test_online.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

import base64
import json

import numpy as np
import openai
import pytest
import pytest_asyncio
import requests
import torch
import torch.nn.functional as F

from tests.models.language.pooling.embed_utils import run_embedding_correctness_test
from tests.models.utils import check_embeddings_close
from tests.utils import RemoteOpenAIServer
from vllm.entrypoints.pooling.embed.protocol import EmbeddingResponse
from vllm.entrypoints.pooling.pooling.protocol import PoolingResponse
from vllm.platforms import current_platform
from vllm.tokenizers import get_tokenizer
from vllm.utils.serial_utils import (
    EMBED_DTYPE_TO_TORCH_DTYPE,
    ENDIANNESS,
    MetadataItem,
    binary2tensor,
    build_metadata_items,
    decode_pooling_output,
)

MODEL_NAME = "intfloat/multilingual-e5-small"
DUMMY_CHAT_TEMPLATE = """{% for message in messages %}{{message['role'] + ': ' + message['content'] + '\\n'}}{% endfor %}"""  # noqa: E501
DTYPE = "bfloat16"
input_text = "The best thing about vLLM is that it supports many different models"
input_tokens = [
    0,
    581,
    2965,
    13580,
    1672,
    81,
    23708,
    594,
    83,
    450,
    442,
    8060,
    7,
    5941,
    12921,
    115774,
    2,
]

if current_platform.is_rocm():
    # Disable Flash/MemEfficient SDP on ROCm to avoid HF Transformers
    # accuracy issues: https://github.com/vllm-project/vllm/issues/30167
    # TODO: Remove once ROCm SDP accuracy issues are resolved on HuggingFace
    torch.backends.cuda.enable_flash_sdp(False)
    torch.backends.cuda.enable_mem_efficient_sdp(False)
    torch.backends.cuda.enable_math_sdp(True)


@pytest.fixture(scope="module")
def server():
    args = [
        "--runner",
        "pooling",
        # use half precision for speed and memory savings in CI environment
        "--dtype",
        DTYPE,
        "--enforce-eager",
        "--max-model-len",
        "512",
        "--chat-template",
        DUMMY_CHAT_TEMPLATE,
    ]

    # ROCm: Use Flex Attention to support encoder-only self-attention.
    if current_platform.is_rocm():
        args.extend(["--attention-backend", "FLEX_ATTENTION"])

    with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
        yield remote_server


@pytest_asyncio.fixture
async def client(server):
    async with server.get_async_client() as async_client:
        yield async_client


@pytest.fixture(scope="module")
def hf_model(hf_runner):
    with hf_runner(MODEL_NAME, dtype=DTYPE, is_sentence_transformer=True) as hf_model:
        yield hf_model


@pytest.mark.asyncio
@pytest.mark.parametrize("model_name", [MODEL_NAME])
async def test_basic(
    server: RemoteOpenAIServer, client: openai.AsyncOpenAI, model_name: str
):
    # test /v1/models
    response = requests.get(server.url_for("/v1/models"))
    model = response.json()["data"][0]["id"]
    assert model == MODEL_NAME

    models = await client.models.list()
    models = models.data
    served_model = models[0]
    assert served_model.id == MODEL_NAME

    # test /tokenize
    response = requests.post(
        server.url_for("/tokenize"),
        json={"model": model_name, "prompt": input_text},
    )
    assert response.json()["tokens"] == input_tokens


@pytest.mark.asyncio
@pytest.mark.parametrize("model_name", [MODEL_NAME])
async def test_completion_request(
    client: openai.AsyncOpenAI, model_name: str, hf_model
):
    # test input: str
    embedding_response = await client.embeddings.create(
        model=model_name,
        input=input_text,
        encoding_format="float",
    )
    embeddings = EmbeddingResponse.model_validate(
        embedding_response.model_dump(mode="json")
    )

    assert embeddings.id is not None
    assert len(embeddings.data) == 1
    assert len(embeddings.data[0].embedding) == 384
    assert embeddings.usage.completion_tokens == 0
    assert embeddings.usage.prompt_tokens == len(input_tokens)
    assert embeddings.usage.total_tokens == len(input_tokens)

    vllm_outputs = [d.embedding for d in embeddings.data]
    run_embedding_correctness_test(hf_model, [input_text], vllm_outputs)

