diff --git a/llama_stack/providers/inline/telemetry/meta_reference/telemetry.py b/llama_stack/providers/inline/telemetry/meta_reference/telemetry.py index 7a993b891..014b800cc 100644 --- a/llama_stack/providers/inline/telemetry/meta_reference/telemetry.py +++ b/llama_stack/providers/inline/telemetry/meta_reference/telemetry.py @@ -79,7 +79,6 @@ class TelemetryAdapter(Telemetry): metrics.set_meter_provider(metric_provider) self.meter = metrics.get_meter(__name__) - self._lock = _global_lock async def initialize(self) -> None: diff --git a/llama_stack/providers/utils/telemetry/trace_protocol.py b/llama_stack/providers/utils/telemetry/trace_protocol.py index 916f7622a..e9320b7a8 100644 --- a/llama_stack/providers/utils/telemetry/trace_protocol.py +++ b/llama_stack/providers/utils/telemetry/trace_protocol.py @@ -70,7 +70,7 @@ def trace_protocol[T](cls: type[T]) -> type[T]: "__class__": class_name, "__method__": method_name, "__type__": span_type, - "__args__": str(combined_args), + 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a/tests/integration/common/recordings/models-64a2277c90f0f42576f60c1030e3a020403d34a95f56931b792d5939f4cebc57-826d44c3.json b/tests/integration/common/recordings/models-64a2277c90f0f42576f60c1030e3a020403d34a95f56931b792d5939f4cebc57-826d44c3.json new file mode 100644 index 000000000..a5f841baa --- /dev/null +++ b/tests/integration/common/recordings/models-64a2277c90f0f42576f60c1030e3a020403d34a95f56931b792d5939f4cebc57-826d44c3.json @@ -0,0 +1,88 @@ +{ + "test_id": null, + "request": { + "method": "POST", + "url": "http://0.0.0.0:11434/v1/v1/models", + "headers": {}, + "body": {}, + "endpoint": "/v1/models", + "model": "" + }, + "response": { + "body": [ + { + "__type__": "openai.types.model.Model", + "__data__": { + "id": "llama3.2:3b-instruct-fp16", + "created": 1760453641, + "object": "model", + "owned_by": "library" + } + }, + { + "__type__": "openai.types.model.Model", + "__data__": { + "id": "qwen3:4b", + "created": 1757615302, + "object": "model", + "owned_by": "library" + } + }, + { + "__type__": "openai.types.model.Model", + "__data__": { + "id": "gpt-oss:latest", + "created": 1756395223, + "object": "model", + "owned_by": "library" + } + }, + { + "__type__": "openai.types.model.Model", + "__data__": { + "id": "nomic-embed-text:latest", + "created": 1756318548, + "object": "model", + "owned_by": "library" + } + }, + { + "__type__": "openai.types.model.Model", + "__data__": { + "id": "llama3.2:3b", + "created": 1755191039, + "object": "model", + "owned_by": "library" + } + }, + { + "__type__": "openai.types.model.Model", + "__data__": { + "id": "all-minilm:l6-v2", + "created": 1753968177, + "object": "model", + "owned_by": "library" + } + }, + { + "__type__": "openai.types.model.Model", + "__data__": { + "id": "llama3.2:1b", + "created": 1746124735, + "object": "model", + "owned_by": "library" + } + }, + { + "__type__": "openai.types.model.Model", + "__data__": { + "id": "llama3.2:latest", + "created": 1746044170, + "object": "model", + "owned_by": "library" + } + } + ], + "is_streaming": false + } +} diff --git a/tests/integration/telemetry/conftest.py b/tests/integration/telemetry/conftest.py new file mode 100644 index 000000000..d11f00c9f --- /dev/null +++ b/tests/integration/telemetry/conftest.py @@ -0,0 +1,95 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +"""Telemetry test configuration using OpenTelemetry SDK exporters. + +This conftest provides in-memory telemetry collection for library_client mode only. +Tests using these fixtures should skip in server mode since the in-memory collector +cannot access spans from a separate server process. +""" + +from typing import Any + +import opentelemetry.metrics as otel_metrics +import opentelemetry.trace as otel_trace +import pytest +from opentelemetry import metrics, trace +from opentelemetry.sdk.metrics import MeterProvider +from opentelemetry.sdk.metrics.export import InMemoryMetricReader +from