来源 examples/offline_inference/openai_batch.

使用OpenAI批量文件格式进行离线推理¶

This is a guide to performing batch inference using the OpenAI batch file format, **not** the complete Batch (REST) API.

文件格式¶

OpenAI批处理文件格式由一系列换行分隔的JSON对象组成。

查看示例文件请点击此处。

每行代表一个独立的请求。详情请参阅OpenAI包参考文档。

We currently support `/v1/chat/completions`, `/v1/embeddings`, and `/v1/score` endpoints (completions coming soon).

前提条件¶

本文档中的示例使用 meta-llama/Meta-Llama-3-8B-Instruct。
创建一个用户访问令牌
在您的机器上安装令牌（运行 huggingface-cli login）。
通过访问模型卡片并同意条款和条件，获取受限模型的访问权限。

示例1：使用本地文件运行¶

步骤1：创建您的批处理文件¶

要跟随此示例操作，您可以下载示例批次文件，或在您的工作目录中创建自己的批次文件。

wget https://raw.githubusercontent.com/vllm-project/vllm/main/examples/offline_inference/openai_batch/openai_example_batch.jsonl

创建批处理文件后，其内容应如下所示

$ cat offline_inference/openai_batch/openai_example_batch.jsonl
{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "meta-llama/Meta-Llama-3-8B-Instruct", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_completion_tokens": 1000}}
{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "meta-llama/Meta-Llama-3-8B-Instruct", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_completion_tokens": 1000}}

步骤2：运行批处理¶

该批量运行工具设计用于命令行操作。

您可以通过以下命令运行批处理，该命令会将结果写入名为results.jsonl的文件中

python -m vllm.entrypoints.openai.run_batch \
    -i offline_inference/openai_batch/openai_example_batch.jsonl \
    -o results.jsonl \
    --model meta-llama/Meta-Llama-3-8B-Instruct

或使用命令行：

vllm run-batch \
    -i offline_inference/openai_batch/openai_example_batch.jsonl \
    -o results.jsonl \
    --model meta-llama/Meta-Llama-3-8B-Instruct

步骤3：检查结果¶

您现在应该能在 results.jsonl 中看到结果。您可以通过运行 cat results.jsonl 来查看结果

$ cat results.jsonl
{"id":"vllm-383d1c59835645aeb2e07d004d62a826","custom_id":"request-1","response":{"id":"cmpl-61c020e54b964d5a98fa7527bfcdd378","object":"chat.completion","created":1715633336,"model":"meta-llama/Meta-Llama-3-8B-Instruct","choices":[{"index":0,"message":{"role":"assistant","content":"Hello! It's great to meet you! I'm here to help with any questions or tasks you may have. What's on your mind today?"},"logprobs":null,"finish_reason":"stop","stop_reason":null}],"usage":{"prompt_tokens":25,"total_tokens":56,"completion_tokens":31}},"error":null}
{"id":"vllm-42e3d09b14b04568afa3f1797751a267","custom_id":"request-2","response":{"id":"cmpl-f44d049f6b3a42d4b2d7850bb1e31bcc","object":"chat.completion","created":1715633336,"model":"meta-llama/Meta-Llama-3-8B-Instruct","choices":[{"index":0,"message":{"role":"assistant","content":"*silence*"},"logprobs":null,"finish_reason":"stop","stop_reason":null}],"usage":{"prompt_tokens":27,"total_tokens":32,"completion_tokens":5}},"error":null}

示例2：使用远程文件¶

批处理运行器支持通过http/https访问的远程输入和输出URL。

例如，要针对我们位于https://raw.githubusercontent.com/vllm-project/vllm/main/examples/offline_inference/openai_batch/openai_example_batch.jsonl的示例输入文件运行，您可以执行

python -m vllm.entrypoints.openai.run_batch \
    -i https://raw.githubusercontent.com/vllm-project/vllm/main/examples/offline_inference/openai_batch/openai_example_batch.jsonl \
    -o results.jsonl \
    --model meta-llama/Meta-Llama-3-8B-Instruct

或使用命令行：

vllm run-batch \
    -i https://raw.githubusercontent.com/vllm-project/vllm/main/examples/offline_inference/openai_batch/openai_example_batch.jsonl \
    -o results.jsonl \
    --model meta-llama/Meta-Llama-3-8B-Instruct

示例3：与AWS S3集成¶

要与云对象存储集成，我们推荐使用预签名URL。

[了解更多关于S3预签名URL的信息]

其他先决条件¶

创建S3存储桶。
需要安装 awscli 包（运行 pip install awscli）来配置您的凭证并交互式使用 s3。
配置您的凭据。
需要安装 boto3 Python 包（运行 pip install boto3）来生成预签名URL。