    # test input: list[int]
    embedding_response = await client.embeddings.create(
        model=model_name,
        input=input_tokens,
        encoding_format="float",
    )
    embeddings = EmbeddingResponse.model_validate(
        embedding_response.model_dump(mode="json")
    )

    assert embeddings.id is not None
    assert len(embeddings.data) == 1
    assert len(embeddings.data[0].embedding) == 384
    assert embeddings.usage.completion_tokens == 0
    assert embeddings.usage.prompt_tokens == len(input_tokens)
    assert embeddings.usage.total_tokens == len(input_tokens)

    vllm_outputs = [d.embedding for d in embeddings.data]
    run_embedding_correctness_test(hf_model, [input_text], vllm_outputs)


@pytest.mark.asyncio
@pytest.mark.parametrize("model_name", [MODEL_NAME])
async def test_completion_request_batched(
    client: openai.AsyncOpenAI, model_name: str, hf_model
):
    N = 10
    input_texts = [input_text] * N

    # test input: list[str]
    embedding_response = await client.embeddings.create(
        model=model_name,
        input=input_texts,
        encoding_format="float",
    )
    embeddings = EmbeddingResponse.model_validate(
        embedding_response.model_dump(mode="json")
    )

    assert embeddings.id is not None
    assert len(embeddings.data) == N
    assert len(embeddings.data[0].embedding) == 384
    assert embeddings.usage.completion_tokens == 0
    assert embeddings.usage.prompt_tokens == len(input_tokens) * N
    assert embeddings.usage.total_tokens == len(input_tokens) * N

    vllm_outputs = [d.embedding for d in embeddings.data]
    run_embedding_correctness_test(hf_model, input_texts, vllm_outputs)

    # test list[list[int]]
    embedding_response = await client.embeddings.create(
        model=model_name,
        input=[input_tokens] * N,
        encoding_format="float",
    )
    embeddings = EmbeddingResponse.model_validate(
        embedding_response.model_dump(mode="json")
    )

    assert embeddings.id is not None
    assert len(embeddings.data) == N
    assert len(embeddings.data[0].embedding) == 384
    assert embeddings.usage.completion_tokens == 0
    assert embeddings.usage.prompt_tokens == len(input_tokens) * N
    assert embeddings.usage.total_tokens == len(input_tokens) * N

    vllm_outputs = [d.embedding for d in embeddings.data]
    run_embedding_correctness_test(hf_model, input_texts, vllm_outputs)


@pytest.mark.asyncio
@pytest.mark.parametrize("model_name", [MODEL_NAME])
async def test_conversation_embedding(
    server: RemoteOpenAIServer, client: openai.AsyncOpenAI, model_name: str
):
    messages = [
        {
            "role": "user",
            "content": "The cat sat on the mat.",
        },
        {
            "role": "assistant",
            "content": "A feline was resting on a rug.",
        },
        {
            "role": "user",
            "content": "Stars twinkle brightly in the night sky.",
        },
    ]

    chat_response = requests.post(
        server.url_for("v1/embeddings"),
        json={
            "model": model_name,
            "messages": messages,
            "encoding_format": "float",
        },
    )
    chat_response.raise_for_status()
    chat_embeddings = EmbeddingResponse.model_validate(chat_response.json())

    tokenizer = get_tokenizer(tokenizer_name=model_name)
    prompt = tokenizer.apply_chat_template(
        messages,
        chat_template=DUMMY_CHAT_TEMPLATE,
        add_generation_prompt=True,
        continue_final_message=False,
        tokenize=False,
    )
    completion_response = await client.embeddings.create(
        model=model_name,
        input=prompt,
        encoding_format="float",
        # To be consistent with chat
        extra_body={"add_special_tokens": False},
    )
    completion_embeddings = EmbeddingResponse.model_validate(
        completion_response.model_dump(mode="json")
    )

    assert chat_embeddings.id is not None
    assert completion_embeddings.id is not None
    assert chat_embeddings.created <= completion_embeddings.created
    assert chat_embeddings.model_dump(exclude={"id", "created"}) == (
        completion_embeddings.model_dump(exclude={"id", "created"})
    )


@pytest.mark.asyncio
@pytest.mark.parametrize("model_name", [MODEL_NAME])
async def test_truncate_prompt_tokens(client: openai.AsyncOpenAI, model_name: str):
    input_texts = [
        "Como o Brasil pode fomentar o desenvolvimento de modelos de IA?",
    ]

