[Misc] Add OpenTelemetry support (#4687)

This PR adds basic support for OpenTelemetry distributed tracing.
It includes changes to enable tracing functionality and improve monitoring capabilities.

I've also added a markdown with print-screens to guide users how to use this feature. You can find it here

.buildkite/test-pipeline.yaml

@@ -159,6 +159,15 @@ steps:
   #mirror_hardwares: [amd]
   command: pytest -v -s quantization
 
+- label: Tracing Test
+  commands: 
+    - "pip install \
+        opentelemetry-sdk \
+        opentelemetry-api \
+        opentelemetry-exporter-otlp \
+        opentelemetry-semantic-conventions-ai"
+    - pytest -v -s tracing
+
 - label: Benchmarks
   working_dir: "/vllm-workspace/.buildkite"
   mirror_hardwares: [amd]

benchmarks/benchmark_latency.py

@@ -20,26 +20,29 @@ def main(args: argparse.Namespace):
 
     # NOTE(woosuk): If the request cannot be processed in a single batch,
     # the engine will automatically process the request in multiple batches.
-    llm = LLM(model=args.model,
-              speculative_model=args.speculative_model,
-              num_speculative_tokens=args.num_speculative_tokens,
-              tokenizer=args.tokenizer,
-              quantization=args.quantization,
-              tensor_parallel_size=args.tensor_parallel_size,
-              trust_remote_code=args.trust_remote_code,
-              dtype=args.dtype,
-              enforce_eager=args.enforce_eager,
-              kv_cache_dtype=args.kv_cache_dtype,
-              quantization_param_path=args.quantization_param_path,
-              device=args.device,
-              ray_workers_use_nsight=args.ray_workers_use_nsight,
-              use_v2_block_manager=args.use_v2_block_manager,
-              enable_chunked_prefill=args.enable_chunked_prefill,
-              download_dir=args.download_dir,
-              block_size=args.block_size,
-              gpu_memory_utilization=args.gpu_memory_utilization,
-              load_format=args.load_format,
-              distributed_executor_backend=args.distributed_executor_backend)
+    llm = LLM(
+        model=args.model,
+        speculative_model=args.speculative_model,
+        num_speculative_tokens=args.num_speculative_tokens,
+        tokenizer=args.tokenizer,
+        quantization=args.quantization,
+        tensor_parallel_size=args.tensor_parallel_size,
+        trust_remote_code=args.trust_remote_code,
+        dtype=args.dtype,
+        enforce_eager=args.enforce_eager,
+        kv_cache_dtype=args.kv_cache_dtype,
+        quantization_param_path=args.quantization_param_path,
+        device=args.device,
+        ray_workers_use_nsight=args.ray_workers_use_nsight,
+        use_v2_block_manager=args.use_v2_block_manager,
+        enable_chunked_prefill=args.enable_chunked_prefill,
+        download_dir=args.download_dir,
+        block_size=args.block_size,
+        gpu_memory_utilization=args.gpu_memory_utilization,
+        load_format=args.load_format,
+        distributed_executor_backend=args.distributed_executor_backend,
+        otlp_traces_endpoint=args.otlp_traces_endpoint,
+    )
 
     sampling_params = SamplingParams(
         n=args.n,
@@ -254,5 +257,10 @@ if __name__ == '__main__':
         help='Backend to use for distributed serving. When more than 1 GPU '
         'is used, will be automatically set to "ray" if installed '
         'or "mp" (multiprocessing) otherwise.')
+    parser.add_argument(
+        '--otlp-traces-endpoint',
+        type=str,
+        default=None,
+        help='Target URL to which OpenTelemetry traces will be sent.')
     args = parser.parse_args()
     main(args)

