[Misc] Add SPDX-License-Identifier headers to python source files (#12628)
- **Add SPDX license headers to python source files** - **Check for SPDX headers using pre-commit** commit 9d7ef44c3cfb72ca4c32e1c677d99259d10d4745 Author: Russell Bryant <rbryant@redhat.com> Date: Fri Jan 31 14:18:24 2025 -0500 Add SPDX license headers to python source files This commit adds SPDX license headers to python source files as recommended to the project by the Linux Foundation. These headers provide a concise way that is both human and machine readable for communicating license information for each source file. It helps avoid any ambiguity about the license of the code and can also be easily used by tools to help manage license compliance. The Linux Foundation runs license scans against the codebase to help ensure we are in compliance with the licenses of the code we use, including dependencies. Having these headers in place helps that tool do its job. More information can be found on the SPDX site: - https://spdx.dev/learn/handling-license-info/ Signed-off-by: Russell Bryant <rbryant@redhat.com> commit 5a1cf1cb3b80759131c73f6a9dddebccac039dea Author: Russell Bryant <rbryant@redhat.com> Date: Fri Jan 31 14:36:32 2025 -0500 Check for SPDX headers using pre-commit Signed-off-by: Russell Bryant <rbryant@redhat.com> --------- Signed-off-by: Russell Bryant <rbryant@redhat.com>
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# SPDX-License-Identifier: Apache-2.0
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import os
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import sys
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import zipfile
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# SPDX-License-Identifier: Apache-2.0
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import argparse
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import os
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# SPDX-License-Identifier: Apache-2.0
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"""
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LM eval harness on model to compare vs HF baseline computed offline.
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Configs are found in configs/$MODEL.yaml
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# SPDX-License-Identifier: Apache-2.0
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import json
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import os
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from pathlib import Path
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# SPDX-License-Identifier: Apache-2.0
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import argparse
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from transformers import AutoTokenizer
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# SPDX-License-Identifier: Apache-2.0
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import argparse
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import json
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from pathlib import Path
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# SPDX-License-Identifier: Apache-2.0
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from lmdeploy.serve.openai.api_client import APIClient
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api_client = APIClient("http://localhost:8000")
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# SPDX-License-Identifier: Apache-2.0
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import datetime
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import json
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import os
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language: system
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verbose: true
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stages: [commit-msg]
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- id: check-spdx-header
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name: Check SPDX headers
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entry: python tools/check_spdx_header.py
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language: python
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types: [python]
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- id: suggestion
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name: Suggestion
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entry: bash -c 'echo "To bypass pre-commit hooks, add --no-verify to git commit."'
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language: system
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verbose: true
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pass_filenames: false
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# SPDX-License-Identifier: Apache-2.0
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import json
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import os
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import sys
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# SPDX-License-Identifier: Apache-2.0
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"""Benchmark guided decoding throughput."""
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import argparse
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import dataclasses
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# SPDX-License-Identifier: Apache-2.0
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"""Benchmark the latency of processing a single batch of requests."""
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import argparse
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import dataclasses
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# SPDX-License-Identifier: Apache-2.0
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"""
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Offline benchmark to test the long document QA throughput.
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# SPDX-License-Identifier: Apache-2.0
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"""
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Benchmark the efficiency of prefix caching.
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# SPDX-License-Identifier: Apache-2.0
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"""Benchmark offline prioritization."""
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import argparse
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import dataclasses
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# SPDX-License-Identifier: Apache-2.0
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r"""Benchmark online serving throughput.
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On the server side, run one of the following commands:
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# SPDX-License-Identifier: Apache-2.0
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r"""Benchmark online serving throughput with guided decoding.
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On the server side, run one of the following commands:
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# SPDX-License-Identifier: Apache-2.0
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"""Benchmark offline inference throughput."""
