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# What does this PR do? Inference router computes the token usage related metrics for all providers and returns the metrics as part of response and also logs to telemetry. ## Test Plan LLAMA_STACK_DISABLE_VERSION_CHECK=true llama stack run ~/.llama/distributions/fireworks/fireworks-run.yaml ``` curl --request POST \ --url http://localhost:8321/v1/inference/chat-completion \ --header 'content-type: application/json' \ --data '{ "model_id": "meta-llama/Llama-3.1-70B-Instruct", "messages": [ { "role": "user", "content": { "type": "text", "text": "where do humans live" } } ], "stream": false }' | jq . { "metrics": [ { "trace_id": "yjv1tf0jS1evOyPm", "span_id": "WqYKvg0_", "timestamp": "2025-02-27T18:55:10.770903Z", "attributes": { "model_id": "meta-llama/Llama-3.1-70B-Instruct", "provider_id": "fireworks" }, "type": "metric", "metric": "prompt_tokens", "value": 10, "unit": "tokens" }, { "trace_id": "yjv1tf0jS1evOyPm", "span_id": "WqYKvg0_", "timestamp": "2025-02-27T18:55:10.770916Z", "attributes": { "model_id": "meta-llama/Llama-3.1-70B-Instruct", "provider_id": "fireworks" }, "type": "metric", "metric": "completion_tokens", "value": 411, "unit": "tokens" }, { "trace_id": "yjv1tf0jS1evOyPm", "span_id": "WqYKvg0_", "timestamp": "2025-02-27T18:55:10.770919Z", "attributes": { "model_id": "meta-llama/Llama-3.1-70B-Instruct", "provider_id": "fireworks" }, "type": "metric", "metric": "total_tokens", "value": 421, "unit": "tokens" } ], "completion_message": { "role": "assistant", "content": "Humans live in various parts of the world, inhabiting almost every continent, country, and region. Here's a breakdown of where humans live:\n\n1. **Continents:** Humans inhabit all seven continents:\n\t* Africa\n\t* Antarctica (research stations only)\n\t* Asia\n\t* Australia\n\t* Europe\n\t* North America\n\t* South America\n2. **Countries:** There are 196 countries recognized by the United Nations, and humans live in almost all of them.\n3. **Regions:** Humans live in diverse regions, including:\n\t* Deserts (e.g., Sahara, Mojave)\n\t* Forests (e.g., Amazon, Congo)\n\t* Grasslands (e.g., Prairies, Steppes)\n\t* Mountains (e.g., Himalayas, Andes)\n\t* Oceans (e.g., coastal areas, islands)\n\t* Tundras (e.g., Arctic, sub-Arctic)\n4. **Cities and towns:** Many humans live in urban areas, such as cities and towns, which are often located near:\n\t* Coastlines\n\t* Rivers\n\t* Lakes\n\t* Mountains\n5. **Rural areas:** Some humans live in rural areas, such as:\n\t* Villages\n\t* Farms\n\t* Countryside\n6. **Islands:** Humans inhabit many islands, including:\n\t* Tropical islands (e.g., Hawaii, Maldives)\n\t* Arctic islands (e.g., Greenland, Iceland)\n\t* Continental islands (e.g., Great Britain, Ireland)\n7. **Extreme environments:** Humans also live in extreme environments, such as:\n\t* High-altitude areas (e.g., Tibet, Andes)\n\t* Low-altitude areas (e.g., Death Valley, Dead Sea)\n\t* Areas with extreme temperatures (e.g., Arctic, Sahara)\n\nOverall, humans have adapted to live in a wide range of environments and ecosystems around the world.", "stop_reason": "end_of_turn", "tool_calls": [] }, "logprobs": null } ``` ``` LLAMA_STACK_CONFIG=fireworks pytest -s -v tests/integration/inference ======================================================================== short test summary info ========================================================================= FAILED tests/integration/inference/test_text_inference.py::test_text_chat_completion_tool_calling_tools_not_in_request[txt=8B:vis=11B-inference:chat_completion:tool_calling_tools_absent-True] - ValueError: Unsupported tool prompt format: ToolPromptFormat.json FAILED tests/integration/inference/test_text_inference.py::test_text_chat_completion_tool_calling_tools_not_in_request[txt=8B:vis=11B-inference:chat_completion:tool_calling_tools_absent-False] - ValueError: Unsupported tool prompt format: ToolPromptFormat.json FAILED tests/integration/inference/test_vision_inference.py::test_image_chat_completion_non_streaming[txt=8B:vis=11B] - fireworks.client.error.InvalidRequestError: {'error': {'object': 'error', 'type': 'invalid_request_error', 'message': 'Failed to decode image cannot identify image f... FAILED tests/integration/inference/test_vision_inference.py::test_image_chat_completion_streaming[txt=8B:vis=11B] - fireworks.client.error.InvalidRequestError: {'error': {'object': 'error', 'type': 'invalid_request_error', 'message': 'Failed to decode image cannot identify image f... ========================================================= 4 failed, 16 passed, 23 xfailed, 17 warnings in 44.36s ========================================================= ```
274 lines
11 KiB
Python
274 lines
11 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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import threading
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from typing import Any, Dict, List, Optional
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from opentelemetry import metrics, trace
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from opentelemetry.exporter.otlp.proto.http.metric_exporter import OTLPMetricExporter
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from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
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from opentelemetry.sdk.metrics import MeterProvider
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from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader
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from opentelemetry.sdk.resources import Resource
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from opentelemetry.sdk.trace import TracerProvider
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from opentelemetry.sdk.trace.export import BatchSpanProcessor
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from opentelemetry.semconv.resource import ResourceAttributes
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from llama_stack.apis.telemetry import (
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Event,
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MetricEvent,
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QueryCondition,
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QuerySpanTreeResponse,
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QueryTracesResponse,
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Span,
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SpanEndPayload,
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SpanStartPayload,
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SpanStatus,
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StructuredLogEvent,
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Telemetry,
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Trace,
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UnstructuredLogEvent,
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)
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from llama_stack.distribution.datatypes import Api
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from llama_stack.providers.inline.telemetry.meta_reference.console_span_processor import (
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ConsoleSpanProcessor,
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)
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from llama_stack.providers.inline.telemetry.meta_reference.sqlite_span_processor import (
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SQLiteSpanProcessor,
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)
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from llama_stack.providers.utils.telemetry.dataset_mixin import TelemetryDatasetMixin
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from llama_stack.providers.utils.telemetry.sqlite_trace_store import SQLiteTraceStore
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from .config import TelemetryConfig, TelemetrySink
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_GLOBAL_STORAGE = {
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"active_spans": {},
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"counters": {},
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"gauges": {},
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"up_down_counters": {},
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}
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_global_lock = threading.Lock()