examples/production_monitoring/Otel.md

@@ -0,0 +1,82 @@
+# Setup OpenTelemetry POC
+
+1. Install OpenTelemetry packages:
+    ```
+    pip install \
+        opentelemetry-sdk \
+        opentelemetry-api \
+        opentelemetry-exporter-otlp \
+        opentelemetry-semantic-conventions-ai
+    ```
+
+1. Start Jaeger in a docker container:
+    ```
+    # From: https://www.jaegertracing.io/docs/1.57/getting-started/
+    docker run --rm --name jaeger \
+        -e COLLECTOR_ZIPKIN_HOST_PORT=:9411 \
+        -p 6831:6831/udp \
+        -p 6832:6832/udp \
+        -p 5778:5778 \
+        -p 16686:16686 \
+        -p 4317:4317 \
+        -p 4318:4318 \
+        -p 14250:14250 \
+        -p 14268:14268 \
+        -p 14269:14269 \
+        -p 9411:9411 \
+        jaegertracing/all-in-one:1.57
+    ```
+
+1. In a new shell, export Jaeger IP:
+    ```
+    export JAEGER_IP=$(docker inspect   --format '{{ .NetworkSettings.IPAddress }}' jaeger)
+    export OTEL_EXPORTER_OTLP_TRACES_ENDPOINT=grpc://$JAEGER_IP:4317
+    ```
+    Then set vLLM's service name for OpenTelemetry, enable insecure connections to Jaeger and run vLLM:
+    ```
+    export OTEL_SERVICE_NAME="vllm-server"
+    export OTEL_EXPORTER_OTLP_TRACES_INSECURE=true
+    python -m vllm.entrypoints.openai.api_server --model="facebook/opt-125m" --otlp-traces-endpoint="$OTEL_EXPORTER_OTLP_TRACES_ENDPOINT"
+    ```
+
+1. In a new shell, send requests with trace context from a dummy client
+    ```
+    export JAEGER_IP=$(docker inspect --format '{{ .NetworkSettings.IPAddress }}' jaeger)
+    export OTEL_EXPORTER_OTLP_TRACES_ENDPOINT=grpc://$JAEGER_IP:4317
+    export OTEL_EXPORTER_OTLP_TRACES_INSECURE=true
+    export OTEL_SERVICE_NAME="client-service"
+    python dummy_client.py
+    ```
+
+1. Open Jaeger webui: http://localhost:16686/
+
+    In the search pane, select `vllm-server` service and hit `Find Traces`. You should get a list of traces, one for each request.
+    ![Traces](https://i.imgur.com/GYHhFjo.png)
+
+1. Clicking on a trace will show its spans and their tags. In this demo, each trace has 2 spans. One from the dummy client containing the prompt text and one from vLLM containing metadata about the request.
+![Spans details](https://i.imgur.com/OPf6CBL.png)
+
+## Exporter Protocol
+OpenTelemetry supports either `grpc` or `http/protobuf` as the transport protocol for trace data in the exporter.
+By default, `grpc` is used. To set `http/protobuf` as the protocol, configure the `OTEL_EXPORTER_OTLP_TRACES_PROTOCOL` environment variable as follows:
+```
+export OTEL_EXPORTER_OTLP_TRACES_PROTOCOL=http/protobuf
+export OTEL_EXPORTER_OTLP_TRACES_ENDPOINT=http://$JAEGER_IP:4318/v1/traces
+python -m vllm.entrypoints.openai.api_server --model="facebook/opt-125m" --otlp-traces-endpoint="$OTEL_EXPORTER_OTLP_TRACES_ENDPOINT"
+```
+
+## Instrumentation of FastAPI
+OpenTelemetry allows automatic instrumentation of FastAPI.
+1. Install the instrumentation library
+    ```
+    pip install opentelemetry-instrumentation-fastapi
+    ```
+
+1. Run vLLM with `opentelemetry-instrument`
+    ```
+    opentelemetry-instrument python -m vllm.entrypoints.openai.api_server --model="facebook/opt-125m" 
+    ```
+
+1. Send a request to vLLM and find its trace in Jaeger. It should contain spans from FastAPI.
+
+![FastAPI Spans](https://i.imgur.com/hywvoOJ.png)

examples/production_monitoring/dummy_client.py

@@ -0,0 +1,35 @@
+import requests
+from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import (
+    OTLPSpanExporter)
+from opentelemetry.sdk.trace import TracerProvider
+from opentelemetry.sdk.trace.export import (BatchSpanProcessor,
+                                            ConsoleSpanExporter)
+from opentelemetry.trace import SpanKind, set_tracer_provider
+from opentelemetry.trace.propagation.tracecontext import (
+    TraceContextTextMapPropagator)
+
+trace_provider = TracerProvider()
+set_tracer_provider(trace_provider)
+
+trace_provider.add_span_processor(BatchSpanProcessor(OTLPSpanExporter()))
+trace_provider.add_span_processor(BatchSpanProcessor(ConsoleSpanExporter()))
+
+tracer = trace_provider.get_tracer("dummy-client")
+
+url = "http://localhost:8000/v1/completions"
+with tracer.start_as_current_span("client-span", kind=SpanKind.CLIENT) as span:
+    prompt = "San Francisco is a"
+    span.set_attribute("prompt", prompt)
+    headers = {}
+    TraceContextTextMapPropagator().inject(headers)
+    payload = {
+        "model": "facebook/opt-125m",
+        "prompt": prompt,
+        "max_tokens": 10,
+        "best_of": 20,
+        "n": 3,
+        "use_beam_search": "true",
+        "temperature": 0.0,
+        # "stream": True,
+    }
+    response = requests.post(url, headers=headers, json=payload)