步骤1：上传您的输入脚本¶

要跟随此示例操作，您可以下载示例批次文件，或在您的工作目录中创建自己的批次文件。

wget https://raw.githubusercontent.com/vllm-project/vllm/main/examples/offline_inference/openai_batch/openai_example_batch.jsonl

创建批处理文件后，其内容应如下所示

$ cat offline_inference/openai_batch/openai_example_batch.jsonl
{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "meta-llama/Meta-Llama-3-8B-Instruct", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_completion_tokens": 1000}}
{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "meta-llama/Meta-Llama-3-8B-Instruct", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_completion_tokens": 1000}}

现在将您的批量文件上传到您的S3存储桶中。

aws s3 cp offline_inference/openai_batch/openai_example_batch.jsonl s3://MY_BUCKET/MY_INPUT_FILE.jsonl

步骤2：生成您的预签名URL¶

预签名URL只能通过SDK生成。您可以运行以下Python脚本来生成预签名URL。请确保将占位符MY_BUCKET、MY_INPUT_FILE.jsonl和MY_OUTPUT_FILE.jsonl替换为您实际的存储桶名称和文件名。

(该脚本改编自 https://github.com/awsdocs/aws-doc-sdk-examples/blob/main/python/example_code/s3/s3_basics/presigned_url.py)

import boto3
from botocore.exceptions import ClientError

def generate_presigned_url(s3_client, client_method, method_parameters, expires_in):
    """
    Generate a presigned Amazon S3 URL that can be used to perform an action.

    :param s3_client: A Boto3 Amazon S3 client.
    :param client_method: The name of the client method that the URL performs.
    :param method_parameters: The parameters of the specified client method.
    :param expires_in: The number of seconds the presigned URL is valid for.
    :return: The presigned URL.
    """
    try:
        url = s3_client.generate_presigned_url(
            ClientMethod=client_method, Params=method_parameters, ExpiresIn=expires_in
        )
    except ClientError:
        raise
    return url


s3_client = boto3.client("s3")
input_url = generate_presigned_url(
    s3_client, "get_object", {"Bucket": "MY_BUCKET", "Key": "MY_INPUT_FILE.jsonl"}, 3600
)
output_url = generate_presigned_url(
    s3_client, "put_object", {"Bucket": "MY_BUCKET", "Key": "MY_OUTPUT_FILE.jsonl"}, 3600
)
print(f"{input_url=}")
print(f"{output_url=}")

该脚本应输出

input_url='https://s3.us-west-2.amazonaws.com/MY_BUCKET/MY_INPUT_FILE.jsonl?AWSAccessKeyId=ABCDEFGHIJKLMNOPQRST&Signature=abcdefghijklmnopqrstuvwxyz12345&Expires=1715800091'
output_url='https://s3.us-west-2.amazonaws.com/MY_BUCKET/MY_OUTPUT_FILE.jsonl?AWSAccessKeyId=ABCDEFGHIJKLMNOPQRST&Signature=abcdefghijklmnopqrstuvwxyz12345&Expires=1715800091'

步骤3：使用您的预签名URL运行批量运行器¶

您现在可以运行批处理运行器，使用上一节生成的URL。

python -m vllm.entrypoints.openai.run_batch \
    -i "https://s3.us-west-2.amazonaws.com/MY_BUCKET/MY_INPUT_FILE.jsonl?AWSAccessKeyId=ABCDEFGHIJKLMNOPQRST&Signature=abcdefghijklmnopqrstuvwxyz12345&Expires=1715800091" \
    -o "https://s3.us-west-2.amazonaws.com/MY_BUCKET/MY_OUTPUT_FILE.jsonl?AWSAccessKeyId=ABCDEFGHIJKLMNOPQRST&Signature=abcdefghijklmnopqrstuvwxyz12345&Expires=1715800091" \
    --model --model meta-llama/Meta-Llama-3-8B-Instruct

或使用命令行：

vllm run-batch \
    -i "https://s3.us-west-2.amazonaws.com/MY_BUCKET/MY_INPUT_FILE.jsonl?AWSAccessKeyId=ABCDEFGHIJKLMNOPQRST&Signature=abcdefghijklmnopqrstuvwxyz12345&Expires=1715800091" \
    -o "https://s3.us-west-2.amazonaws.com/MY_BUCKET/MY_OUTPUT_FILE.jsonl?AWSAccessKeyId=ABCDEFGHIJKLMNOPQRST&Signature=abcdefghijklmnopqrstuvwxyz12345&Expires=1715800091" \
    --model --model meta-llama/Meta-Llama-3-8B-Instruct

