[Misc] Remove experimental dep from tracing.py (#12007)

Signed-off-by: Adrian Cole <[email protected]>

tests/tracing/test_tracing.py

@@ -100,32 +100,32 @@ def test_traces(trace_service):
 
     attributes = decode_attributes(
         request.resource_spans[0].scope_spans[0].spans[0].attributes)
-    assert attributes.get(SpanAttributes.LLM_RESPONSE_MODEL) == model
+    assert attributes.get(SpanAttributes.GEN_AI_RESPONSE_MODEL) == model
     assert attributes.get(
-        SpanAttributes.LLM_REQUEST_ID) == outputs[0].request_id
+        SpanAttributes.GEN_AI_REQUEST_ID) == outputs[0].request_id
+    assert attributes.get(SpanAttributes.GEN_AI_REQUEST_TEMPERATURE
+                          ) == sampling_params.temperature
     assert attributes.get(
-        SpanAttributes.LLM_REQUEST_TEMPERATURE) == sampling_params.temperature
+        SpanAttributes.GEN_AI_REQUEST_TOP_P) == sampling_params.top_p
     assert attributes.get(
-        SpanAttributes.LLM_REQUEST_TOP_P) == sampling_params.top_p
-    assert attributes.get(
-        SpanAttributes.LLM_REQUEST_MAX_TOKENS) == sampling_params.max_tokens
-    assert attributes.get(SpanAttributes.LLM_REQUEST_N) == sampling_params.n
-    assert attributes.get(SpanAttributes.LLM_USAGE_PROMPT_TOKENS) == len(
+        SpanAttributes.GEN_AI_REQUEST_MAX_TOKENS) == sampling_params.max_tokens
+    assert attributes.get(SpanAttributes.GEN_AI_REQUEST_N) == sampling_params.n
+    assert attributes.get(SpanAttributes.GEN_AI_USAGE_PROMPT_TOKENS) == len(
         outputs[0].prompt_token_ids)
     completion_tokens = sum(len(o.token_ids) for o in outputs[0].outputs)
     assert attributes.get(
-        SpanAttributes.LLM_USAGE_COMPLETION_TOKENS) == completion_tokens
+        SpanAttributes.GEN_AI_USAGE_COMPLETION_TOKENS) == completion_tokens
     metrics = outputs[0].metrics
     assert attributes.get(
-        SpanAttributes.LLM_LATENCY_TIME_IN_QUEUE) == metrics.time_in_queue
+        SpanAttributes.GEN_AI_LATENCY_TIME_IN_QUEUE) == metrics.time_in_queue
     ttft = metrics.first_token_time - metrics.arrival_time
     assert attributes.get(
-        SpanAttributes.LLM_LATENCY_TIME_TO_FIRST_TOKEN) == ttft
+        SpanAttributes.GEN_AI_LATENCY_TIME_TO_FIRST_TOKEN) == ttft
     e2e_time = metrics.finished_time - metrics.arrival_time
-    assert attributes.get(SpanAttributes.LLM_LATENCY_E2E) == e2e_time
+    assert attributes.get(SpanAttributes.GEN_AI_LATENCY_E2E) == e2e_time
     assert metrics.scheduler_time > 0
-    assert attributes.get(
-        SpanAttributes.LLM_LATENCY_TIME_IN_SCHEDULER) == metrics.scheduler_time
+    assert attributes.get(SpanAttributes.GEN_AI_LATENCY_TIME_IN_SCHEDULER
+                          ) == metrics.scheduler_time
     # Model forward and model execute should be none, since detailed traces is
     # not enabled.
     assert metrics.model_forward_time is None
@@ -166,37 +166,37 @@ def test_traces_with_detailed_steps(trace_service):
 
