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Merge branch 'main' into remove-batch-inference

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Matthew Farrellee 2025-09-26 11:25:56 -04:00
commit 32b87bf88a
748 changed files with 127607 additions and 50032 deletions
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25
llama_stack/core/routers/inference.py
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@ -61,7 +61,7 @@ from llama_stack.models.llama.llama3.chat_format import ChatFormat
from llama_stack.models.llama.llama3.tokenizer import Tokenizer
from llama_stack.providers.datatypes import HealthResponse, HealthStatus, RoutingTable
from llama_stack.providers.utils.inference.inference_store import InferenceStore
from llama_stack.providers.utils.telemetry.tracing import get_current_span
from llama_stack.providers.utils.telemetry.tracing import enqueue_event, get_current_span
logger = get_logger(name=__name__, category="core::routers")
@ -88,6 +88,11 @@ class InferenceRouter(Inference):
async def shutdown(self) -> None:
logger.debug("InferenceRouter.shutdown")
if self.store:
try:
await self.store.shutdown()
except Exception as e:
logger.warning(f"Error during InferenceStore shutdown: {e}")
async def register_model(
self,
@ -158,7 +163,7 @@ class InferenceRouter(Inference):
metrics = self._construct_metrics(prompt_tokens, completion_tokens, total_tokens, model)
if self.telemetry:
for metric in metrics:
await self.telemetry.log_event(metric)
enqueue_event(metric)
return [MetricInResponse(metric=metric.metric, value=metric.value) for metric in metrics]
async def _count_tokens(
@ -391,7 +396,7 @@ class InferenceRouter(Inference):
model=model_obj,
)
for metric in metrics:
await self.telemetry.log_event(metric)
enqueue_event(metric)
# these metrics will show up in the client response.
response.metrics = (
@ -487,7 +492,7 @@ class InferenceRouter(Inference):
# Store the response with the ID that will be returned to the client
if self.store:
await self.store.store_chat_completion(response, messages)
asyncio.create_task(self.store.store_chat_completion(response, messages))
if self.telemetry:
metrics = self._construct_metrics(
@ -497,7 +502,7 @@ class InferenceRouter(Inference):
model=model_obj,
)
for metric in metrics:
await self.telemetry.log_event(metric)
enqueue_event(metric)
# these metrics will show up in the client response.
response.metrics = (
metrics if not hasattr(response, "metrics") or response.metrics is None else response.metrics + metrics
@ -624,7 +629,7 @@ class InferenceRouter(Inference):
"completion_tokens",
"total_tokens",
]: # Only log completion and total tokens
await self.telemetry.log_event(metric)
enqueue_event(metric)
# Return metrics in response
async_metrics = [
@ -670,7 +675,7 @@ class InferenceRouter(Inference):
)
for metric in completion_metrics:
if metric.metric in ["completion_tokens", "total_tokens"]: # Only log completion and total tokens
await self.telemetry.log_event(metric)
enqueue_event(metric)
# Return metrics in response
return [MetricInResponse(metric=metric.metric, value=metric.value) for metric in completion_metrics]
@ -715,7 +720,7 @@ class InferenceRouter(Inference):
choices_data[idx] = {
"content_parts": [],
"tool_calls_builder": {},
"finish_reason": None,
"finish_reason": "stop",
"logprobs_content_parts": [],
}
current_choice_data = choices_data[idx]
@ -766,7 +771,7 @@ class InferenceRouter(Inference):
model=model,
)
for metric in metrics:
await self.telemetry.log_event(metric)
enqueue_event(metric)
yield chunk
finally:
@ -815,4 +820,4 @@ class InferenceRouter(Inference):
object="chat.completion",
)
logger.debug(f"InferenceRouter.completion_response: {final_response}")
await self.store.store_chat_completion(final_response, messages)
asyncio.create_task(self.store.store_chat_completion(final_response, messages))