mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-12-04 10:10:36 +00:00
111 lines
4.1 KiB
Python
111 lines
4.1 KiB
Python
# 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 across stack modes."""
|
|
|
|
import json
|
|
|
|
|
|
def test_streaming_chunk_count(mock_otlp_collector, llama_stack_client, text_model_id):
|
|
"""Verify streaming adds chunk_count and __type__=async_generator."""
|
|
mock_otlp_collector.clear()
|
|
|
|
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(expected_count=5)
|
|
assert len(spans) > 0
|
|
|
|
async_generator_span = next(
|
|
(s for s in spans if s.attributes.get("__type__") == "async_generator" and s.attributes.get("chunk_count")),
|
|
None,
|
|
)
|
|
|
|
assert async_generator_span is not None
|
|
|
|
raw_chunk_count = async_generator_span.attributes.get("chunk_count")
|
|
assert raw_chunk_count is not None
|
|
chunk_count = int(raw_chunk_count)
|
|
|
|
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."""
|
|
mock_otlp_collector.clear()
|
|
|
|
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(expected_count=7)
|
|
spans = [span for span in spans if span.attributes.get("__root__") or span.attributes.get("__autotraced__")]
|
|
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:
|
|
assert attrs.get("__location__") in ["library_client", "server"]
|
|
continue
|
|
|
|
assert attrs.get("__autotraced__")
|
|
assert attrs.get("__class__") and attrs.get("__method__")
|
|
assert attrs.get("__type__") in ["async", "sync", "async_generator"]
|
|
|
|
args_field = attrs.get("__args__")
|
|
if args_field:
|
|
args = json.loads(args_field)
|
|
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"
|
|
"""
|