vllm/tests/v1/tracing/test_tracing.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# ruff: noqa
# type: ignore
import pytest
import time
from opentelemetry.sdk.environment_variables import OTEL_EXPORTER_OTLP_TRACES_INSECURE

from vllm import LLM, SamplingParams
from vllm.tracing import SpanAttributes

# Import shared fixtures from the tracing conftest
from tests.tracing.conftest import (  # noqa: F401
    FAKE_TRACE_SERVER_ADDRESS,
    FakeTraceService,
    trace_service,
)


def test_traces(
    monkeypatch: pytest.MonkeyPatch,
    trace_service: FakeTraceService,
):
    with monkeypatch.context() as m:
        m.setenv(OTEL_EXPORTER_OTLP_TRACES_INSECURE, "true")

        sampling_params = SamplingParams(
            temperature=0.01,
            top_p=0.1,
            max_tokens=256,
        )
        model = "facebook/opt-125m"
        llm = LLM(
            model=model,
            otlp_traces_endpoint=FAKE_TRACE_SERVER_ADDRESS,
            gpu_memory_utilization=0.3,
            disable_log_stats=False,
        )
        prompts = ["This is a short prompt"]
        outputs = llm.generate(prompts, sampling_params=sampling_params)
        print(f"test_traces outputs is : {outputs}")

        # Wait for the "llm_request" span to be exported.
        # The BatchSpanProcessor batches spans and exports them periodically,
        # so we need to wait specifically for the llm_request span to appear.
        timeout = 15
        deadline = time.time() + timeout
        llm_request_spans = []
        while time.time() < deadline:
            all_spans = trace_service.get_all_spans()
            llm_request_spans = [s for s in all_spans if s["name"] == "llm_request"]
            if llm_request_spans:
                break
            time.sleep(0.5)

        assert len(llm_request_spans) == 1, (
            f"Expected exactly 1 'llm_request' span, but got {len(llm_request_spans)}. "
            f"All span names: {[s['name'] for s in all_spans]}"
        )

        attributes = llm_request_spans[0]["attributes"]
        # assert attributes.get(SpanAttributes.GEN_AI_RESPONSE_MODEL) == model
        assert attributes.get(SpanAttributes.GEN_AI_REQUEST_ID) == outputs[0].request_id
        assert (
            attributes.get(SpanAttributes.GEN_AI_REQUEST_TEMPERATURE)
            == sampling_params.temperature
        )
        assert (
            attributes.get(SpanAttributes.GEN_AI_REQUEST_TOP_P) == sampling_params.top_p
        )
        assert (
            attributes.get(SpanAttributes.GEN_AI_REQUEST_MAX_TOKENS)
            == sampling_params.max_tokens
        )
        assert attributes.get(SpanAttributes.GEN_AI_REQUEST_N) == sampling_params.n
        assert attributes.get(SpanAttributes.GEN_AI_USAGE_PROMPT_TOKENS) == len(
            outputs[0].prompt_token_ids
        )
        completion_tokens = sum(len(o.token_ids) for o in outputs[0].outputs)
        assert (
            attributes.get(SpanAttributes.GEN_AI_USAGE_COMPLETION_TOKENS)
            == completion_tokens
        )

        assert attributes.get(SpanAttributes.GEN_AI_LATENCY_TIME_IN_QUEUE) > 0
        assert attributes.get(SpanAttributes.GEN_AI_LATENCY_TIME_TO_FIRST_TOKEN) > 0
        assert attributes.get(SpanAttributes.GEN_AI_LATENCY_E2E) > 0