步骤4：查看结果¶

您的结果现已存储在S3上。您可以通过在终端运行以下命令查看结果

aws s3 cp s3://MY_BUCKET/MY_OUTPUT_FILE.jsonl -

示例4：使用嵌入端点¶

其他先决条件¶

确保您使用的是 vllm >= 0.5.5。

步骤1：创建您的批处理文件¶

将嵌入请求添加到您的批处理文件中。以下是一个示例：

{"custom_id": "request-1", "method": "POST", "url": "/v1/embeddings", "body": {"model": "intfloat/e5-mistral-7b-instruct", "input": "You are a helpful assistant."}}
{"custom_id": "request-2", "method": "POST", "url": "/v1/embeddings", "body": {"model": "intfloat/e5-mistral-7b-instruct", "input": "You are an unhelpful assistant."}}

你甚至可以在批处理文件中混合使用聊天补全和嵌入请求，只要使用的模型同时支持聊天补全和嵌入功能（注意所有请求必须使用相同的模型）。

步骤2：运行批处理¶

你可以使用与之前示例相同的命令来运行批处理。

步骤3：检查结果¶

您可以通过运行cat results.jsonl来检查结果

$ cat results.jsonl
{"id":"vllm-db0f71f7dec244e6bce530e0b4ef908b","custom_id":"request-1","response":{"status_code":200,"request_id":"vllm-batch-3580bf4d4ae54d52b67eee266a6eab20","body":{"id":"embd-33ac2efa7996430184461f2e38529746","object":"list","created":444647,"model":"intfloat/e5-mistral-7b-instruct","data":[{"index":0,"object":"embedding","embedding":[0.016204833984375,0.0092010498046875,0.0018358230590820312,-0.0028228759765625,0.001422882080078125,-0.0031147003173828125,...]}],"usage":{"prompt_tokens":8,"total_tokens":8,"completion_tokens":0}}},"error":null}
...

示例5：使用score端点¶

其他先决条件¶

确保您使用的是 vllm >= 0.7.0。

步骤1：创建您的批处理文件¶

将评分请求添加到您的批处理文件中。以下是一个示例：

{"custom_id": "request-1", "method": "POST", "url": "/v1/score", "body": {"model": "BAAI/bge-reranker-v2-m3", "text_1": "What is the capital of France?", "text_2": ["The capital of Brazil is Brasilia.", "The capital of France is Paris."]}}
{"custom_id": "request-2", "method": "POST", "url": "/v1/score", "body": {"model": "BAAI/bge-reranker-v2-m3", "text_1": "What is the capital of France?", "text_2": ["The capital of Brazil is Brasilia.", "The capital of France is Paris."]}}

您可以在批处理文件中混合使用聊天补全、嵌入和评分请求，只要您使用的模型支持所有这些功能（请注意所有请求必须使用相同的模型）。

步骤2：运行批处理¶

你可以使用与之前示例相同的命令来运行批处理。

步骤3：检查结果¶

您可以通过运行cat results.jsonl来查看结果

$ cat results.jsonl
{"id":"vllm-f87c5c4539184f618e555744a2965987","custom_id":"request-1","response":{"status_code":200,"request_id":"vllm-batch-806ab64512e44071b37d3f7ccd291413","body":{"id":"score-4ee45236897b4d29907d49b01298cdb1","object":"list","created":1737847944,"model":"BAAI/bge-reranker-v2-m3","data":[{"index":0,"object":"score","score":0.0010900497436523438},{"index":1,"object":"score","score":1.0}],"usage":{"prompt_tokens":37,"total_tokens":37,"completion_tokens":0,"prompt_tokens_details":null}}},"error":null}
{"id":"vllm-41990c51a26d4fac8419077f12871099","custom_id":"request-2","response":{"status_code":200,"request_id":"vllm-batch-73ce66379026482699f81974e14e1e99","body":{"id":"score-13f2ffe6ba40460fbf9f7f00ad667d75","object":"list","created":1737847944,"model":"BAAI/bge-reranker-v2-m3","data":[{"index":0,"object":"score","score":0.001094818115234375},{"index":1,"object":"score","score":1.0}],"usage":{"prompt_tokens":37,"total_tokens":37,"completion_tokens":0,"prompt_tokens_details":null}}},"error":null}

示例材料¶

openai_example_batch.jsonl

{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "meta-llama/Meta-Llama-3-8B-Instruct", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_completion_tokens": 1000}}
{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "meta-llama/Meta-Llama-3-8B-Instruct", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_completion_tokens": 1000}}