     attributes = decode_attributes(
         request.resource_spans[0].scope_spans[0].spans[0].attributes)
-    assert attributes.get(SpanAttributes.LLM_RESPONSE_MODEL) == model
+    assert attributes.get(SpanAttributes.GEN_AI_RESPONSE_MODEL) == model
     assert attributes.get(
-        SpanAttributes.LLM_REQUEST_ID) == outputs[0].request_id
+        SpanAttributes.GEN_AI_REQUEST_ID) == outputs[0].request_id
+    assert attributes.get(SpanAttributes.GEN_AI_REQUEST_TEMPERATURE
+                          ) == sampling_params.temperature
     assert attributes.get(
-        SpanAttributes.LLM_REQUEST_TEMPERATURE) == sampling_params.temperature
+        SpanAttributes.GEN_AI_REQUEST_TOP_P) == sampling_params.top_p
     assert attributes.get(
-        SpanAttributes.LLM_REQUEST_TOP_P) == sampling_params.top_p
-    assert attributes.get(
-        SpanAttributes.LLM_REQUEST_MAX_TOKENS) == sampling_params.max_tokens
-    assert attributes.get(SpanAttributes.LLM_REQUEST_N) == sampling_params.n
-    assert attributes.get(SpanAttributes.LLM_USAGE_PROMPT_TOKENS) == len(
+        SpanAttributes.GEN_AI_REQUEST_MAX_TOKENS) == sampling_params.max_tokens
+    assert attributes.get(SpanAttributes.GEN_AI_REQUEST_N) == sampling_params.n
+    assert attributes.get(SpanAttributes.GEN_AI_USAGE_PROMPT_TOKENS) == len(
         outputs[0].prompt_token_ids)
     completion_tokens = sum(len(o.token_ids) for o in outputs[0].outputs)
     assert attributes.get(
-        SpanAttributes.LLM_USAGE_COMPLETION_TOKENS) == completion_tokens
+        SpanAttributes.GEN_AI_USAGE_COMPLETION_TOKENS) == completion_tokens
     metrics = outputs[0].metrics
     assert attributes.get(
-        SpanAttributes.LLM_LATENCY_TIME_IN_QUEUE) == metrics.time_in_queue
+        SpanAttributes.GEN_AI_LATENCY_TIME_IN_QUEUE) == metrics.time_in_queue
     ttft = metrics.first_token_time - metrics.arrival_time
     assert attributes.get(
-        SpanAttributes.LLM_LATENCY_TIME_TO_FIRST_TOKEN) == ttft
+        SpanAttributes.GEN_AI_LATENCY_TIME_TO_FIRST_TOKEN) == ttft
     e2e_time = metrics.finished_time - metrics.arrival_time
-    assert attributes.get(SpanAttributes.LLM_LATENCY_E2E) == e2e_time
+    assert attributes.get(SpanAttributes.GEN_AI_LATENCY_E2E) == e2e_time
     assert metrics.scheduler_time > 0
-    assert attributes.get(
-        SpanAttributes.LLM_LATENCY_TIME_IN_SCHEDULER) == metrics.scheduler_time
+    assert attributes.get(SpanAttributes.GEN_AI_LATENCY_TIME_IN_SCHEDULER
+                          ) == metrics.scheduler_time
     assert metrics.model_forward_time > 0
     assert attributes.get(
-        SpanAttributes.LLM_LATENCY_TIME_IN_MODEL_FORWARD) == pytest.approx(
+        SpanAttributes.GEN_AI_LATENCY_TIME_IN_MODEL_FORWARD) == pytest.approx(
             metrics.model_forward_time / 1000)
     assert metrics.model_execute_time > 0
-    assert attributes.get(SpanAttributes.LLM_LATENCY_TIME_IN_MODEL_EXECUTE
+    assert attributes.get(SpanAttributes.GEN_AI_LATENCY_TIME_IN_MODEL_EXECUTE
                           ) == metrics.model_execute_time
     assert metrics.model_forward_time < 1000 * metrics.model_execute_time