opentelemetry.sdk.trace import ReadableSpan, TracerProvider +from opentelemetry.sdk.trace.export import SimpleSpanProcessor +from opentelemetry.sdk.trace.export.in_memory_span_exporter import InMemorySpanExporter + +import llama_stack.providers.inline.telemetry.meta_reference.telemetry as telemetry_module +from llama_stack.testing.api_recorder import patch_httpx_for_test_id +from tests.integration.fixtures.common import instantiate_llama_stack_client + + +class TestCollector: + def __init__(self, span_exp, metric_read): + assert span_exp and metric_read + self.span_exporter = span_exp + self.metric_reader = metric_read + + def get_spans(self) -> tuple[ReadableSpan, ...]: + return self.span_exporter.get_finished_spans() + + def get_metrics(self) -> Any | None: + metrics = self.metric_reader.get_metrics_data() + if metrics and metrics.resource_metrics: + return metrics.resource_metrics[0].scope_metrics[0].metrics + return None + + def clear(self) -> None: + self.span_exporter.clear() + self.metric_reader.get_metrics_data() + + +@pytest.fixture(scope="session") +def _telemetry_providers(): + """Set up in-memory OTEL providers before llama_stack_client initializes.""" + # Reset set-once flags to allow re-initialization + if hasattr(otel_trace, "_TRACER_PROVIDER_SET_ONCE"): + otel_trace._TRACER_PROVIDER_SET_ONCE._done = False # type: ignore + if hasattr(otel_metrics, "_METER_PROVIDER_SET_ONCE"): + otel_metrics._METER_PROVIDER_SET_ONCE._done = False # type: ignore + + # Create in-memory exporters/readers + span_exporter = InMemorySpanExporter() + tracer_provider = TracerProvider() + tracer_provider.add_span_processor(SimpleSpanProcessor(span_exporter)) + trace.set_tracer_provider(tracer_provider) + + metric_reader = InMemoryMetricReader() + meter_provider = MeterProvider(metric_readers=[metric_reader]) + metrics.set_meter_provider(meter_provider) + + # Set module-level provider so TelemetryAdapter uses our in-memory providers + telemetry_module._TRACER_PROVIDER = tracer_provider + + yield (span_exporter, metric_reader, tracer_provider, meter_provider) + + telemetry_module._TRACER_PROVIDER = None + tracer_provider.shutdown() + meter_provider.shutdown() + + +@pytest.fixture(scope="session") +def llama_stack_client(_telemetry_providers, request): + """Override llama_stack_client to ensure in-memory telemetry providers are used.""" + patch_httpx_for_test_id() + client = instantiate_llama_stack_client(request.session) + + return client + + +@pytest.fixture +def mock_otlp_collector(_telemetry_providers): + """Provides access to telemetry data and clears between tests.""" + span_exporter, metric_reader, _, _ = _telemetry_providers + collector = TestCollector(span_exporter, metric_reader) + yield collector + collector.clear() diff --git a/tests/integration/telemetry/recordings/0de60cd6a6ec3dbfc4a7601e77be8083caf34f49147ad1c25efae1de3f0b25e5.json b/tests/integration/telemetry/recordings/0de60cd6a6ec3dbfc4a7601e77be8083caf34f49147ad1c25efae1de3f0b25e5.json new file mode 100644 index 000000000..ce518f01d --- /dev/null +++ b/tests/integration/telemetry/recordings/0de60cd6a6ec3dbfc4a7601e77be8083caf34f49147ad1c25efae1de3f0b25e5.json @@ -0,0 +1,57 @@ +{ + "test_id": "tests/integration/telemetry/test_openai_telemetry.py::test_openai_completion_creates_telemetry[txt=ollama/llama3.2:3b-instruct-fp16]", + "request": { + "method": "POST", + "url": "http://0.0.0.0:11434/v1/v1/chat/completions", + "headers": {}, + "body": { + "model": "llama3.2:3b-instruct-fp16", + "messages": [ + { + "role": "user", + "content": "Test OpenAI telemetry creation" + } + ], + "stream": false + }, + "endpoint": "/v1/chat/completions", + "model": "llama3.2:3b-instruct-fp16" + }, + "response": { + "body": { + "__type__": "openai.types.chat.chat_completion.ChatCompletion", + "__data__": { + "id": "rec-0de60cd6a6ec", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "logprobs": null, + "message": { + "content": "I'm happy to help you with setting up and testing OpenAI's telemetry creation.