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import argparse
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import dataclasses
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# SPDX-License-Identifier: Apache-2.0
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import argparse
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import copy
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import itertools
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# SPDX-License-Identifier: Apache-2.0
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# Cutlass bench utils
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from typing import Iterable, Tuple
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# SPDX-License-Identifier: Apache-2.0
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import argparse
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import copy
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import itertools
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# SPDX-License-Identifier: Apache-2.0
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# Weight Shapes are in the format
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# ([K, N], TP_SPLIT_DIM)
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# Example:
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# SPDX-License-Identifier: Apache-2.0
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import os
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import aiohttp
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# SPDX-License-Identifier: Apache-2.0
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import asyncio
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import itertools
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# SPDX-License-Identifier: Apache-2.0
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import json
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import matplotlib.pyplot as plt
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# SPDX-License-Identifier: Apache-2.0
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import pickle as pkl
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import time
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from dataclasses import dataclass
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# SPDX-License-Identifier: Apache-2.0
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import os
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import sys
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from typing import Optional
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# SPDX-License-Identifier: Apache-2.0
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import time
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import torch
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# SPDX-License-Identifier: Apache-2.0
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import argparse
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import copy
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import json
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# SPDX-License-Identifier: Apache-2.0
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import argparse
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import copy
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import itertools
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# SPDX-License-Identifier: Apache-2.0
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from typing import List
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import torch
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# SPDX-License-Identifier: Apache-2.0
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import argparse
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import time
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from datetime import datetime
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# SPDX-License-Identifier: Apache-2.0
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import random
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import time
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from typing import List, Optional
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# SPDX-License-Identifier: Apache-2.0
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import time
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import torch
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# SPDX-License-Identifier: Apache-2.0
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import itertools
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from typing import Optional, Tuple, Union
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# SPDX-License-Identifier: Apache-2.0
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from itertools import accumulate
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from typing import List, Optional
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# SPDX-License-Identifier: Apache-2.0
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WEIGHT_SHAPES = {
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"ideal": [[4 * 256 * 32, 256 * 32]],
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"mistralai/Mistral-7B-v0.1/TP1": [
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# SPDX-License-Identifier: Apache-2.0
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import math
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import pickle
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import re
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# SPDX-License-Identifier: Apache-2.0
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import dataclasses
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from typing import Any, Callable, Iterable, Optional
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# SPDX-License-Identifier: Apache-2.0
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# Weight Shapes are in the format
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# ([K, N], TP_SPLIT_DIM)
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# Example:
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# SPDX-License-Identifier: Apache-2.0
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import cProfile
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import pstats
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# SPDX-License-Identifier: Apache-2.0
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#!/usr/bin/env python3
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#
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# SPDX-License-Identifier: Apache-2.0
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# ruff: noqa
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# code borrowed from https://github.com/pytorch/pytorch/blob/main/torch/utils/collect_env.py
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# SPDX-License-Identifier: Apache-2.0
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import enum
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from typing import Dict, Union
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# SPDX-License-Identifier: Apache-2.0
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import itertools
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import math
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import os
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# SPDX-License-Identifier: Apache-2.0
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# Configuration file for the Sphinx documentation builder.
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#
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# This file only contains a selection of the most common options. For a full
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# SPDX-License-Identifier: Apache-2.0
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import itertools
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import re
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from dataclasses import dataclass, field
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# SPDX-License-Identifier: Apache-2.0
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from vllm import LLM, SamplingParams
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from vllm.utils import FlexibleArgumentParser
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# SPDX-License-Identifier: Apache-2.0
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from vllm import LLM, SamplingParams
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# Sample prompts.
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# SPDX-License-Identifier: Apache-2.0
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"""
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This example shows how to use vLLM for running offline inference
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with the correct prompt format on audio language models.
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# SPDX-License-Identifier: Apache-2.0
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from vllm import LLM, SamplingParams
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# Sample prompts.
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# SPDX-License-Identifier: Apache-2.0
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from vllm import LLM
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# Sample prompts.
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# SPDX-License-Identifier: Apache-2.0
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from vllm import LLM, SamplingParams
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llm = LLM(model="meta-llama/Meta-Llama-3-8B-Instruct")
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# SPDX-License-Identifier: Apache-2.0
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# ruff: noqa
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import json
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import random
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# SPDX-License-Identifier: Apache-2.0
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from vllm import LLM
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# Sample prompts.
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# SPDX-License-Identifier: Apache-2.0
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from dataclasses import asdict
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from vllm import LLM, SamplingParams
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# SPDX-License-Identifier: Apache-2.0
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from vllm import LLM, SamplingParams
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# Sample prompts.
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# SPDX-License-Identifier: Apache-2.0
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"""
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This example shows how to use Ray Data for running offline batch inference
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distributively on a multi-nodes cluster.
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# SPDX-License-Identifier: Apache-2.0
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from vllm import LLM
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# Sample prompts.
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# SPDX-License-Identifier: Apache-2.0
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'''
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Demonstrate prompting of text-to-text
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encoder/decoder models, specifically BART
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# SPDX-License-Identifier: Apache-2.0
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'''
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Demonstrate prompting of text-to-text
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encoder/decoder models, specifically Florence-2
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# SPDX-License-Identifier: Apache-2.0
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from huggingface_hub import hf_hub_download
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from vllm import LLM, SamplingParams
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# SPDX-License-Identifier: Apache-2.0
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import argparse
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from typing import List, Tuple
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# SPDX-License-Identifier: Apache-2.0
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"""
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This example shows how to use LoRA with different quantization techniques
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for offline inference.
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# SPDX-License-Identifier: Apache-2.0
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import gc
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import time
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from typing import List
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# SPDX-License-Identifier: Apache-2.0
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"""
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This example shows how to use the multi-LoRA functionality
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for offline inference.
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# SPDX-License-Identifier: Apache-2.0
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from vllm import LLM, SamplingParams
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# Sample prompts.