vllm/engine/llm_engine.py

@@ -1857,46 +1857,44 @@ def create_trace_span(self, seq_group: SequenceGroup) -> None:
             metrics = seq_group.metrics
             ttft = metrics.first_token_time - metrics.arrival_time
             e2e_time = metrics.finished_time - metrics.arrival_time
-            # attribute names are based on
-            # https://github.com/open-telemetry/semantic-conventions/blob/main/docs/gen-ai/llm-spans.md
-            seq_span.set_attribute(SpanAttributes.LLM_RESPONSE_MODEL,
+            seq_span.set_attribute(SpanAttributes.GEN_AI_RESPONSE_MODEL,
                                    self.model_config.model)
-            seq_span.set_attribute(SpanAttributes.LLM_REQUEST_ID,
+            seq_span.set_attribute(SpanAttributes.GEN_AI_REQUEST_ID,
                                    seq_group.request_id)
-            seq_span.set_attribute(SpanAttributes.LLM_REQUEST_TEMPERATURE,
+            seq_span.set_attribute(SpanAttributes.GEN_AI_REQUEST_TEMPERATURE,
                                    seq_group.sampling_params.temperature)
-            seq_span.set_attribute(SpanAttributes.LLM_REQUEST_TOP_P,
+            seq_span.set_attribute(SpanAttributes.GEN_AI_REQUEST_TOP_P,
                                    seq_group.sampling_params.top_p)
-            seq_span.set_attribute(SpanAttributes.LLM_REQUEST_MAX_TOKENS,
+            seq_span.set_attribute(SpanAttributes.GEN_AI_REQUEST_MAX_TOKENS,
                                    seq_group.sampling_params.max_tokens)
-            seq_span.set_attribute(SpanAttributes.LLM_REQUEST_N,
+            seq_span.set_attribute(SpanAttributes.GEN_AI_REQUEST_N,
                                    seq_group.sampling_params.n)
-            seq_span.set_attribute(SpanAttributes.LLM_USAGE_NUM_SEQUENCES,
+            seq_span.set_attribute(SpanAttributes.GEN_AI_USAGE_NUM_SEQUENCES,
                                    seq_group.num_seqs())
-            seq_span.set_attribute(SpanAttributes.LLM_USAGE_PROMPT_TOKENS,
+            seq_span.set_attribute(SpanAttributes.GEN_AI_USAGE_PROMPT_TOKENS,
                                    len(seq_group.prompt_token_ids))
             seq_span.set_attribute(
-                SpanAttributes.LLM_USAGE_COMPLETION_TOKENS,
+                SpanAttributes.GEN_AI_USAGE_COMPLETION_TOKENS,
                 sum([
                     seq.get_output_len()
                     for seq in seq_group.get_finished_seqs()
                 ]))
-            seq_span.set_attribute(SpanAttributes.LLM_LATENCY_TIME_IN_QUEUE,
+            seq_span.set_attribute(SpanAttributes.GEN_AI_LATENCY_TIME_IN_QUEUE,
                                    metrics.time_in_queue)
             seq_span.set_attribute(
-                SpanAttributes.LLM_LATENCY_TIME_TO_FIRST_TOKEN, ttft)
-            seq_span.set_attribute(SpanAttributes.LLM_LATENCY_E2E, e2e_time)
+                SpanAttributes.GEN_AI_LATENCY_TIME_TO_FIRST_TOKEN, ttft)
+            seq_span.set_attribute(SpanAttributes.GEN_AI_LATENCY_E2E, e2e_time)
             if metrics.scheduler_time is not None:
                 seq_span.set_attribute(
-                    SpanAttributes.LLM_LATENCY_TIME_IN_SCHEDULER,
+                    SpanAttributes.GEN_AI_LATENCY_TIME_IN_SCHEDULER,
                     metrics.scheduler_time)
             if metrics.model_forward_time is not None:
                 seq_span.set_attribute(
-                    SpanAttributes.LLM_LATENCY_TIME_IN_MODEL_FORWARD,
+                    SpanAttributes.GEN_AI_LATENCY_TIME_IN_MODEL_FORWARD,
                     metrics.model_forward_time / 1000.0)
             if metrics.model_execute_time is not None:
                 seq_span.set_attribute(
-                    SpanAttributes.LLM_LATENCY_TIME_IN_MODEL_EXECUTE,
+                    SpanAttributes.GEN_AI_LATENCY_TIME_IN_MODEL_EXECUTE,
                     metrics.model_execute_time)
 