tests/tracing/test_tracing.py

@@ -0,0 +1,116 @@
+import os
+import threading
+from concurrent import futures
+from typing import Callable, Dict, Iterable, Literal
+
+import grpc
+import pytest
+from opentelemetry.proto.collector.trace.v1.trace_service_pb2 import (
+    ExportTraceServiceResponse)
+from opentelemetry.proto.collector.trace.v1.trace_service_pb2_grpc import (
+    TraceServiceServicer, add_TraceServiceServicer_to_server)
+from opentelemetry.proto.common.v1.common_pb2 import AnyValue, KeyValue
+from opentelemetry.sdk.environment_variables import (
+    OTEL_EXPORTER_OTLP_TRACES_INSECURE)
+
+from vllm import LLM, SamplingParams
+from vllm.tracing import SpanAttributes
+
+FAKE_TRACE_SERVER_ADDRESS = "localhost:4317"
+
+FieldName = Literal['bool_value', 'string_value', 'int_value', 'double_value',
+                    'array_value']
+
+
+def decode_value(value: AnyValue):
+    field_decoders: Dict[FieldName, Callable] = {
+        "bool_value": (lambda v: v.bool_value),
+        "string_value": (lambda v: v.string_value),
+        "int_value": (lambda v: v.int_value),
+        "double_value": (lambda v: v.double_value),
+        "array_value":
+        (lambda v: [decode_value(item) for item in v.array_value.values]),
+    }
+    for field, decoder in field_decoders.items():
+        if value.HasField(field):
+            return decoder(value)
+    raise ValueError(f"Couldn't decode value: {value}")
+
+
+def decode_attributes(attributes: Iterable[KeyValue]):
+    return {kv.key: decode_value(kv.value) for kv in attributes}
+
+
+class FakeTraceService(TraceServiceServicer):
+
+    def __init__(self):
+        self.request = None
+        self.evt = threading.Event()
+
+    def Export(self, request, context):
+        self.request = request
+        self.evt.set()
+        return ExportTraceServiceResponse()
+
+
+@pytest.fixture
+def trace_service():
+    """Fixture to set up a fake gRPC trace service"""
+    server = grpc.server(futures.ThreadPoolExecutor(max_workers=1))
+    service = FakeTraceService()
+    add_TraceServiceServicer_to_server(service, server)
+    server.add_insecure_port(FAKE_TRACE_SERVER_ADDRESS)
+    server.start()
+
+    yield service
+
+    server.stop(None)
+
+
+def test_traces(trace_service):
+    os.environ[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,
+    )
+    prompts = ["This is a short prompt"]
+    outputs = llm.generate(prompts, sampling_params=sampling_params)
+
+    timeout = 5
+    if not trace_service.evt.wait(timeout):
+        raise TimeoutError(
+            f"The fake trace service didn't receive a trace within "
+            f"the {timeout} seconds timeout")
+
+    attributes = decode_attributes(trace_service.request.resource_spans[0].
+                                   scope_spans[0].spans[0].attributes)
+    assert attributes.get(SpanAttributes.LLM_RESPONSE_MODEL) == model
+    assert attributes.get(
+        SpanAttributes.LLM_REQUEST_ID) == outputs[0].request_id
+    assert attributes.get(
+        SpanAttributes.LLM_REQUEST_TEMPERATURE) == sampling_params.temperature
+    assert attributes.get(
+        SpanAttributes.LLM_REQUEST_TOP_P) == sampling_params.top_p
+    assert attributes.get(
+        SpanAttributes.LLM_REQUEST_MAX_TOKENS) == sampling_params.max_tokens
+    assert attributes.get(
+        SpanAttributes.LLM_REQUEST_BEST_OF) == sampling_params.best_of
+    assert attributes.get(SpanAttributes.LLM_REQUEST_N) == sampling_params.n
+    assert attributes.get(SpanAttributes.LLM_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.LLM_USAGE_COMPLETION_TOKENS) == completion_tokens
+    metrics = outputs[0].metrics
+    assert attributes.get(
+        SpanAttributes.LLM_LATENCY_TIME_IN_QUEUE) == metrics.time_in_queue
+    ttft = metrics.first_token_time - metrics.arrival_time
+    assert attributes.get(
+        SpanAttributes.LLM_LATENCY_TIME_TO_FIRST_TOKEN) == ttft
+    e2e_time = metrics.finished_time - metrics.arrival_time
+    assert attributes.get(SpanAttributes.LLM_LATENCY_E2E) == e2e_time

vllm/config.py

@@ -10,6 +10,7 @@ from transformers import PretrainedConfig, PreTrainedTokenizerBase
 from vllm.logger import init_logger
 from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
 from vllm.model_executor.models import ModelRegistry
+from vllm.tracing import is_otel_installed
 from vllm.transformers_utils.config import get_config, get_hf_text_config
 from vllm.utils import (cuda_device_count_stateless, get_cpu_memory, is_cpu,
                         is_hip, is_neuron, is_tpu, is_xpu)
@@ -1371,6 +1372,17 @@ class DecodingConfig:
                              f"must be one of {valid_guided_backends}")
 
 
+@dataclass
+class ObservabilityConfig:
+    """Configuration for observability."""
+    otlp_traces_endpoint: Optional[str] = None
+
+    def __post_init__(self):
+        if not is_otel_installed() and self.otlp_traces_endpoint is not None:
+            raise ValueError("OpenTelemetry packages must be installed before "
+                             "configuring 'otlp_traces_endpoint'")
+
+
 @dataclass(frozen=True)
 class EngineConfig:
     """Dataclass which contains all engine-related configuration. This
@@ -1387,6 +1399,7 @@ class EngineConfig:
     vision_language_config: Optional[VisionLanguageConfig]
     speculative_config: Optional[SpeculativeConfig]
     decoding_config: Optional[DecodingConfig]
+    observability_config: Optional[ObservabilityConfig]
 
     def __post_init__(self):
         """Verify configs are valid & consistent with each other.

