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test(telemetry): Telemetry Tests (#3805)
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# What does this PR do? Adds a test and a standardized way to build future tests out for telemetry in llama stack. Contributes to https://github.com/llamastack/llama-stack/issues/3806 ## Test Plan This is the test plan 😎
This commit is contained in:
Emilio Garcia
GitHub
parent
224c99560c
commit
943558af36
11 changed files with 9452 additions and 3 deletions
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@ -79,7 +79,6 @@ class TelemetryAdapter(Telemetry):
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metrics.set_meter_provider(metric_provider)
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self.meter = metrics.get_meter(__name__)
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self._lock = _global_lock
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async def initialize(self) -> None:
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@ -70,7 +70,7 @@ def trace_protocol[T](cls: type[T]) -> type[T]:
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"__class__": class_name,
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"__method__": method_name,
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"__type__": span_type,
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"__args__": str(combined_args),
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"__args__": json.dumps(combined_args),
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}
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return class_name, method_name, span_attributes
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@ -82,8 +82,8 @@ def trace_protocol[T](cls: type[T]) -> type[T]:
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class_name, method_name, span_attributes = create_span_context(self, *args, **kwargs)
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with tracing.span(f"{class_name}.{method_name}", span_attributes) as span:
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count = 0
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try:
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count = 0
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async for item in method(self, *args, **kwargs):
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yield item
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count += 1
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@ -0,0 +1,506 @@
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"system_fingerprint": "fp_ollama",
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}
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},
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{
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"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
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"__data__": {
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"id": "rec-ab1a32474062",
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"choices": [
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{
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"delta": {
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"content": "!",
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"model": "llama3.2:3b-instruct-fp16",
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"object": "chat.completion.chunk",
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"system_fingerprint": "fp_ollama",
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"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
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"__data__": {
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"id": "rec-ab1a32474062",
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"choices": [
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{
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"system_fingerprint": "fp_ollama",
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"usage": null
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}
|
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],
|
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"is_streaming": true
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,88 @@
|
|||
{
|
||||
"test_id": null,
|
||||
"request": {
|
||||
"method": "POST",
|
||||
"url": "http://0.0.0.0:11434/v1/v1/models",
|
||||
"headers": {},
|
||||
"body": {},
|
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"endpoint": "/v1/models",
|
||||
"model": ""
|
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},
|
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"response": {
|
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"body": [
|
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{
|
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"__type__": "openai.types.model.Model",
|
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{
|
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"__type__": "openai.types.model.Model",
|
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|
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"id": "qwen3:4b",
|
||||
"created": 1757615302,
|
||||
"object": "model",
|
||||
"owned_by": "library"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.model.Model",
|
||||
"__data__": {
|
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"id": "gpt-oss:latest",
|
||||
"created": 1756395223,
|
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"object": "model",
|
||||
"owned_by": "library"
|
||||
}
|
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},
|
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{
|
||||
"__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
|
||||
}
|
||||
}
|
||||
95
tests/integration/telemetry/conftest.py
Normal file
95
tests/integration/telemetry/conftest.py
Normal file
|
|
@ -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()
|
||||
|
|
@ -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": {
|
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"method": "POST",
|
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"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": {
|
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"body": {
|
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"__type__": "openai.types.chat.chat_completion.ChatCompletion",
|
||||
"__data__": {
|
||||
"id": "rec-0de60cd6a6ec",
|
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"choices": [
|
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{
|
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"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",
|
||||
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|
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|
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"created": 0,
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"model": "llama3.2:3b-instruct-fp16",
|
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"object": "chat.completion",
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"service_tier": null,
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"system_fingerprint": "fp_ollama",
|
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"usage": {
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"prompt_tokens_details": null
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},
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"is_streaming": false
|
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}
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}
|
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|
|
@ -0,0 +1,59 @@
|
|||
{
|
||||
"test_id": "tests/integration/telemetry/test_completions.py::test_telemetry_format_completeness[txt=ollama/llama3.2:3b-instruct-fp16]",
|
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"request": {
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"headers": {},
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"body": {
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"model": "llama3.2:3b-instruct-fp16",
|
||||
"messages": [
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||||
{
|
||||
"role": "user",
|
||||
"content": "Test trace openai with temperature 0.7"
|
||||
}
|
||||
],
|
||||
"max_tokens": 100,
|
||||
"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-1fcfd86d8111",
|
||||
"choices": [
|
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{
|
||||
"finish_reason": "length",
|
||||
"index": 0,
|
||||
"logprobs": null,
|
||||
"message": {
|
||||
"content": "import torch\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\n\n# Load the pre-trained model and tokenizer\nmodel_name = \"CompVis/transformers-base-uncased\"\nmodel = AutoModelForCausalLM.from_pretrained(model_name)\ntokenizer = AutoTokenizer.from_pretrained(model_name)\n\n# Set the temperature to 0.7\ntemperature = 0.7\n\n# Define a function to generate text\ndef generate_text(prompt, max_length=100):\n input",
|
||||
"refusal": null,
|
||||
"role": "assistant",
|
||||
"annotations": null,
|
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"audio": null,
|
||||
"function_call": null,
|
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"tool_calls": null
|
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}
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"created": 0,
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"model": "llama3.2:3b-instruct-fp16",
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|
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"service_tier": null,
|
||||
"system_fingerprint": "fp_ollama",
|
||||
"usage": {
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"completion_tokens": 100,
|
||||
"prompt_tokens": 35,
|
||||
"total_tokens": 135,
|
||||
"completion_tokens_details": null,
|
||||
"prompt_tokens_details": null
|
||||
}
|
||||
}
|
||||
},
|
||||
"is_streaming": false
|
||||
}
|
||||
}
|
||||
4211
tests/integration/telemetry/recordings/d45c9a9229e7e3f50a6eac139508babe21988649eb321b562f74061f58593c25.json
generated
Normal file
4211
tests/integration/telemetry/recordings/d45c9a9229e7e3f50a6eac139508babe21988649eb321b562f74061f58593c25.json
generated
Normal file
File diff suppressed because it is too large
Load diff
4263
tests/integration/telemetry/recordings/db8ffad4840512348c215005128557807ffbed0cf6bf11a52c1d1009878886ef.json
generated
Normal file
4263
tests/integration/telemetry/recordings/db8ffad4840512348c215005128557807ffbed0cf6bf11a52c1d1009878886ef.json
generated
Normal file
File diff suppressed because it is too large
Load diff
|
|
@ -0,0 +1,59 @@
|
|||
{
|
||||
"test_id": "tests/integration/telemetry/test_completions.py::test_telemetry_format_completeness[txt=llama3.2:3b-instruct-fp16]",
|
||||
"request": {
|
||||
"method": "POST",
|
||||
"url": "http://localhost:11434/v1/v1/chat/completions",
|
||||
"headers": {},
|
||||
"body": {
|
||||
"model": "llama3.2:3b-instruct-fp16",
|
||||
"messages": [
|
||||
{
|
||||
"role": "user",
|
||||
"content": "Test trace openai with temperature 0.7"
|
||||
}
|
||||
],
|
||||
"max_tokens": 100,
|
||||
"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
|
||||
}
|
||||
}
|
||||
112
tests/integration/telemetry/test_completions.py
Normal file
112
tests/integration/telemetry/test_completions.py
Normal file
|
|
@ -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"
|
||||
"""
|
||||
Loading…
Add table
Add a link
Reference in a new issue