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185 lines
8.4 KiB
Python
185 lines
8.4 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import json
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import subprocess
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import tempfile
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import pytest
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from vllm.entrypoints.openai.protocol import BatchRequestOutput
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# ruff: noqa: E501
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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}}
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{"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}}
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{"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}}
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{"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}}
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{"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}}"""
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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}}
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{"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}}"""
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INPUT_EMBEDDING_BATCH = """{"custom_id": "request-1", "method": "POST", "url": "/v1/embeddings", "body": {"model": "intfloat/multilingual-e5-small", "input": "You are a helpful assistant."}}
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{"custom_id": "request-2", "method": "POST", "url": "/v1/embeddings", "body": {"model": "intfloat/multilingual-e5-small", "input": "You are an unhelpful assistant."}}
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{"custom_id": "request-3", "method": "POST", "url": "/v1/embeddings", "body": {"model": "intfloat/multilingual-e5-small", "input": "Hello world!"}}
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{"custom_id": "request-4", "method": "POST", "url": "/v1/embeddings", "body": {"model": "NonExistModel", "input": "Hello world!"}}"""
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INPUT_SCORE_BATCH = """{"custom_id": "request-1", "method": "POST", "url": "/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."]}}
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{"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."]}}"""
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INPUT_RERANK_BATCH = """{"custom_id": "request-1", "method": "POST", "url": "/rerank", "body": {"model": "BAAI/bge-reranker-v2-m3", "query": "What is the capital of France?", "documents": ["The capital of Brazil is Brasilia.", "The capital of France is Paris."]}}
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{"custom_id": "request-2", "method": "POST", "url": "/v1/rerank", "body": {"model": "BAAI/bge-reranker-v2-m3", "query": "What is the capital of France?", "documents": ["The capital of Brazil is Brasilia.", "The capital of France is Paris."]}}
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{"custom_id": "request-2", "method": "POST", "url": "/v2/rerank", "body": {"model": "BAAI/bge-reranker-v2-m3", "query": "What is the capital of France?", "documents": ["The capital of Brazil is Brasilia.", "The capital of France is Paris."]}}"""
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def test_empty_file():
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with (
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tempfile.NamedTemporaryFile("w") as input_file,
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tempfile.NamedTemporaryFile("r") as output_file,
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):
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input_file.write("")
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input_file.flush()
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proc = subprocess.Popen(
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[
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"vllm",
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"run-batch",
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"-i",
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input_file.name,
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"-o",
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output_file.name,
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"--model",
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"intfloat/multilingual-e5-small",
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],
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)
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proc.communicate()
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proc.wait()
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assert proc.returncode == 0, f"{proc=}"
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contents = output_file.read()
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assert contents.strip() == ""
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def test_completions():
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with (
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tempfile.NamedTemporaryFile("w") as input_file,
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tempfile.NamedTemporaryFile("r") as output_file,
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):
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input_file.write(INPUT_BATCH)
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input_file.flush()
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proc = subprocess.Popen(
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[
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"vllm",
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"run-batch",
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"-i",
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input_file.name,
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"-o",
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output_file.name,
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"--model",
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"NousResearch/Meta-Llama-3-8B-Instruct",
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],
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)
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proc.communicate()
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proc.wait()
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assert proc.returncode == 0, f"{proc=}"
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contents = output_file.read()
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for line in contents.strip().split("\n"):
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# Ensure that the output format conforms to the openai api.
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# Validation should throw if the schema is wrong.
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BatchRequestOutput.model_validate_json(line)
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def test_completions_invalid_input():
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"""
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Ensure that we fail when the input doesn't conform to the openai api.
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"""
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with (
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tempfile.NamedTemporaryFile("w") as input_file,
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tempfile.NamedTemporaryFile("r") as output_file,
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):
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input_file.write(INVALID_INPUT_BATCH)
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input_file.flush()
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proc = subprocess.Popen(
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[
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"vllm",
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"run-batch",
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"-i",
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input_file.name,
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"-o",
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output_file.name,
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"--model",
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"NousResearch/Meta-Llama-3-8B-Instruct",
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],
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)
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proc.communicate()
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proc.wait()
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assert proc.returncode != 0, f"{proc=}"
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def test_embeddings():
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with (
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tempfile.NamedTemporaryFile("w") as input_file,
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tempfile.NamedTemporaryFile("r") as output_file,
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):
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input_file.write(INPUT_EMBEDDING_BATCH)
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input_file.flush()
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proc = subprocess.Popen(
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[
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"vllm",
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"run-batch",
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"-i",
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input_file.name,
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"-o",
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output_file.name,
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"--model",
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"intfloat/multilingual-e5-small",
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],
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)
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proc.communicate()
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proc.wait()
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assert proc.returncode == 0, f"{proc=}"
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contents = output_file.read()
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for line in contents.strip().split("\n"):
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# Ensure that the output format conforms to the openai api.
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# Validation should throw if the schema is wrong.
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BatchRequestOutput.model_validate_json(line)
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@pytest.mark.parametrize("input_batch", [INPUT_SCORE_BATCH, INPUT_RERANK_BATCH])
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def test_score(input_batch):
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with (
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tempfile.NamedTemporaryFile("w") as input_file,
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tempfile.NamedTemporaryFile("r") as output_file,
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):
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input_file.write(input_batch)
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input_file.flush()
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proc = subprocess.Popen(
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[
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"vllm",
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"run-batch",
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"-i",
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input_file.name,
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"-o",
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output_file.name,
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"--model",
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"BAAI/bge-reranker-v2-m3",
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],
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)
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proc.communicate()
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proc.wait()
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assert proc.returncode == 0, f"{proc=}"
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contents = output_file.read()
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for line in contents.strip().split("\n"):
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# Ensure that the output format conforms to the openai api.
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# Validation should throw if the schema is wrong.
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BatchRequestOutput.model_validate_json(line)
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# Ensure that there is no error in the response.
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line_dict = json.loads(line)
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assert isinstance(line_dict, dict)
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assert line_dict["error"] is None
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