vllm/engine/arg_utils.py

@@ -7,8 +7,9 @@ from typing import List, Optional, Tuple, Union
 
 from vllm.config import (CacheConfig, DecodingConfig, DeviceConfig,
                          EngineConfig, LoadConfig, LoRAConfig, ModelConfig,
-                         ParallelConfig, SchedulerConfig, SpeculativeConfig,
-                         TokenizerPoolConfig, VisionLanguageConfig)
+                         ObservabilityConfig, ParallelConfig, SchedulerConfig,
+                         SpeculativeConfig, TokenizerPoolConfig,
+                         VisionLanguageConfig)
 from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
 from vllm.utils import str_to_int_tuple
 
@@ -101,6 +102,8 @@ class EngineArgs:
 
     qlora_adapter_name_or_path: Optional[str] = None
 
+    otlp_traces_endpoint: Optional[str] = None
+
     def __post_init__(self):
         if self.tokenizer is None:
             self.tokenizer = self.model
@@ -599,6 +602,13 @@ class EngineArgs:
                             type=str,
                             default=None,
                             help='Name or path of the QLoRA adapter.')
+
+        parser.add_argument(
+            '--otlp-traces-endpoint',
+            type=str,
+            default=None,
+            help='Target URL to which OpenTelemetry traces will be sent.')
+
         return parser
 
     @classmethod
@@ -757,6 +767,9 @@ class EngineArgs:
         decoding_config = DecodingConfig(
             guided_decoding_backend=self.guided_decoding_backend)
 
+        observability_config = ObservabilityConfig(
+            otlp_traces_endpoint=self.otlp_traces_endpoint)
+
         if (model_config.get_sliding_window() is not None
                 and scheduler_config.chunked_prefill_enabled
                 and not scheduler_config.use_v2_block_manager):
@@ -764,16 +777,19 @@ class EngineArgs:
                 "Chunked prefill is not supported with sliding window. "
                 "Set --disable-sliding-window to disable sliding window.")
 
-        return EngineConfig(model_config=model_config,
-                            cache_config=cache_config,
-                            parallel_config=parallel_config,
-                            scheduler_config=scheduler_config,
-                            device_config=device_config,
-                            lora_config=lora_config,
-                            vision_language_config=vision_language_config,
-                            speculative_config=speculative_config,
-                            load_config=load_config,
-                            decoding_config=decoding_config)
+        return EngineConfig(
+            model_config=model_config,
+            cache_config=cache_config,
+            parallel_config=parallel_config,
+            scheduler_config=scheduler_config,
+            device_config=device_config,
+            lora_config=lora_config,
+            vision_language_config=vision_language_config,
+            speculative_config=speculative_config,
+            load_config=load_config,
+            decoding_config=decoding_config,
+            observability_config=observability_config,
+        )
 
 
 @dataclass

vllm/engine/async_llm_engine.py

@@ -244,6 +244,9 @@ class _AsyncLLMEngine(LLMEngine):
         # Log stats.
         self.do_log_stats(scheduler_outputs, output)
 
+        # Tracing
+        self.do_tracing(scheduler_outputs)
+
         if not request_outputs:
             # Stop the execute model loop in parallel workers until there are
             # more requests to process. This avoids waiting indefinitely in
@@ -285,6 +288,7 @@ class _AsyncLLMEngine(LLMEngine):
         params: Union[SamplingParams, PoolingParams],
         arrival_time: Optional[float] = None,
         lora_request: Optional[LoRARequest] = None,
+        trace_headers: Optional[Dict[str, str]] = None,
     ) -> None:
         if lora_request is not None and not self.lora_config:
             raise ValueError(f"Got lora_request {lora_request} but LoRA is "
@@ -301,6 +305,7 @@ class _AsyncLLMEngine(LLMEngine):
             params=params,
             arrival_time=arrival_time,
             lora_request=lora_request,
+            trace_headers=trace_headers,
         )
 
     async def check_health_async(self) -> None:
@@ -556,6 +561,7 @@ class AsyncLLMEngine:
         params: Union[SamplingParams, PoolingParams],
         arrival_time: Optional[float] = None,
         lora_request: Optional[LoRARequest] = None,
+        trace_headers: Optional[Dict[str, str]] = None,
     ) -> AsyncStream:
         if self.log_requests:
             if isinstance(inputs, str):
@@ -597,6 +603,7 @@ class AsyncLLMEngine:
             params=params,
             arrival_time=arrival_time,
             lora_request=lora_request,
+            trace_headers=trace_headers,
         )
 
         return stream
@@ -607,6 +614,7 @@ class AsyncLLMEngine:
         sampling_params: SamplingParams,
         request_id: str,
         lora_request: Optional[LoRARequest] = None,
+        trace_headers: Optional[Dict[str, str]] = None,
     ) -> AsyncIterator[RequestOutput]:
         """Generate outputs for a request.
 
@@ -621,6 +629,7 @@ class AsyncLLMEngine:
             sampling_params: The sampling parameters of the request.
             request_id: The unique id of the request.
             lora_request: LoRA request to use for generation, if any.
+            trace_headers: OpenTelemetry trace headers.
 