     def _validate_model_inputs(self, inputs: ProcessorInputs,

vllm/tracing.py

@@ -16,7 +16,6 @@
         OTEL_EXPORTER_OTLP_TRACES_PROTOCOL)
     from opentelemetry.sdk.trace import TracerProvider
     from opentelemetry.sdk.trace.export import BatchSpanProcessor
-    from opentelemetry.semconv_ai import SpanAttributes as BaseSpanAttributes
     from opentelemetry.trace import SpanKind, Tracer, set_tracer_provider
     from opentelemetry.trace.propagation.tracecontext import (
         TraceContextTextMapPropagator)
@@ -92,21 +91,30 @@ def extract_trace_headers(headers: Mapping[str, str]) -> Mapping[str, str]:
     return {h: headers[h] for h in TRACE_HEADERS if h in headers}
 
 
-class SpanAttributes(BaseSpanAttributes):
-    # The following span attribute names are added here because they are missing
-    # from the Semantic Conventions for LLM.
-    LLM_REQUEST_ID = "gen_ai.request.id"
-    LLM_REQUEST_N = "gen_ai.request.n"
-    LLM_USAGE_NUM_SEQUENCES = "gen_ai.usage.num_sequences"
-    LLM_LATENCY_TIME_IN_QUEUE = "gen_ai.latency.time_in_queue"
-    LLM_LATENCY_TIME_TO_FIRST_TOKEN = "gen_ai.latency.time_to_first_token"
-    LLM_LATENCY_E2E = "gen_ai.latency.e2e"
-    LLM_LATENCY_TIME_IN_SCHEDULER = "gen_ai.latency.time_in_scheduler"
+class SpanAttributes:
+    # Attribute names copied from here to avoid version conflicts:
+    # https://github.com/open-telemetry/semantic-conventions/blob/main/docs/gen-ai/gen-ai-spans.md
+    GEN_AI_USAGE_COMPLETION_TOKENS = "gen_ai.usage.completion_tokens"
+    GEN_AI_USAGE_PROMPT_TOKENS = "gen_ai.usage.prompt_tokens"
+    GEN_AI_REQUEST_MAX_TOKENS = "gen_ai.request.max_tokens"
+    GEN_AI_REQUEST_TOP_P = "gen_ai.request.top_p"
+    GEN_AI_REQUEST_TEMPERATURE = "gen_ai.request.temperature"
+    GEN_AI_RESPONSE_MODEL = "gen_ai.response.model"
+    # Attribute names added until they are added to the semantic conventions:
+    GEN_AI_REQUEST_ID = "gen_ai.request.id"
+    GEN_AI_REQUEST_N = "gen_ai.request.n"
+    GEN_AI_USAGE_NUM_SEQUENCES = "gen_ai.usage.num_sequences"
+    GEN_AI_LATENCY_TIME_IN_QUEUE = "gen_ai.latency.time_in_queue"
+    GEN_AI_LATENCY_TIME_TO_FIRST_TOKEN = "gen_ai.latency.time_to_first_token"
+    GEN_AI_LATENCY_E2E = "gen_ai.latency.e2e"
+    GEN_AI_LATENCY_TIME_IN_SCHEDULER = "gen_ai.latency.time_in_scheduler"
     # Time taken in the forward pass for this across all workers
-    LLM_LATENCY_TIME_IN_MODEL_FORWARD = "gen_ai.latency.time_in_model_forward"
+    GEN_AI_LATENCY_TIME_IN_MODEL_FORWARD = (
+        "gen_ai.latency.time_in_model_forward")
     # Time taken in the model execute function. This will include model
     # forward, block/sync across workers, cpu-gpu sync time and sampling time.
-    LLM_LATENCY_TIME_IN_MODEL_EXECUTE = "gen_ai.latency.time_in_model_execute"
+    GEN_AI_LATENCY_TIME_IN_MODEL_EXECUTE = (
+        "gen_ai.latency.time_in_model_execute")
 
 
 def contains_trace_headers(headers: Mapping[str, str]) -> bool: