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# What does this PR do? - as title, cleaning up `import *`'s - upgrade tests to make them more robust to bad model outputs - remove import *'s in llama_stack/apis/* (skip __init__ modules) <img width="465" alt="image" src="https://github.com/user-attachments/assets/d8339c13-3b40-4ba5-9c53-0d2329726ee2" /> - run `sh run_openapi_generator.sh`, no types gets affected ## Test Plan ### Providers Tests **agents** ``` pytest -v -s llama_stack/providers/tests/agents/test_agents.py -m "together" --safety-shield meta-llama/Llama-Guard-3-8B --inference-model meta-llama/Llama-3.1-405B-Instruct-FP8 ``` **inference** ```bash # meta-reference torchrun $CONDA_PREFIX/bin/pytest -v -s -k "meta_reference" --inference-model="meta-llama/Llama-3.1-8B-Instruct" ./llama_stack/providers/tests/inference/test_text_inference.py torchrun $CONDA_PREFIX/bin/pytest -v -s -k "meta_reference" --inference-model="meta-llama/Llama-3.2-11B-Vision-Instruct" ./llama_stack/providers/tests/inference/test_vision_inference.py # together pytest -v -s -k "together" --inference-model="meta-llama/Llama-3.1-8B-Instruct" ./llama_stack/providers/tests/inference/test_text_inference.py pytest -v -s -k "together" --inference-model="meta-llama/Llama-3.2-11B-Vision-Instruct" ./llama_stack/providers/tests/inference/test_vision_inference.py pytest ./llama_stack/providers/tests/inference/test_prompt_adapter.py ``` **safety** ``` pytest -v -s llama_stack/providers/tests/safety/test_safety.py -m together --safety-shield meta-llama/Llama-Guard-3-8B ``` **memory** ``` pytest -v -s llama_stack/providers/tests/memory/test_memory.py -m "sentence_transformers" --env EMBEDDING_DIMENSION=384 ``` **scoring** ``` pytest -v -s -m llm_as_judge_scoring_together_inference llama_stack/providers/tests/scoring/test_scoring.py --judge-model meta-llama/Llama-3.2-3B-Instruct pytest -v -s -m basic_scoring_together_inference llama_stack/providers/tests/scoring/test_scoring.py pytest -v -s -m braintrust_scoring_together_inference llama_stack/providers/tests/scoring/test_scoring.py ``` **datasetio** ``` pytest -v -s -m localfs llama_stack/providers/tests/datasetio/test_datasetio.py pytest -v -s -m huggingface llama_stack/providers/tests/datasetio/test_datasetio.py ``` **eval** ``` pytest -v -s -m meta_reference_eval_together_inference llama_stack/providers/tests/eval/test_eval.py pytest -v -s -m meta_reference_eval_together_inference_huggingface_datasetio llama_stack/providers/tests/eval/test_eval.py ``` ### Client-SDK Tests ``` LLAMA_STACK_BASE_URL=http://localhost:5000 pytest -v ./tests/client-sdk ``` ### llama-stack-apps ``` PORT=5000 LOCALHOST=localhost python -m examples.agents.hello $LOCALHOST $PORT python -m examples.agents.inflation $LOCALHOST $PORT python -m examples.agents.podcast_transcript $LOCALHOST $PORT python -m examples.agents.rag_as_attachments $LOCALHOST $PORT python -m examples.agents.rag_with_memory_bank $LOCALHOST $PORT python -m examples.safety.llama_guard_demo_mm $LOCALHOST $PORT python -m examples.agents.e2e_loop_with_custom_tools $LOCALHOST $PORT # Vision model python -m examples.interior_design_assistant.app python -m examples.agent_store.app $LOCALHOST $PORT ``` ### CLI ``` which llama llama model prompt-format -m Llama3.2-11B-Vision-Instruct llama model list llama stack list-apis llama stack list-providers inference llama stack build --template ollama --image-type conda ``` ### Distributions Tests **ollama** ``` llama stack build --template ollama --image-type conda ollama run llama3.2:1b-instruct-fp16 llama stack run ./llama_stack/templates/ollama/run.yaml --env INFERENCE_MODEL=meta-llama/Llama-3.2-1B-Instruct ``` **fireworks** ``` llama stack build --template fireworks --image-type conda llama stack run ./llama_stack/templates/fireworks/run.yaml ``` **together** ``` llama stack build --template together --image-type conda llama stack run ./llama_stack/templates/together/run.yaml ``` **tgi** ``` llama stack run ./llama_stack/templates/tgi/run.yaml --env TGI_URL=http://0.0.0.0:5009 --env INFERENCE_MODEL=meta-llama/Llama-3.1-8B-Instruct ``` ## Sources Please link relevant resources if necessary. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Ran pre-commit to handle lint / formatting issues. - [ ] Read the [contributor guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md), Pull Request section? - [ ] Updated relevant documentation. - [ ] Wrote necessary unit or integration tests.
263 lines
9.8 KiB
Python
263 lines
9.8 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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SpanEndPayload,
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SpanStartPayload,
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SpanStatus,
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SpanWithStatus,
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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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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[Api.datasetio]
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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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provider = TracerProvider(resource=resource)
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trace.set_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(
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resource=resource, metric_readers=[metric_reader]
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)
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metrics.set_meter_provider(metric_provider)
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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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trace.get_tracer_provider().add_span_processor(
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SQLiteSpanProcessor(self.config.sqlite_db_path)
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)
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self.trace_store = SQLiteTraceStore(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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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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trace.get_tracer_provider().shutdown()
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metrics.get_meter_provider().shutdown()
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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(
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f"Warning: No active span found for span_id {span_id}. Dropping event: {event}"
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)
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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 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(
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event.metric, event.unit
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)
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up_down_counter.add(event.value, attributes=event.attributes)
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def _get_or_create_up_down_counter(
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self, name: str, unit: str
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) -> 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] = (
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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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)
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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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) -> List[Trace]:
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return 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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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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) -> Dict[str, SpanWithStatus]:
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return 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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