         Yields:
             The output `RequestOutput` objects from the LLMEngine
@@ -674,6 +683,7 @@ class AsyncLLMEngine:
                 inputs,
                 sampling_params,
                 lora_request=lora_request,
+                trace_headers=trace_headers,
         ):
             yield LLMEngine.validate_output(output, RequestOutput)
 
@@ -683,6 +693,7 @@ class AsyncLLMEngine:
         pooling_params: PoolingParams,
         request_id: str,
         lora_request: Optional[LoRARequest] = None,
+        trace_headers: Optional[Dict[str, str]] = None,
     ) -> AsyncIterator[EmbeddingRequestOutput]:
         """Generate outputs for a request from an embedding model.
 
@@ -697,6 +708,7 @@ class AsyncLLMEngine:
             pooling_params: The pooling parameters of the request.
             request_id: The unique id of the request.
             lora_request: LoRA request to use for generation, if any.
+            trace_headers: OpenTelemetry trace headers.
 
         Yields:
             The output `EmbeddingRequestOutput` objects from the LLMEngine
@@ -748,6 +760,7 @@ class AsyncLLMEngine:
                 inputs,
                 pooling_params,
                 lora_request=lora_request,
+                trace_headers=trace_headers,
         ):
             yield LLMEngine.validate_output(output, EmbeddingRequestOutput)
 
@@ -758,6 +771,7 @@ class AsyncLLMEngine:
         params: Union[SamplingParams, PoolingParams],
         *,
         lora_request: Optional[LoRARequest] = None,
+        trace_headers: Optional[Dict[str, str]] = None,
     ) -> AsyncIterator[Union[RequestOutput, EmbeddingRequestOutput]]:
         """Common logic to process requests with SamplingParams or
         PoolingParams."""
@@ -769,6 +783,7 @@ class AsyncLLMEngine:
             params,
             arrival_time=arrival_time,
             lora_request=lora_request,
+            trace_headers=trace_headers,
         )
 
         try:
@@ -848,3 +863,10 @@ class AsyncLLMEngine:
         else:
             await self.engine.check_health_async()
         logger.debug("Health check took %fs", time.perf_counter() - t)
+
+    async def is_tracing_enabled(self) -> bool:
+        if self.engine_use_ray:
+            return await self.engine.is_tracing_enabled.remote(  # type: ignore
+            )
+        else:
+            return self.engine.is_tracing_enabled()

vllm/engine/llm_engine.py

@@ -1,14 +1,14 @@
 import time
 from contextlib import contextmanager
-from typing import TYPE_CHECKING, ClassVar, Iterable, List, Optional
+from typing import TYPE_CHECKING, ClassVar, Dict, Iterable, List, Optional
 from typing import Sequence as GenericSequence
 from typing import Set, Type, TypeVar, Union
 
 from transformers import GenerationConfig, PreTrainedTokenizer
 
 from vllm.config import (CacheConfig, DecodingConfig, DeviceConfig, LoadConfig,
-                         LoRAConfig, ModelConfig, ParallelConfig,
-                         SchedulerConfig, SpeculativeConfig,
+                         LoRAConfig, ModelConfig, ObservabilityConfig,
+                         ParallelConfig, SchedulerConfig, SpeculativeConfig,
                          VisionLanguageConfig)
 from vllm.core.scheduler import (ScheduledSequenceGroup, Scheduler,
                                  SchedulerOutputs)
@@ -31,6 +31,8 @@ from vllm.sequence import (EmbeddingSequenceGroupOutput, ExecuteModelRequest,
                            PoolerOutput, SamplerOutput, Sequence,
                            SequenceGroup, SequenceGroupMetadata,
                            SequenceStatus)
+from vllm.tracing import (SpanAttributes, SpanKind, extract_trace_context,
+                          init_tracer)
 from vllm.transformers_utils.detokenizer import Detokenizer
 from vllm.transformers_utils.tokenizer_group import (BaseTokenizerGroup,
                                                      get_tokenizer_group)
@@ -154,6 +156,7 @@ class LLMEngine:
         vision_language_config: Optional[VisionLanguageConfig],
         speculative_config: Optional[SpeculativeConfig],
         decoding_config: Optional[DecodingConfig],
+        observability_config: Optional[ObservabilityConfig],
         executor_class: Type[ExecutorBase],
         log_stats: bool,
         usage_context: UsageContext = UsageContext.ENGINE_CONTEXT,
@@ -168,7 +171,8 @@ class LLMEngine:
             "disable_custom_all_reduce=%s, quantization=%s, "
             "enforce_eager=%s, kv_cache_dtype=%s, "
             "quantization_param_path=%s, device_config=%s, "
-            "decoding_config=%r, seed=%d, served_model_name=%s)",
+            "decoding_config=%r, observability_config=%r, "
+            "seed=%d, served_model_name=%s)",
             VLLM_VERSION,
             model_config.model,
             speculative_config,
@@ -192,6 +196,7 @@ class LLMEngine:
             model_config.quantization_param_path,
             device_config.device,
             decoding_config,
+            observability_config,
             model_config.seed,
             model_config.served_model_name,
         )
@@ -207,6 +212,8 @@ class LLMEngine:
         self.speculative_config = speculative_config
         self.load_config = load_config
         self.decoding_config = decoding_config or DecodingConfig()
+        self.observability_config = observability_config or ObservabilityConfig(
+        )
         self.log_stats = log_stats
 
         if not self.model_config.skip_tokenizer_init:
@@ -288,6 +295,12 @@ class LLMEngine:
                 max_model_len=self.model_config.max_model_len)
             self.stat_logger.info("cache_config", self.cache_config)
 
+        self.tracer = None
+        if self.observability_config.otlp_traces_endpoint:
+            self.tracer = init_tracer(
+                "vllm.llm_engine",
+                self.observability_config.otlp_traces_endpoint)
+
         # Create sequence output processor, e.g. for beam search or
         # speculative decoding.
         self.output_processor = (
@@ -444,6 +457,7 @@ class LLMEngine:
         params: Union[SamplingParams, PoolingParams],
         arrival_time: float,
         lora_request: Optional[LoRARequest],
+        trace_headers: Optional[Dict[str, str]] = None,
     ) -> None:
         # Create the sequences.
         block_size = self.cache_config.block_size
@@ -461,6 +475,7 @@ class LLMEngine:
                 params,
                 arrival_time=arrival_time,
                 lora_request=lora_request,
+                trace_headers=trace_headers,
             )
         elif isinstance(params, PoolingParams):
             seq_group = self._create_sequence_group_with_pooling(
@@ -507,6 +522,7 @@ class LLMEngine:
         params: Union[SamplingParams, PoolingParams],
         arrival_time: Optional[float] = None,
         lora_request: Optional[LoRARequest] = None,
+        trace_headers: Optional[Dict[str, str]] = None,
     ) -> None:
         """Add a request to the engine's request pool.
 
@@ -524,6 +540,7 @@ class LLMEngine:
                 :class:`~vllm.PoolingParams` for pooling.
             arrival_time: The arrival time of the request. If None, we use
                 the current monotonic time.
+            trace_headers: OpenTelemetry trace headers.
 
         Details:
             - Set arrival_time to the current time if it is None.
@@ -565,6 +582,7 @@ class LLMEngine:
             params=params,
             arrival_time=arrival_time,
             lora_request=lora_request,
+            trace_headers=trace_headers,
         )
 
     def _create_sequence_group_with_sampling(
@@ -574,6 +592,7 @@ class LLMEngine:
         sampling_params: SamplingParams,
         arrival_time: float,
         lora_request: Optional[LoRARequest],
+        trace_headers: Optional[Dict[str, str]] = None,
     ) -> SequenceGroup:
         """Creates a SequenceGroup with SamplingParams."""
         max_logprobs = self.get_model_config().max_logprobs
@@ -595,11 +614,14 @@ class LLMEngine:
             self.generation_config_fields)
 
         # Create the sequence group.
-        seq_group = SequenceGroup(request_id=request_id,
-                                  seqs=[seq],
-                                  arrival_time=arrival_time,
-                                  sampling_params=sampling_params,
-                                  lora_request=lora_request)
+        seq_group = SequenceGroup(
+            request_id=request_id,
+            seqs=[seq],
+            arrival_time=arrival_time,
+            sampling_params=sampling_params,
+            lora_request=lora_request,
+            trace_headers=trace_headers,
+        )
 
         return seq_group
 
@@ -793,6 +815,9 @@ class LLMEngine:
         # Log stats.
         self.do_log_stats(scheduler_outputs, output)
 
+        # Tracing
+        self.do_tracing(scheduler_outputs)
+
         if not request_outputs:
             # Stop the execute model loop in parallel workers until there are
             # more requests to process. This avoids waiting indefinitely in
@@ -986,3 +1011,62 @@ class LLMEngine:
 
     def check_health(self) -> None:
         self.model_executor.check_health()
+
+    def is_tracing_enabled(self) -> bool:
+        return self.tracer is not None
+
+    def do_tracing(self, scheduler_outputs: SchedulerOutputs) -> None:
+        if self.tracer is None:
+            return
+
+        for scheduled_seq_group in scheduler_outputs.scheduled_seq_groups:
+            seq_group = scheduled_seq_group.seq_group
+            if seq_group.is_finished():
+                self.create_trace_span(seq_group)
+
+    def create_trace_span(self, seq_group: SequenceGroup) -> None:
+        if self.tracer is None or seq_group.sampling_params is None:
+            return
+        arrival_time_nano_seconds = int(seq_group.metrics.arrival_time * 1e9)
+
+        trace_context = extract_trace_context(seq_group.trace_headers)
+
+        with self.tracer.start_as_current_span(
+                "llm_request",
+                kind=SpanKind.SERVER,
+                context=trace_context,
+                start_time=arrival_time_nano_seconds) as seq_span:
+            metrics = seq_group.metrics
+            ttft = metrics.first_token_time - metrics.arrival_time
+            e2e_time = metrics.finished_time - metrics.arrival_time
+            # attribute names are based on
+            # https://github.com/open-telemetry/semantic-conventions/blob/main/docs/gen-ai/llm-spans.md
+            seq_span.set_attribute(SpanAttributes.LLM_RESPONSE_MODEL,
+                                   self.model_config.model)
+            seq_span.set_attribute(SpanAttributes.LLM_REQUEST_ID,
+                                   seq_group.request_id)
+            seq_span.set_attribute(SpanAttributes.LLM_REQUEST_TEMPERATURE,
+                                   seq_group.sampling_params.temperature)
+            seq_span.set_attribute(SpanAttributes.LLM_REQUEST_TOP_P,
+                                   seq_group.sampling_params.top_p)
+            seq_span.set_attribute(SpanAttributes.LLM_REQUEST_MAX_TOKENS,
+                                   seq_group.sampling_params.max_tokens)
+            seq_span.set_attribute(SpanAttributes.LLM_REQUEST_BEST_OF,
+                                   seq_group.sampling_params.best_of)
+            seq_span.set_attribute(SpanAttributes.LLM_REQUEST_N,
+                                   seq_group.sampling_params.n)
+            seq_span.set_attribute(SpanAttributes.LLM_USAGE_NUM_SEQUENCES,
+                                   seq_group.num_seqs())
+            seq_span.set_attribute(SpanAttributes.LLM_USAGE_PROMPT_TOKENS,
+                                   len(seq_group.prompt_token_ids))
+            seq_span.set_attribute(
+                SpanAttributes.LLM_USAGE_COMPLETION_TOKENS,
+                sum([
+                    seq.get_output_len()
+                    for seq in seq_group.get_finished_seqs()
+                ]))
+            seq_span.set_attribute(SpanAttributes.LLM_LATENCY_TIME_IN_QUEUE,
+                                   metrics.time_in_queue)
+            seq_span.set_attribute(
+                SpanAttributes.LLM_LATENCY_TIME_TO_FIRST_TOKEN, ttft)
+            seq_span.set_attribute(SpanAttributes.LLM_LATENCY_E2E, e2e_time)

vllm/entrypoints/openai/serving_chat.py

@@ -31,6 +31,8 @@ from vllm.multimodal.utils import (async_get_and_parse_image,
                                    get_full_image_text_prompt)
 from vllm.outputs import RequestOutput
 from vllm.sequence import Logprob
+from vllm.tracing import (contains_trace_headers, extract_trace_headers,
+                          log_tracing_disabled_warning)
 from vllm.utils import random_uuid
 
 logger = init_logger(__name__)
@@ -267,11 +269,20 @@ class OpenAIServingChat(OpenAIServing):
         if image_data is not None:
             inputs["multi_modal_data"] = image_data
 
+        is_tracing_enabled = await self.engine.is_tracing_enabled()
+        trace_headers = None
+        if is_tracing_enabled and raw_request:
+            trace_headers = extract_trace_headers(raw_request.headers)
+        if not is_tracing_enabled and raw_request and contains_trace_headers(
+                raw_request.headers):
+            log_tracing_disabled_warning()
+
         result_generator = self.engine.generate(
             inputs,
             sampling_params,
             request_id,
             lora_request,
+            trace_headers=trace_headers,
         )
         # Streaming response
         if request.stream:

vllm/entrypoints/openai/serving_completion.py

@@ -24,6 +24,8 @@ from vllm.model_executor.guided_decoding import (
     get_guided_decoding_logits_processor)
 from vllm.outputs import RequestOutput
 from vllm.sequence import Logprob
+from vllm.tracing import (contains_trace_headers, extract_trace_headers,
+                          log_tracing_disabled_warning)
 from vllm.utils import merge_async_iterators, random_uuid
 
 logger = init_logger(__name__)
@@ -125,6 +127,14 @@ class OpenAIServingCompletion(OpenAIServing):
                         truncate_prompt_tokens)
                 prompt_ids, prompt_text = prompt_formats
 
+                is_tracing_enabled = await self.engine.is_tracing_enabled()
+                trace_headers = None
+                if is_tracing_enabled:
+                    trace_headers = extract_trace_headers(raw_request.headers)
+                if not is_tracing_enabled and contains_trace_headers(
+                        raw_request.headers):
+                    log_tracing_disabled_warning()
+
                 generator = self.engine.generate(
                     {
                         "prompt": prompt_text,
@@ -133,6 +143,7 @@ class OpenAIServingCompletion(OpenAIServing):
                     sampling_params,
                     f"{request_id}-{i}",
                     lora_request=lora_request,
+                    trace_headers=trace_headers,
                 )
 
                 generators.append(generator)

vllm/sequence.py

@@ -414,6 +414,7 @@ class SequenceGroup:
             for an embedding model.
         encoder_seq: Optional, the single encoder sequence. Should be None
                      unless you are working with an encoder/decoder model.
+        trace_headers: OpenTelemetry trace headers.
     """
 
     def __init__(
@@ -426,6 +427,7 @@ class SequenceGroup:
         embeddings: Optional[List[float]] = None,
         pooling_params: Optional[PoolingParams] = None,
         encoder_seq: Optional[Sequence] = None,
+        trace_headers: Optional[Dict[str, str]] = None,
     ) -> None:
         self.request_id = request_id
         self.seqs_dict = {seq.seq_id: seq for seq in seqs}
@@ -441,6 +443,7 @@ class SequenceGroup:
         self.embeddings = embeddings
         self.pooling_params = pooling_params
         self.encoder_seq = encoder_seq
+        self.trace_headers = trace_headers
 
     @property
     def prompt(self) -> Optional[str]:

vllm/tracing.py

@@ -0,0 +1,104 @@
+import os
+from typing import Mapping, Optional
+
+from vllm.logger import init_logger
+from vllm.utils import run_once
+
+TRACE_HEADERS = ["traceparent", "tracestate"]
+
+logger = init_logger(__name__)
+
+_is_otel_installed = False
+try:
+    from opentelemetry.context.context import Context
+    from opentelemetry.sdk.environment_variables import (
+        OTEL_EXPORTER_OTLP_TRACES_PROTOCOL)
+    from opentelemetry.sdk.trace import TracerProvider
+    from opentelemetry.sdk.trace.export import BatchSpanProcessor
+    from opentelemetry.semconv.ai import SpanAttributes as BaseSpanAttributes
+    from opentelemetry.trace import SpanKind, Tracer, set_tracer_provider
+    from opentelemetry.trace.propagation.tracecontext import (
+        TraceContextTextMapPropagator)
+    _is_otel_installed = True
+except ImportError:
+
+    class Context:  # type: ignore
+        pass
+
+    class BaseSpanAttributes:  # type: ignore
+        pass
+
+    class SpanKind:  # type: ignore
+        pass
+
+    class Tracer:  # type: ignore
+        pass
+
+
+def is_otel_installed() -> bool:
+    return _is_otel_installed
+
+
+def init_tracer(instrumenting_module_name: str,
+                otlp_traces_endpoint: str) -> Optional[Tracer]:
+    assert is_otel_installed(), ("OpenTelemetry packages must be installed "
+                                 "prior to initializing a tracer")
+    trace_provider = TracerProvider()
+
+    span_exporter = get_span_exporter(otlp_traces_endpoint)
+    trace_provider.add_span_processor(BatchSpanProcessor(span_exporter))
+    set_tracer_provider(trace_provider)
+
+    tracer = trace_provider.get_tracer(instrumenting_module_name)
+    return tracer
+
+
+def get_span_exporter(endpoint):
+    protocol = os.environ.get(OTEL_EXPORTER_OTLP_TRACES_PROTOCOL, "grpc")
+    if protocol == "grpc":
+        from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import (
+            OTLPSpanExporter)
+    elif protocol == "http/protobuf":
+        from opentelemetry.exporter.otlp.proto.http.trace_exporter import (
+            OTLPSpanExporter)
+    else:
+        raise ValueError(
+            f"Unsupported OTLP protocol '{protocol}' is configured")
+
+    return OTLPSpanExporter(endpoint=endpoint)
+
+
+def extract_trace_context(
+        headers: Optional[Mapping[str, str]]) -> Optional[Context]:
+    if is_otel_installed():
+        headers = headers or {}
+        return TraceContextTextMapPropagator().extract(headers)
+    else:
+        return None
+
+
+def extract_trace_headers(headers: Mapping[str, str]) -> Mapping[str, str]:
+
+    return {h: headers[h] for h in TRACE_HEADERS if h in headers}
+
+
+class SpanAttributes(BaseSpanAttributes):
+    # The following span attribute names are added here because they are missing
+    # from the Semantic Conventions for LLM.
+    LLM_REQUEST_ID = "gen_ai.request.id"
+    LLM_REQUEST_BEST_OF = "gen_ai.request.best_of"
+    LLM_REQUEST_N = "gen_ai.request.n"
+    LLM_USAGE_NUM_SEQUENCES = "gen_ai.usage.num_sequences"
+    LLM_LATENCY_TIME_IN_QUEUE = "gen_ai.latency.time_in_queue"
+    LLM_LATENCY_TIME_TO_FIRST_TOKEN = "gen_ai.latency.time_to_first_token"
+    LLM_LATENCY_E2E = "gen_ai.latency.e2e"
+
+
+def contains_trace_headers(headers: Mapping[str, str]) -> bool:
+    return any(h in headers for h in TRACE_HEADERS)
+
+
+@run_once
+def log_tracing_disabled_warning() -> None:
+    logger.warning(
+        "Received a request with trace context but tracing is disabled")

vllm/utils.py

@@ -763,3 +763,15 @@ def cuda_device_count_stateless() -> int:
     # after https://github.com/pytorch/pytorch/pull/122815 is released.
 
     return _cuda_device_count_stateless(envs.CUDA_VISIBLE_DEVICES)
+
+
+#From: https://stackoverflow.com/a/4104188/2749989
+def run_once(f):
+
+    def wrapper(*args, **kwargs) -> Any:
+        if not wrapper.has_run:  # type: ignore[attr-defined]
+            wrapper.has_run = True  # type: ignore[attr-defined]
+            return f(*args, **kwargs)
+
+    wrapper.has_run = False  # type: ignore[attr-defined]
+    return wrapper