test_run_batch.py

# SPDX-License-Identifier: Apache-2.0

import json
import subprocess
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}}

{"custom_id": "request-3", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "NonExistModel", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 1000}}
{"custom_id": "request-4", "method": "POST", "url": "/bad_url", "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}}
{"custom_id": "request-5", "method": "POST", "url": "/v1/chat/completions", "body": {"stream": "True", "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}}"""

INPUT_EMBEDDING_BATCH = """{"custom_id": "request-1", "method": "POST", "url": "/v1/embeddings", "body": {"model": "intfloat/multilingual-e5-small", "input": "You are a helpful assistant."}}
{"custom_id": "request-2", "method": "POST", "url": "/v1/embeddings", "body": {"model": "intfloat/multilingual-e5-small", "input": "You are an unhelpful assistant."}}

{"custom_id": "request-3", "method": "POST", "url": "/v1/embeddings", "body": {"model": "intfloat/multilingual-e5-small", "input": "Hello world!"}}
{"custom_id": "request-4", "method": "POST", "url": "/v1/embeddings", "body": {"model": "NonExistModel", "input": "Hello world!"}}"""

INPUT_SCORE_BATCH = """{"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."]}}"""


def test_empty_file():
    with tempfile.NamedTemporaryFile(
            "w") as input_file, tempfile.NamedTemporaryFile(
                "r") as output_file:
        input_file.write("")
        input_file.flush()
        proc = subprocess.Popen([
            "vllm", "run-batch", "-i", input_file.name, "-o", output_file.name,
            "--model", "intfloat/multilingual-e5-small"
        ], )
        proc.communicate()
        proc.wait()
        assert proc.returncode == 0, f"{proc=}"

        contents = output_file.read()
        assert contents.strip() == ""


def test_completions():
    with tempfile.NamedTemporaryFile(
            "w") as input_file, tempfile.NamedTemporaryFile(
                "r") as output_file:
        input_file.write(INPUT_BATCH)
        input_file.flush()
        proc = subprocess.Popen([
            "vllm", "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_completions_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([
            "vllm", "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=}"


def test_embeddings():
    with tempfile.NamedTemporaryFile(
            "w") as input_file, tempfile.NamedTemporaryFile(
                "r") as output_file:
        input_file.write(INPUT_EMBEDDING_BATCH)
        input_file.flush()
        proc = subprocess.Popen([
            "vllm", "run-batch", "-i", input_file.name, "-o", output_file.name,
            "--model", "intfloat/multilingual-e5-small"
        ], )
        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_score():
    with tempfile.NamedTemporaryFile(
            "w") as input_file, tempfile.NamedTemporaryFile(
                "r") as output_file:
        input_file.write(INPUT_SCORE_BATCH)
        input_file.flush()
        proc = subprocess.Popen([
            "vllm",
            "run-batch",
            "-i",
            input_file.name,
            "-o",
            output_file.name,
            "--model",
            "BAAI/bge-reranker-v2-m3",
        ], )
        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)

            # Ensure that there is no error in the response.
            line_dict = json.loads(line)
            assert isinstance(line_dict, dict)
            assert line_dict["error"] is None