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https://github.com/BerriAI/litellm.git
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feat(hosted_vllm): keep stop_reason and token_ids on streamed chunks and proxy stream output
Streamed chat finish chunks and streamed /v1/completions finish chunks now carry vLLM's stop_reason. Each stream chunk keeps the token_ids vLLM sent with it, including held-back empty chunks, and the proxy no longer strips those chunks as empty usage chunks or drops the fields in its fast serializer.
This commit is contained in:
mateo-berri
parent
40a5a57d0a
commit
97c6616945
8 changed files with 188 additions and 4 deletions
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@ -685,6 +685,8 @@ STREAM_SSE_DONE_STRING: Final[str] = "[DONE]"
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STREAM_SSE_DATA_PREFIX: Final[str] = "data: "
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STREAM_SSE_KEEPALIVE_PING_CHUNK: Final[str] = 'event: ping\ndata: {"type": "ping"}\n\n'
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STREAM_SSE_KEEPALIVE_PING_BYTES: Final[bytes] = STREAM_SSE_KEEPALIVE_PING_CHUNK.encode("utf-8")
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STREAM_FINISH_CHOICE_FIELDS: Final = ("stop_reason",)
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STREAM_CHUNK_CHOICE_FIELDS: Final = ("token_ids",)
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### SPEND TRACKING ###
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DEFAULT_REPLICATE_GPU_PRICE_PER_SECOND: Final = get_env_float(
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"DEFAULT_REPLICATE_GPU_PRICE_PER_SECOND", 0.001400
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@ -23,6 +23,7 @@ from typing_extensions import NotRequired, TypedDict
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import litellm
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from litellm import verbose_logger
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from litellm._uuid import uuid
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from litellm.constants import STREAM_CHUNK_CHOICE_FIELDS, STREAM_FINISH_CHOICE_FIELDS
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from litellm.litellm_core_utils.asyncify import asyncify
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from litellm.litellm_core_utils.core_helpers import chunk_usage, provider_reported_usage_cost
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from litellm.litellm_core_utils.hidden_params import (
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@ -113,6 +114,29 @@ def _prepend_upstream_choice_extras(
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choice.__pydantic_fields_set__.update(upstream_extras)
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_NO_UPSTREAM_CHOICE_FIELDS: Final[Mapping[str, object]] = MappingProxyType({})
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def _upstream_choice_fields(upstream_chunk: object, fields: Sequence[str]) -> Mapping[str, object]:
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match getattr(upstream_chunk, "choices", None):
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case [BaseModel() as first_choice, *_]:
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upstream_extras: Final[Mapping[str, object]] = first_choice.model_extra or {}
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return MappingProxyType({field: upstream_extras[field] for field in fields if field in upstream_extras})
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case _:
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return _NO_UPSTREAM_CHOICE_FIELDS
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def _non_null_upstream_choice_fields(upstream_chunk: object, fields: Sequence[str]) -> Mapping[str, object]:
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return MappingProxyType(
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{field: value for field, value in _upstream_choice_fields(upstream_chunk, fields).items() if value is not None}
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)
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def _apply_choice_fields(choice: StreamingChoices, fields: Mapping[str, object]) -> None:
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for field, value in fields.items():
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setattr(choice, field, value)
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def _holds_only_scalars(fields: Iterable[tuple[str, object]]) -> bool:
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return all(value is None or isinstance(value, (str, int, float, bool)) for _, value in fields)
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@ -337,6 +361,7 @@ class CustomStreamWrapper:
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self.system_fingerprint: str | None = None
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self._provider_response_model: str | None = None
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self.received_finish_reason: str | None = None
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self.received_finish_choice_fields: Mapping[str, object] = _NO_UPSTREAM_CHOICE_FIELDS
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self.intermittent_finish_reason: str | None = None # finish reasons that show up mid-stream
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self.special_tokens = [
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"<|assistant|>",
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@ -1268,12 +1293,24 @@ class CustomStreamWrapper:
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model_response.choices[0].finish_reason = map_finish_reason(
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finish_reason=self.received_finish_reason
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) # ensure consistent output to openai
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_apply_choice_fields(model_response.choices[0], self.received_finish_choice_fields)
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if response_obj.get("is_finished"):
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_apply_choice_fields(
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model_response.choices[0],
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_non_null_upstream_choice_fields(_original_chunk, STREAM_CHUNK_CHOICE_FIELDS),
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)
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self.sent_last_chunk = True
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return model_response
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elif self._has_special_delta_content(model_response):
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return self._handle_special_delta_content(model_response)
