e489ad7a21
- **Add SPDX license headers to python source files**
- **Check for SPDX headers using pre-commit**
commit 9d7ef44c3cfb72ca4c32e1c677d99259d10d4745
Author: Russell Bryant <rbryant@redhat.com>
Date: Fri Jan 31 14:18:24 2025 -0500
Add SPDX license headers to python source files
This commit adds SPDX license headers to python source files as
recommended to
the project by the Linux Foundation. These headers provide a concise way
that is
both human and machine readable for communicating license information
for each
source file. It helps avoid any ambiguity about the license of the code
and can
also be easily used by tools to help manage license compliance.
The Linux Foundation runs license scans against the codebase to help
ensure
we are in compliance with the licenses of the code we use, including
dependencies. Having these headers in place helps that tool do its job.
More information can be found on the SPDX site:
- https://spdx.dev/learn/handling-license-info/
Signed-off-by: Russell Bryant <rbryant@redhat.com>
commit 5a1cf1cb3b80759131c73f6a9dddebccac039dea
Author: Russell Bryant <rbryant@redhat.com>
Date: Fri Jan 31 14:36:32 2025 -0500
Check for SPDX headers using pre-commit
Signed-off-by: Russell Bryant <rbryant@redhat.com>
---------
Signed-off-by: Russell Bryant <rbryant@redhat.com>
205 lines
8.2 KiB
Python
205 lines
8.2 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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import os
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import threading
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from concurrent import futures
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from typing import Callable, Dict, Iterable, Literal
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import grpc
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import pytest
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from opentelemetry.proto.collector.trace.v1.trace_service_pb2 import (
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ExportTraceServiceResponse)
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from opentelemetry.proto.collector.trace.v1.trace_service_pb2_grpc import (
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TraceServiceServicer, add_TraceServiceServicer_to_server)
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from opentelemetry.proto.common.v1.common_pb2 import AnyValue, KeyValue
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from opentelemetry.sdk.environment_variables import (
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OTEL_EXPORTER_OTLP_TRACES_INSECURE)
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from vllm import LLM, SamplingParams
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from vllm.tracing import SpanAttributes
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FAKE_TRACE_SERVER_ADDRESS = "localhost:4317"
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FieldName = Literal['bool_value', 'string_value', 'int_value', 'double_value',
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'array_value']
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def decode_value(value: AnyValue):
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field_decoders: Dict[FieldName, Callable] = {
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"bool_value": (lambda v: v.bool_value),
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"string_value": (lambda v: v.string_value),
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"int_value": (lambda v: v.int_value),
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"double_value": (lambda v: v.double_value),
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"array_value":
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(lambda v: [decode_value(item) for item in v.array_value.values]),
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}
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for field, decoder in field_decoders.items():
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if value.HasField(field):
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return decoder(value)
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raise ValueError(f"Couldn't decode value: {value}")
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def decode_attributes(attributes: Iterable[KeyValue]):
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return {kv.key: decode_value(kv.value) for kv in attributes}
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class FakeTraceService(TraceServiceServicer):
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def __init__(self):
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self.request = None
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self.evt = threading.Event()
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def Export(self, request, context):
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self.request = request
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self.evt.set()
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return ExportTraceServiceResponse()
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@pytest.fixture
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def trace_service():
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"""Fixture to set up a fake gRPC trace service"""
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server = grpc.server(futures.ThreadPoolExecutor(max_workers=1))
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service = FakeTraceService()
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add_TraceServiceServicer_to_server(service, server)
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server.add_insecure_port(FAKE_TRACE_SERVER_ADDRESS)
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server.start()
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yield service
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server.stop(None)
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def test_traces(trace_service):
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os.environ[OTEL_EXPORTER_OTLP_TRACES_INSECURE] = "true"
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sampling_params = SamplingParams(temperature=0.01,
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top_p=0.1,
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max_tokens=256)
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model = "facebook/opt-125m"
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llm = LLM(
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model=model,
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otlp_traces_endpoint=FAKE_TRACE_SERVER_ADDRESS,
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)
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prompts = ["This is a short prompt"]
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outputs = llm.generate(prompts, sampling_params=sampling_params)
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timeout = 5
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if not trace_service.evt.wait(timeout):
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raise TimeoutError(
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f"The fake trace service didn't receive a trace within "
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f"the {timeout} seconds timeout")
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request = trace_service.request
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assert len(request.resource_spans) == 1, (
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f"Expected 1 resource span, "
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f"but got {len(request.resource_spans)}")
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assert len(request.resource_spans[0].scope_spans) == 1, (
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f"Expected 1 scope span, "
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f"but got {len(request.resource_spans[0].scope_spans)}")
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assert len(request.resource_spans[0].scope_spans[0].spans) == 1, (
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f"Expected 1 span, "
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f"but got {len(request.resource_spans[0].scope_spans[0].spans)}")
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attributes = decode_attributes(
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request.resource_spans[0].scope_spans[0].spans[0].attributes)
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assert attributes.get(SpanAttributes.GEN_AI_RESPONSE_MODEL) == model
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assert attributes.get(
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SpanAttributes.GEN_AI_REQUEST_ID) == outputs[0].request_id
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assert attributes.get(SpanAttributes.GEN_AI_REQUEST_TEMPERATURE
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) == sampling_params.temperature
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assert attributes.get(
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SpanAttributes.GEN_AI_REQUEST_TOP_P) == sampling_params.top_p
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assert attributes.get(
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SpanAttributes.GEN_AI_REQUEST_MAX_TOKENS) == sampling_params.max_tokens
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assert attributes.get(SpanAttributes.GEN_AI_REQUEST_N) == sampling_params.n
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assert attributes.get(SpanAttributes.GEN_AI_USAGE_PROMPT_TOKENS) == len(
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outputs[0].prompt_token_ids)
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completion_tokens = sum(len(o.token_ids) for o in outputs[0].outputs)
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assert attributes.get(
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SpanAttributes.GEN_AI_USAGE_COMPLETION_TOKENS) == completion_tokens
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metrics = outputs[0].metrics
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assert attributes.get(
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SpanAttributes.GEN_AI_LATENCY_TIME_IN_QUEUE) == metrics.time_in_queue
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ttft = metrics.first_token_time - metrics.arrival_time
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assert attributes.get(
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SpanAttributes.GEN_AI_LATENCY_TIME_TO_FIRST_TOKEN) == ttft
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e2e_time = metrics.finished_time - metrics.arrival_time
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assert attributes.get(SpanAttributes.GEN_AI_LATENCY_E2E) == e2e_time
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assert metrics.scheduler_time > 0
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assert attributes.get(SpanAttributes.GEN_AI_LATENCY_TIME_IN_SCHEDULER
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) == metrics.scheduler_time
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# Model forward and model execute should be none, since detailed traces is
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# not enabled.