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_TRACER_PROVIDER = None
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def string_to_trace_id(s: str) -> int:
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# Convert the string to bytes and then to an integer
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return int.from_bytes(s.encode(), byteorder="big", signed=False)
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def string_to_span_id(s: str) -> int:
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# Use only the first 8 bytes (64 bits) for span ID
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return int.from_bytes(s.encode()[:8], byteorder="big", signed=False)
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def is_tracing_enabled(tracer):
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with tracer.start_as_current_span("check_tracing") as span:
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return span.is_recording()
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class TelemetryAdapter(TelemetryDatasetMixin, Telemetry):
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def __init__(self, config: TelemetryConfig, deps: Dict[str, Any]) -> None:
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self.config = config
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self.datasetio_api = deps.get(Api.datasetio)
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self.meter = None
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resource = Resource.create(
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{
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ResourceAttributes.SERVICE_NAME: self.config.service_name,
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}
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)
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global _TRACER_PROVIDER
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# Initialize the correct span processor based on the provider state.
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# This is needed since once the span processor is set, it cannot be unset.
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# Recreating the telemetry adapter multiple times will result in duplicate span processors.
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# Since the library client can be recreated multiple times in a notebook,
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# the kernel will hold on to the span processor and cause duplicate spans to be written.
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if _TRACER_PROVIDER is None:
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provider = TracerProvider(resource=resource)
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trace.set_tracer_provider(provider)
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_TRACER_PROVIDER = provider
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if TelemetrySink.OTEL in self.config.sinks:
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otlp_exporter = OTLPSpanExporter(
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endpoint=self.config.otel_endpoint,
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)
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span_processor = BatchSpanProcessor(otlp_exporter)
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trace.get_tracer_provider().add_span_processor(span_processor)
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metric_reader = PeriodicExportingMetricReader(
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OTLPMetricExporter(
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endpoint=self.config.otel_endpoint,
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)
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)
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metric_provider = MeterProvider(resource=resource, metric_readers=[metric_reader])
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metrics.set_meter_provider(metric_provider)
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if TelemetrySink.SQLITE in self.config.sinks:
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trace.get_tracer_provider().add_span_processor(SQLiteSpanProcessor(self.config.sqlite_db_path))
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if TelemetrySink.CONSOLE in self.config.sinks:
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trace.get_tracer_provider().add_span_processor(ConsoleSpanProcessor())
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if TelemetrySink.OTEL in self.config.sinks:
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self.meter = metrics.get_meter(__name__)
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if TelemetrySink.SQLITE in self.config.sinks:
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self.trace_store = SQLiteTraceStore(self.config.sqlite_db_path)
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self._lock = _global_lock
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async def initialize(self) -> None:
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pass
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async def shutdown(self) -> None:
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trace.get_tracer_provider().force_flush()
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async def log_event(self, event: Event, ttl_seconds: int = 604800) -> None:
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if isinstance(event, UnstructuredLogEvent):
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self._log_unstructured(event, ttl_seconds)
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elif isinstance(event, MetricEvent):
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self._log_metric(event)
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elif isinstance(event, StructuredLogEvent):
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self._log_structured(event, ttl_seconds)
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else:
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raise ValueError(f"Unknown event type: {event}")
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def _log_unstructured(self, event: UnstructuredLogEvent, ttl_seconds: int) -> None:
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with self._lock:
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# Use global storage instead of instance storage
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span_id = string_to_span_id(event.span_id)
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span = _GLOBAL_STORAGE["active_spans"].get(span_id)
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if span:
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timestamp_ns = int(event.timestamp.timestamp() * 1e9)
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span.add_event(
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name=event.type,
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attributes={
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"message": event.message,
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"severity": event.severity.value,
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"__ttl__": ttl_seconds,
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**event.attributes,
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},
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timestamp=timestamp_ns,
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)
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else:
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print(f"Warning: No active span found for span_id {span_id}. Dropping event: {event}")
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def _get_or_create_counter(self, name: str, unit: str) -> metrics.Counter:
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if name not in _GLOBAL_STORAGE["counters"]:
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_GLOBAL_STORAGE["counters"][name] = self.meter.create_counter(
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name=name,
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unit=unit,
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description=f"Counter for {name}",
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)
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return _GLOBAL_STORAGE["counters"][name]