\n\nOpenAI provides a feature called \"Telemetry\" which allows developers to collect data about their users' interactions with the model. To test this feature, we need to create a simple application that uses the OpenAI API and sends telemetry data to their servers.\n\nHere's an example code in Python that demonstrates how to create a simple telemetry creator:\n\n```python\nimport os\nfrom openai.api import API\n\n# Initialize the OpenAI API client\napi = API(os.environ['OPENAI_API_KEY'])\n\ndef create_user():\n # Create a new user entity\n user_entity = {\n 'id': 'user-123',\n 'name': 'John Doe',\n 'email': 'john.doe@example.com'\n }\n \n # Send the user creation request to OpenAI\n response = api.users.create(user_entity)\n print(f\"User created: {response}\")\n\ndef create_transaction():\n # Create a new transaction entity\n transaction_entity = {\n 'id': 'tran-123',\n 'user_id': 'user-123',\n 'transaction_type': 'query'\n }\n \n # Send the transaction creation request to OpenAI\n response = api.transactions.create(transaction_entity)\n print(f\"Transaction created: {response}\")\n\ndef send_telemetry_data():\n # Create a new telemetry event entity\n telemetry_event_entity = {\n 'id': 'telem-123',\n 'transaction_id': 'tran-123',\n 'data': '{ \"event\": \"test\", \"user_id\": 1 }'\n }\n \n # Send the telemetry data to OpenAI\n response = api.telemetry.create(telemetry_event_entity)\n print(f\"Telemetry event sent: {response}\")\n\n# Test the telemetry creation\ncreate_user()\ncreate_transaction()\nsend_telemetry_data()\n```\n\nMake sure you replace `OPENAI_API_KEY` with your actual API key. Also, ensure that you have the OpenAI API client library installed by running `pip install openai`.\n\nOnce you've created the test code, run it and observe the behavior of the telemetry creation process.\n\nPlease let me know if you need further modifications or assistance!", + "refusal": null, + "role": "assistant", + "annotations": null, + "audio": null, + "function_call": null, + "tool_calls": null + } + } + ], + "created": 0, + "model": "llama3.2:3b-instruct-fp16", + "object": "chat.completion", + "service_tier": null, + "system_fingerprint": "fp_ollama", + "usage": { + "completion_tokens": 460, + "prompt_tokens": 30, + "total_tokens": 490, + "completion_tokens_details": null, + "prompt_tokens_details": null + } + } + }, + "is_streaming": false + } +} diff --git a/tests/integration/telemetry/recordings/1fcfd86d8111374dc852cfdea6bfdb6a511f92cee84a6325b04ae84878512c30.json b/tests/integration/telemetry/recordings/1fcfd86d8111374dc852cfdea6bfdb6a511f92cee84a6325b04ae84878512c30.json 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"stream": false, + "temperature": 0.7 + }, + "endpoint": "/v1/chat/completions", + "model": "llama3.2:3b-instruct-fp16" + }, + "response": { + "body": { + "__type__": "openai.types.chat.chat_completion.ChatCompletion", + "__data__": { + "id": "rec-dba5042d6691", + "choices": [ + { + "finish_reason": "length", + "index": 0, + "logprobs": null, + "message": { + "content": "To test the \"trace\" functionality of OpenAI's GPT-4 model at a temperature of 0.7, you can follow these steps:\n\n1. First, make sure you have an account with OpenAI and have been granted access to their API.\n\n2. You will need to install the `transformers` library, which is the official library for working with Transformers models like GPT-4:\n\n ```bash\npip install transformers\n```\n\n3. Next, import the necessary", + "refusal": null, + "role": "assistant", + "annotations": null, + "audio": null, + "function_call": null, + "tool_calls": null + } + } + ], + "created": 0, + "model": "llama3.2:3b-instruct-fp16", + "object": "chat.completion", + "service_tier": null, + "system_fingerprint": "fp_ollama", + "usage": { + "completion_tokens": 100, + "prompt_tokens": 35, + "total_tokens": 135, + "completion_tokens_details": null, + "prompt_tokens_details": null + } + } + }, + "is_streaming": false + } +} diff --git a/tests/integration/telemetry/test_completions.py b/tests/integration/telemetry/test_completions.py new file mode 100644 index 000000000..77ca4d51c --- /dev/null +++ b/tests/integration/telemetry/test_completions.py @@ -0,0 +1,112 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +"""Telemetry tests verifying @trace_protocol decorator format using in-memory exporter.""" + +import json +import os + +import pytest + +pytestmark = pytest.mark.skipif( + os.environ.get("LLAMA_STACK_TEST_STACK_CONFIG_TYPE") == "server", + reason="In-memory telemetry tests only work in library_client mode (server mode runs in separate process)", +) + + +def test_streaming_chunk_count(mock_otlp_collector, llama_stack_client, text_model_id): + """Verify streaming adds chunk_count and __type__=async_generator.""" + + stream = llama_stack_client.chat.completions.create( + model=text_model_id, + messages=[{"role": "user", "content": "Test trace openai 1"}], + stream=True, + ) + + chunks = list(stream) + assert len(chunks) > 0 + + spans = mock_otlp_collector.get_spans() + assert len(spans) > 0 + + chunk_count = None + for span in spans: + if span.attributes.get("__type__") == "async_generator": + chunk_count = span.attributes.get("chunk_count") + if chunk_count: + chunk_count = int(chunk_count) + break + + assert chunk_count is not None + assert chunk_count == len(chunks) + + +def test_telemetry_format_completeness(mock_otlp_collector, llama_stack_client, text_model_id): + """Comprehensive validation of telemetry data format including spans and metrics.""" + response = llama_stack_client.chat.completions.create( + model=text_model_id, + messages=[{"role": "user", "content": "Test trace openai with temperature 0.7"}], + temperature=0.7, + max_tokens=100, + stream=False, + ) + + # Handle both dict and Pydantic model for usage + # This occurs due to the replay system returning a dict for usage, but the client returning a Pydantic model + # TODO: Fix this by making the replay system return a Pydantic model for usage + usage = response.usage if isinstance(response.usage, dict) else response.usage.model_dump() + assert usage.get("prompt_tokens") and usage["prompt_tokens"] > 0 + assert usage.get("completion_tokens") and usage["completion_tokens"] > 0 + assert usage.get("total_tokens") and usage["total_tokens"] > 0 + + # Verify spans + spans = mock_otlp_collector.get_spans() + assert len(spans) == 5 + + # we only need this captured one time + logged_model_id = None + + for span in spans: + attrs = span.attributes + assert attrs is not None + + # Root span is created manually by tracing middleware, not by @trace_protocol decorator + is_root_span = attrs.get("__root__") is True + + if is_root_span: + # Root spans have different attributes + assert attrs.get("__location__") in ["library_client", "server"] + else: + # Non-root spans are created by @trace_protocol decorator + assert attrs.get("__autotraced__") + assert attrs.get("__class__") and attrs.get("__method__") + assert attrs.get("__type__") in ["async", "sync", "async_generator"] + + args = json.loads(attrs["__args__"]) + if "model_id" in args: + logged_model_id = args["model_id"] + + assert logged_model_id is not None + assert logged_model_id == text_model_id + + # TODO: re-enable this once metrics get fixed + """ + # Verify token usage metrics in response + metrics = mock_otlp_collector.get_metrics() + + assert metrics + for metric in metrics: + assert metric.name in ["completion_tokens", "total_tokens", "prompt_tokens"] + assert metric.unit == "tokens" + assert metric.data.data_points and len(metric.data.data_points) == 1 + match metric.name: + case "completion_tokens": + assert metric.data.data_points[0].value == usage["completion_tokens"] + case "total_tokens": + assert metric.data.data_points[0].value == usage["total_tokens"] + case "prompt_tokens": + assert metric.data.data_points[0].value == usage["prompt_tokens" + """