingParams(
-        n=args.n,
-        temperature=0.0 if args.use_beam_search else 1.0,
-        top_p=1.0,
-        use_beam_search=args.use_beam_search,
-        ignore_eos=True,
-        max_tokens=args.output_len,
-    )
-    print(sampling_params)
-    dummy_prompt_token_ids = [[0] * args.input_len] * args.batch_size
-
-    def run_to_completion(profile: bool = False):
-        if profile:
-            torch.cuda.cudart().cudaProfilerStart()
-        start_time = time.perf_counter()
-
-        llm.generate(prompt_token_ids=dummy_prompt_token_ids,
-                     sampling_params=sampling_params,
-                     use_tqdm=False)
-
-        end_time = time.perf_counter()
-        latency = end_time - start_time
-        if profile:
-            torch.cuda.cudart().cudaProfilerStop()
-        return latency
-
-    print("Warming up...")
-    run_to_completion(profile=False)
-
-    # Benchmark.
-    latencies = []
-    for _ in tqdm(range(args.num_iters), desc="Profiling iterations"):
-        latencies.append(run_to_completion(profile=False))
-    print(f'Avg latency: {np.mean(latencies)} seconds')
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser(
-        description='Benchmark the latency of processing a single batch of '
-        'requests till completion.')
-    parser.add_argument('--model', type=str, default='facebook/opt-125m')
-    parser.add_argument('--tokenizer', type=str, default=None)
-    parser.add_argument('--quantization',
-                        '-q',
-                        choices=['awq', None],
-                        default=None)
-    parser.add_argument('--tensor-parallel-size', '-tp', type=int, default=1)
-    parser.add_argument('--input-len', type=int, default=32)
-    parser.add_argument('--output-len', type=int, default=128)
-    parser.add_argument('--batch-size', type=int, default=8)
-    parser.add_argument('--n',
-                        type=int,
-                        default=1,
-                        help='Number of generated sequences per prompt.')
-    parser.add_argument('--use-beam-search', action='store_true')
-    parser.add_argument('--num-iters',
-                        type=int,
-                        default=3,
-                        help='Number of iterations to run.')
-    parser.add_argument('--trust-remote-code',
-                        action='store_true',
-                        help='trust remote code from huggingface')
-    parser.add_argument(
-        '--dtype',
-        type=str,
-        default='auto',
-        choices=['auto', 'half', 'float16', 'bfloat16', 'float', 'float32'],
-        help='data type for model weights and activations. '
-        'The "auto" option will use FP16 precision '
-        'for FP32 and FP16 models, and BF16 precision '
-        'for BF16 models.')
-    args = parser.parse_args()
-    main(args)