    # test single embedding
    embedding_response = await client.embeddings.create(
        model=model_name, input=input_texts, extra_body={"truncate_prompt_tokens": 10}
    )
    embeddings = EmbeddingResponse.model_validate(
        embedding_response.model_dump(mode="json")
    )

    assert embeddings.id is not None
    assert len(embeddings.data) == 1
    assert len(embeddings.data[0].embedding) == 384
    assert embeddings.usage.completion_tokens == 0
    assert embeddings.usage.prompt_tokens == 10
    assert embeddings.usage.total_tokens == 10

    input_tokens = [
        1,
        24428,
        289,
        18341,
        26165,
        285,
        19323,
        283,
        289,
        26789,
        3871,
        28728,
        9901,
        340,
        2229,
        385,
        340,
        315,
        28741,
        28804,
        2,
    ]
    embedding_response = await client.embeddings.create(
        model=model_name, input=input_tokens, extra_body={"truncate_prompt_tokens": 10}
    )
    embeddings = EmbeddingResponse.model_validate(
        embedding_response.model_dump(mode="json")
    )

    assert embeddings.id is not None
    assert len(embeddings.data) == 1
    assert len(embeddings.data[0].embedding) == 384
    assert embeddings.usage.completion_tokens == 0
    assert embeddings.usage.prompt_tokens == 10
    assert embeddings.usage.total_tokens == 10

    # invalid_truncate_prompt_tokens
    input_texts = [
        "Como o Brasil pode fomentar o desenvolvimento de modelos de IA?",
    ]

    with pytest.raises(openai.BadRequestError):
        response = await client.embeddings.create(
            model=model_name,
            input=input_texts,
            extra_body={"truncate_prompt_tokens": 8193},
        )
        assert "error" in response.object
        assert (
            "truncate_prompt_tokens value is greater than max_model_len. "
            "Please, select a smaller truncation size." in response.message
        )


@pytest.mark.asyncio
async def test_invocations(server: RemoteOpenAIServer, client: openai.AsyncOpenAI):
    request_args = {
        "model": MODEL_NAME,
        "input": input_text,
        "encoding_format": "float",
    }

    completion_response = await client.embeddings.create(**request_args)

    invocation_response = requests.post(
        server.url_for("invocations"), json=request_args
    )
    invocation_response.raise_for_status()

    completion_output = completion_response.model_dump()
    invocation_output = invocation_response.json()

    assert completion_output.keys() == invocation_output.keys()
    for completion_data, invocation_data in zip(
        completion_output["data"], invocation_output["data"]
    ):
        assert completion_data.keys() == invocation_data.keys()
        check_embeddings_close(
            embeddings_0_lst=[completion_data["embedding"]],
            embeddings_1_lst=[invocation_data["embedding"]],
            name_0="completion",
            name_1="invocation",
        )


@pytest.mark.asyncio
async def test_invocations_conversation(server: RemoteOpenAIServer):
    messages = [
        {
            "role": "user",
            "content": "The cat sat on the mat.",
        },
        {
            "role": "assistant",
            "content": "A feline was resting on a rug.",
        },
        {
            "role": "user",
            "content": "Stars twinkle brightly in the night sky.",
        },
    ]

    request_args = {
        "model": MODEL_NAME,
        "messages": messages,
        "encoding_format": "float",
    }

    chat_response = requests.post(server.url_for("v1/embeddings"), json=request_args)
    chat_response.raise_for_status()

    invocation_response = requests.post(
        server.url_for("invocations"), json=request_args
    )
    invocation_response.raise_for_status()

    chat_output = chat_response.json()
    invocation_output = invocation_response.json()

    assert chat_output.keys() == invocation_output.keys()
    for chat_data, invocation_data in zip(
        chat_output["data"], invocation_output["data"]
    ):
        assert chat_data.keys() == invocation_data.keys()
        check_embeddings_close(
            embeddings_0_lst=[chat_data["embedding"]],
            embeddings_1_lst=[invocation_data["embedding"]],
            name_0="chat",
            name_1="invocation",
        )