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def _get_or_create_gauge(self, name: str, unit: str) -> metrics.ObservableGauge:
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if name not in _GLOBAL_STORAGE["gauges"]:
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_GLOBAL_STORAGE["gauges"][name] = self.meter.create_gauge(
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name=name,
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unit=unit,
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description=f"Gauge for {name}",
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)
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return _GLOBAL_STORAGE["gauges"][name]
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def _log_metric(self, event: MetricEvent) -> None:
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if self.meter is None:
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return
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if isinstance(event.value, int):
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counter = self._get_or_create_counter(event.metric, event.unit)
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counter.add(event.value, attributes=event.attributes)
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elif isinstance(event.value, float):
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up_down_counter = self._get_or_create_up_down_counter(event.metric, event.unit)
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up_down_counter.add(event.value, attributes=event.attributes)
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def _get_or_create_up_down_counter(self, name: str, unit: str) -> metrics.UpDownCounter:
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if name not in _GLOBAL_STORAGE["up_down_counters"]:
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_GLOBAL_STORAGE["up_down_counters"][name] = self.meter.create_up_down_counter(
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name=name,
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unit=unit,
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description=f"UpDownCounter for {name}",
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)
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return _GLOBAL_STORAGE["up_down_counters"][name]
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def _log_structured(self, event: StructuredLogEvent, ttl_seconds: int) -> None:
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with self._lock:
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span_id = string_to_span_id(event.span_id)
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trace_id = string_to_trace_id(event.trace_id)
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tracer = trace.get_tracer(__name__)
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if event.attributes is None:
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event.attributes = {}
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event.attributes["__ttl__"] = ttl_seconds
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if isinstance(event.payload, SpanStartPayload):
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# Check if span already exists to prevent duplicates
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if span_id in _GLOBAL_STORAGE["active_spans"]:
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return
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parent_span = None
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if event.payload.parent_span_id:
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parent_span_id = string_to_span_id(event.payload.parent_span_id)
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parent_span = _GLOBAL_STORAGE["active_spans"].get(parent_span_id)
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context = trace.Context(trace_id=trace_id)
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if parent_span:
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context = trace.set_span_in_context(parent_span, context)
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span = tracer.start_span(
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name=event.payload.name,
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context=context,
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attributes=event.attributes or {},
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)
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_GLOBAL_STORAGE["active_spans"][span_id] = span
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elif isinstance(event.payload, SpanEndPayload):
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span = _GLOBAL_STORAGE["active_spans"].get(span_id)
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if span:
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if event.attributes:
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span.set_attributes(event.attributes)
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status = (
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trace.Status(status_code=trace.StatusCode.OK)
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if event.payload.status == SpanStatus.OK
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else trace.Status(status_code=trace.StatusCode.ERROR)
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)
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span.set_status(status)
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span.end()
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_GLOBAL_STORAGE["active_spans"].pop(span_id, None)
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else:
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raise ValueError(f"Unknown structured log event: {event}")
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async def query_traces(
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self,
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attribute_filters: Optional[List[QueryCondition]] = None,
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limit: Optional[int] = 100,
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offset: Optional[int] = 0,
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order_by: Optional[List[str]] = None,
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) -> QueryTracesResponse:
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return QueryTracesResponse(
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data=await self.trace_store.query_traces(
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attribute_filters=attribute_filters,
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limit=limit,
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offset=offset,
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order_by=order_by,
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)
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)
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async def get_trace(self, trace_id: str) -> Trace:
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return await self.trace_store.get_trace(trace_id)
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async def get_span(self, trace_id: str, span_id: str) -> Span:
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return await self.trace_store.get_span(trace_id, span_id)
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async def get_span_tree(
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self,
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span_id: str,
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attributes_to_return: Optional[List[str]] = None,
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max_depth: Optional[int] = None,
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) -> QuerySpanTreeResponse:
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return QuerySpanTreeResponse(
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data=await self.trace_store.get_span_tree(
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span_id=span_id,
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attributes_to_return=attributes_to_return,
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max_depth=max_depth,
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)
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)
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