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assert metrics.model_forward_time is None
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assert metrics.model_execute_time is None
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def test_traces_with_detailed_steps(trace_service):
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os.environ[OTEL_EXPORTER_OTLP_TRACES_INSECURE] = "true"
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sampling_params = SamplingParams(temperature=0.01,
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top_p=0.1,
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max_tokens=256)
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model = "facebook/opt-125m"
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llm = LLM(
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model=model,
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otlp_traces_endpoint=FAKE_TRACE_SERVER_ADDRESS,
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collect_detailed_traces="all",
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)
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prompts = ["This is a short prompt"]
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outputs = llm.generate(prompts, sampling_params=sampling_params)
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timeout = 5
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if not trace_service.evt.wait(timeout):
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raise TimeoutError(
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f"The fake trace service didn't receive a trace within "
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f"the {timeout} seconds timeout")
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request = trace_service.request
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assert len(request.resource_spans) == 1, (
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f"Expected 1 resource span, "
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f"but got {len(request.resource_spans)}")
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assert len(request.resource_spans[0].scope_spans) == 1, (
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f"Expected 1 scope span, "
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f"but got {len(request.resource_spans[0].scope_spans)}")
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assert len(request.resource_spans[0].scope_spans[0].spans) == 1, (
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f"Expected 1 span, "
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f"but got {len(request.resource_spans[0].scope_spans[0].spans)}")
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attributes = decode_attributes(
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request.resource_spans[0].scope_spans[0].spans[0].attributes)
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assert attributes.get(SpanAttributes.GEN_AI_RESPONSE_MODEL) == model
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assert attributes.get(
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SpanAttributes.GEN_AI_REQUEST_ID) == outputs[0].request_id
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assert attributes.get(SpanAttributes.GEN_AI_REQUEST_TEMPERATURE
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) == sampling_params.temperature
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assert attributes.get(
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SpanAttributes.GEN_AI_REQUEST_TOP_P) == sampling_params.top_p
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assert attributes.get(
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SpanAttributes.GEN_AI_REQUEST_MAX_TOKENS) == sampling_params.max_tokens
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assert attributes.get(SpanAttributes.GEN_AI_REQUEST_N) == sampling_params.n
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assert attributes.get(SpanAttributes.GEN_AI_USAGE_PROMPT_TOKENS) == len(
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outputs[0].prompt_token_ids)
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completion_tokens = sum(len(o.token_ids) for o in outputs[0].outputs)
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assert attributes.get(
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SpanAttributes.GEN_AI_USAGE_COMPLETION_TOKENS) == completion_tokens
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metrics = outputs[0].metrics
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assert attributes.get(
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SpanAttributes.GEN_AI_LATENCY_TIME_IN_QUEUE) == metrics.time_in_queue
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ttft = metrics.first_token_time - metrics.arrival_time
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assert attributes.get(
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SpanAttributes.GEN_AI_LATENCY_TIME_TO_FIRST_TOKEN) == ttft
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e2e_time = metrics.finished_time - metrics.arrival_time
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assert attributes.get(SpanAttributes.GEN_AI_LATENCY_E2E) == e2e_time
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assert metrics.scheduler_time > 0
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assert attributes.get(SpanAttributes.GEN_AI_LATENCY_TIME_IN_SCHEDULER
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) == metrics.scheduler_time
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assert metrics.model_forward_time > 0
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assert attributes.get(
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SpanAttributes.GEN_AI_LATENCY_TIME_IN_MODEL_FORWARD) == pytest.approx(
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metrics.model_forward_time / 1000)
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assert metrics.model_execute_time > 0
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assert attributes.get(SpanAttributes.GEN_AI_LATENCY_TIME_IN_MODEL_EXECUTE
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) == metrics.model_execute_time
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assert metrics.model_forward_time < 1000 * metrics.model_execute_time
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