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# SPDX-License-Identifier: Apache-2.0
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import os
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from vllm import LLM, SamplingParams
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# SPDX-License-Identifier: Apache-2.0
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# ruff: noqa
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import argparse
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# SPDX-License-Identifier: Apache-2.0
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from vllm import LLM, SamplingParams
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from vllm.distributed import cleanup_dist_env_and_memory
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# SPDX-License-Identifier: Apache-2.0
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import inspect
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import json
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import os
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# SPDX-License-Identifier: Apache-2.0
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import argparse
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import dataclasses
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import os
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# SPDX-License-Identifier: Apache-2.0
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"""
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a simple demonstration of RLHF with vLLM, inspired by
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the OpenRLHF framework https://github.com/OpenRLHF/OpenRLHF .
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# SPDX-License-Identifier: Apache-2.0
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"""
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Saves each worker's model state dict directly to a checkpoint, which enables a
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fast load path for large tensor-parallel models where each worker only needs to
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# SPDX-License-Identifier: Apache-2.0
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from vllm import LLM
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# Sample prompts.
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# SPDX-License-Identifier: Apache-2.0
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import os
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import time
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# SPDX-License-Identifier: Apache-2.0
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from enum import Enum
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from pydantic import BaseModel
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# SPDX-License-Identifier: Apache-2.0
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"""
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experimental support for tensor-parallel inference with torchrun,
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see https://github.com/vllm-project/vllm/issues/11400 for
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# SPDX-License-Identifier: Apache-2.0
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from vllm import LLM, SamplingParams
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prompts = [
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# SPDX-License-Identifier: Apache-2.0
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"""
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This example shows how to use vLLM for running offline inference with
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the correct prompt format on vision language models for text generation.
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# SPDX-License-Identifier: Apache-2.0
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"""
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This example shows how to use vLLM for running offline inference with
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the correct prompt format on vision language models for multimodal embedding.
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# SPDX-License-Identifier: Apache-2.0
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"""
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This example shows how to use vLLM for running offline inference with
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multi-image input on vision language models for text generation,
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# SPDX-License-Identifier: Apache-2.0
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import time
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from vllm import LLM, SamplingParams
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# SPDX-License-Identifier: Apache-2.0
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"""Example Python client for `vllm.entrypoints.api_server`
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NOTE: The API server is used only for demonstration and simple performance
|
||||
benchmarks. It is not intended for production use.
|
||||
|
@ -1,3 +1,4 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
"""
|
||||
Example of using the OpenAI entrypoint's rerank API which is compatible with
|
||||
the Cohere SDK: https://github.com/cohere-ai/cohere-python
|
||||
|
@ -1,3 +1,5 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
import argparse
|
||||
|
||||
import gradio as gr
|
||||
|
@ -1,3 +1,5 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
import argparse
|
||||
import json
|
||||
|
||||
|
@ -1,3 +1,4 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
"""
|
||||
Example of using the OpenAI entrypoint's rerank API which is compatible with
|
||||
Jina and Cohere https://jina.ai/reranker
|
||||
|
@ -1,3 +1,5 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
from openai import OpenAI
|
||||
|
||||
# Modify OpenAI's API key and API base to use vLLM's API server.
|
||||
|
@ -1,3 +1,4 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
"""An example showing how to use vLLM to serve multimodal models
|
||||
and run online serving with OpenAI client.
|
||||
|
||||
|
@ -1,3 +1,4 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
"""
|
||||
Set up this example by starting a vLLM OpenAI-compatible server with tool call
|
||||
options enabled. For example:
|
||||
|
@ -1,3 +1,5 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
from enum import Enum
|
||||
|
||||
from openai import OpenAI
|
||||
|
@ -1,3 +1,4 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
"""
|
||||
An example shows how to generate chat completions from reasoning models
|
||||
like DeepSeekR1.
|
||||
|
@ -1,3 +1,4 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
"""
|
||||
An example shows how to generate chat completions from reasoning models
|
||||
like DeepSeekR1.
|
||||
|
@ -1,3 +1,5 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
import argparse
|
||||
import base64
|
||||
import io
|
||||
|
@ -1,3 +1,5 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
from openai import OpenAI
|
||||
|
||||
# Modify OpenAI's API key and API base to use vLLM's API server.
|
||||
|
@ -1,3 +1,4 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
"""
|
||||
Example online usage of Score API.
|
||||
|
||||
|
@ -1,3 +1,5 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
from openai import OpenAI
|
||||
|
||||
# Modify OpenAI's API key and API base to use vLLM's API server.
|
||||
|
@ -1,3 +1,4 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
"""
|
||||
Example online usage of Pooling API.
|
||||
|
||||
|
@ -1,3 +1,5 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
import requests
|
||||
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import (
|
||||
OTLPSpanExporter)
|
||||
|
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Reference in New Issue
Block a user