@pytest.mark.asyncio
@pytest.mark.parametrize("model_name", [MODEL_NAME])
async def test_base64_embedding(hf_model, client: openai.AsyncOpenAI, model_name: str):
    input_texts = [
        "Hello my name is",
        "The best thing about vLLM is that it supports many different models",
    ]

    responses_float = await client.embeddings.create(
        input=input_texts, model=model_name, encoding_format="float"
    )
    float_data = [d.embedding for d in responses_float.data]
    run_embedding_correctness_test(hf_model, input_texts, float_data)

    responses_base64 = await client.embeddings.create(
        input=input_texts, model=model_name, encoding_format="base64"
    )
    base64_data = []
    for data in responses_base64.data:
        base64_data.append(
            np.frombuffer(base64.b64decode(data.embedding), dtype="float32").tolist()
        )

    run_embedding_correctness_test(hf_model, input_texts, base64_data)

    # Default response is float32 decoded from base64 by OpenAI Client
    responses_default = await client.embeddings.create(
        input=input_texts, model=model_name
    )
    default_data = [d.embedding for d in responses_default.data]
    run_embedding_correctness_test(hf_model, input_texts, default_data)


@pytest.mark.asyncio
@pytest.mark.parametrize("model_name", [MODEL_NAME])
async def test_base64_embed_dtype_and_endianness(
    server: RemoteOpenAIServer, client: openai.AsyncOpenAI, model_name: str
):
    input_texts = [input_text] * 3
    responses_float = await client.embeddings.create(
        input=input_texts, model=model_name, encoding_format="float"
    )
    float_data = [d.embedding for d in responses_float.data]

    for embed_dtype in EMBED_DTYPE_TO_TORCH_DTYPE:
        for endianness in ENDIANNESS:
            responses_base64 = requests.post(
                server.url_for("/v1/embeddings"),
                json={
                    "model": model_name,
                    "input": input_texts,
                    "encoding_format": "base64",
                    "embed_dtype": embed_dtype,
                    "endianness": endianness,
                },
            )

            base64_data = []
            for data in responses_base64.json()["data"]:
                binary = base64.b64decode(data["embedding"])
                tensor = binary2tensor(binary, (-1,), embed_dtype, endianness)
                base64_data.append(tensor.to(torch.float32).tolist())

            check_embeddings_close(
                embeddings_0_lst=float_data,
                embeddings_1_lst=base64_data,
                name_0="float_data",
                name_1="base64_data",
                tol=1e-2,
            )


@pytest.mark.asyncio
@pytest.mark.parametrize("model_name", [MODEL_NAME])
async def test_bytes_embed_dtype_and_endianness(
    server: RemoteOpenAIServer, client: openai.AsyncOpenAI, model_name: str
):
    input_texts = [input_text] * 3
    responses_float = await client.embeddings.create(
        input=input_texts, model=model_name, encoding_format="float"
    )
    float_data = [d.embedding for d in responses_float.data]

    for embed_dtype in list(EMBED_DTYPE_TO_TORCH_DTYPE.keys()):
        for endianness in ENDIANNESS:
            responses_bytes = requests.post(
                server.url_for("/v1/embeddings"),
                json={
                    "model": model_name,
                    "input": input_texts,
                    "encoding_format": "bytes",
                    "embed_dtype": embed_dtype,
                    "endianness": endianness,
                },
            )

            metadata = json.loads(responses_bytes.headers["metadata"])
            body = responses_bytes.content
            items = [MetadataItem(**x) for x in metadata["data"]]

            bytes_data = decode_pooling_output(items=items, body=body)
            bytes_data = [x.to(torch.float32).tolist() for x in bytes_data]

            check_embeddings_close(
                embeddings_0_lst=float_data,
                embeddings_1_lst=bytes_data,
                name_0="float_data",
                name_1="bytes_data",
                tol=1e-2,
            )


@pytest.mark.asyncio
@pytest.mark.parametrize("model_name", [MODEL_NAME])
async def test_bytes_only_embed_dtype_and_endianness(
    server: RemoteOpenAIServer, client: openai.AsyncOpenAI, model_name: str
):
    input_texts = [
        "The best thing about vLLM is that it supports many different models",
    ] * 2

    responses_float = await client.embeddings.create(
        input=input_texts, model=model_name, encoding_format="float"
    )
    float_data = [d.embedding for d in responses_float.data]
    embedding_size = len(float_data[0])

    for embed_dtype in list(EMBED_DTYPE_TO_TORCH_DTYPE.keys()):
        for endianness in ENDIANNESS:
            responses_bytes = requests.post(
                server.url_for("/v1/embeddings"),
                json={
                    "model": model_name,
                    "input": input_texts,
                    "encoding_format": "bytes_only",
                    "embed_dtype": embed_dtype,
                    "endianness": endianness,
                },
            )

            assert "metadata" not in responses_bytes.headers
            body = responses_bytes.content
            items = build_metadata_items(
                embed_dtype=embed_dtype,
                endianness=endianness,
                shape=(embedding_size,),
                n_request=len(input_texts),
            )

            bytes_data = decode_pooling_output(items=items, body=body)
            bytes_data = [x.to(torch.float32).tolist() for x in bytes_data]

            check_embeddings_close(
                embeddings_0_lst=float_data,
                embeddings_1_lst=bytes_data,
                name_0="float_data",
                name_1="bytes_data",
                tol=1e-2,
            )


@pytest.mark.asyncio
@pytest.mark.parametrize("model_name", [MODEL_NAME])
@pytest.mark.parametrize("param_name", ["encoding_format", "embed_dtype", "endianness"])
async def test_params_not_supported(
    server: RemoteOpenAIServer, model_name: str, param_name: str
):
    responses_base64 = requests.post(
        server.url_for("/v1/embeddings"),
        json={
            "model": model_name,
            "input": input_text,
            "encoding_format": "base64",
            param_name: f"bad_{param_name}",
        },
    )

    assert responses_base64.status_code == 400
    assert "literal_error" in responses_base64.json()["error"]["message"]
    assert f"bad_{param_name}" in responses_base64.json()["error"]["message"]


@pytest.mark.asyncio
@pytest.mark.parametrize("model_name", [MODEL_NAME])
async def test_normalize(server: RemoteOpenAIServer, model_name: str):
    async def get_outputs(normalize):
        request_args = {
            "model": MODEL_NAME,
            "input": input_text,
            "encoding_format": "float",
            "normalize": normalize,
        }

        response = requests.post(server.url_for("v1/embeddings"), json=request_args)
        outputs = response.json()

        return torch.tensor([x["embedding"] for x in outputs["data"]])

    default = await get_outputs(normalize=None)
    w_normal = await get_outputs(normalize=True)
    wo_normal = await get_outputs(normalize=False)

    assert torch.allclose(default, w_normal, atol=1e-2), "Default should use normal."
    assert not torch.allclose(w_normal, wo_normal, atol=1e-2), (
        "wo_normal should not use normal."
    )
    assert torch.allclose(w_normal, F.normalize(wo_normal, p=2, dim=-1), atol=1e-2), (
        "w_normal should be close to normal(wo_normal)."
    )


@pytest.mark.asyncio
@pytest.mark.parametrize("model_name", [MODEL_NAME])
async def test_pooling_embed(server: RemoteOpenAIServer, model_name: str):
    task = "embed"
    response = requests.post(
        server.url_for("pooling"),
        json={
            "model": model_name,
            "input": input_text,
            "encoding_format": "float",
            "task": task,
        },
    )

    poolings = PoolingResponse.model_validate(response.json())

    assert len(poolings.data) == 1
    assert len(poolings.data[0].data) == 384


@pytest.mark.asyncio
@pytest.mark.parametrize("model_name", [MODEL_NAME])
async def test_pooling_token_embed(server: RemoteOpenAIServer, model_name: str):
    task = "token_embed"
    response = requests.post(
        server.url_for("pooling"),
        json={
            "model": model_name,
            "input": input_text,
            "encoding_format": "float",
            "task": task,
        },
    )

    poolings = PoolingResponse.model_validate(response.json())

    assert len(poolings.data) == 1
    assert len(poolings.data[0].data) == len(input_tokens)
    assert len(poolings.data[0].data[0]) == 384


@pytest.mark.asyncio
@pytest.mark.parametrize("model_name", [MODEL_NAME])
@pytest.mark.parametrize("task", ["classify", "token_classify", "plugin"])
async def test_pooling_not_supported(
    server: RemoteOpenAIServer, model_name: str, task: str
):
    response = requests.post(
        server.url_for("pooling"),
        json={
            "model": model_name,
            "input": "test",
            "encoding_format": "float",
            "task": task,
        },
    )
    assert response.json()["error"]["type"] == "BadRequestError"
    assert response.json()["error"]["message"].startswith(
        f"Task {task} is not supported"
    )