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elif upstream_chunk_fields := _non_null_upstream_choice_fields(
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response_obj.get("original_chunk"), STREAM_CHUNK_CHOICE_FIELDS
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):
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model_response.choices[0].delta = Delta(content=None) # rebind-ok: chunks are built in place here
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_apply_choice_fields(model_response.choices[0], upstream_chunk_fields)
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return model_response
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else:
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if hasattr(model_response, "usage"):
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self.chunks.append(model_response)
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@ -1492,6 +1529,7 @@ class CustomStreamWrapper:
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print_verbose(f"completion obj content: {completion_obj['content']}")
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if response_obj["is_finished"]:
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self.received_finish_reason = response_obj["finish_reason"]
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self.received_finish_choice_fields = _upstream_choice_fields(chunk, STREAM_FINISH_CHOICE_FIELDS)
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if response_obj["usage"] is not None:
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_text_completion_usage: Final[Usage] = response_obj["usage"]
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setattr(
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@ -1575,6 +1613,7 @@ class CustomStreamWrapper:
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f"{self.custom_llm_provider} raised a streaming error - finish_reason: error, no content string given. Received Chunk={response_obj}"
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)
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self.received_finish_reason = response_obj["finish_reason"]
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self.received_finish_choice_fields = _upstream_choice_fields(chunk, STREAM_FINISH_CHOICE_FIELDS)
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if response_obj.get("original_chunk", None) is not None:
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if hasattr(response_obj["original_chunk"], "id"):
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model_response = self.set_model_id(response_obj["original_chunk"].id, model_response)
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@ -1920,6 +1959,7 @@ class CustomStreamWrapper:
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model_response.choices[0].finish_reason = _finish_reason
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else:
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model_response.choices[0].finish_reason = "stop"
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_apply_choice_fields(model_response.choices[0], self.received_finish_choice_fields)
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## if tool use
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if (
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@ -10061,6 +10061,9 @@ def _restamp_nested_response_model_in_sse_line(line: bytes, target_model: str) -
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return _SSE_DATA_FIELD + b" " + serialized_event.encode() + line_ending
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_STANDARD_STREAM_CHOICE_FIELDS: Final = frozenset({"finish_reason", "index", "delta", "logprobs", "enhancements"})
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def _fast_serialize_simple_model_response_stream(
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chunk: ModelResponseStream,
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) -> bytes | None:
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@ -10083,6 +10086,8 @@ def _fast_serialize_simple_model_response_stream(
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choice: Final = choices[0]
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if getattr(choice, "logprobs", None) is not None or getattr(choice, "enhancements", None) is not None:
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return None
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if not isinstance(choice, BaseModel) or (choice.model_extra or {}).keys() - _STANDARD_STREAM_CHOICE_FIELDS:
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return None
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delta: Final[object] = getattr(choice, "delta", None)
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if delta is None:
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@ -10161,6 +10166,11 @@ def _is_empty_streaming_choice(choice: StreamingChoices) -> bool:
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return False
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if getattr(choice, "logprobs", None) is not None:
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return False
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provider_choice_values: Final = (
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value for field, value in (choice.model_extra or {}).items() if field not in _STANDARD_STREAM_CHOICE_FIELDS
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)
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if not _all_values_none(provider_choice_values):
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return False
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delta: Final = choice.delta if hasattr(choice, "delta") else None
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if delta is None:
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return True
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@ -1,5 +1,6 @@
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from typing import Final
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from litellm.constants import STREAM_FINISH_CHOICE_FIELDS
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from litellm.types.utils import Choices, ModelResponse, TextChoices, TextCompletionResponse
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@ -34,11 +35,15 @@ class TextCompletionStreamWrapper:
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raise Exception
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if chunk["choices"]:
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delta: Final = chunk["choices"][0]["delta"]
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chat_choice: Final = chunk["choices"][0]
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delta: Final = chat_choice["delta"]
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text_choices["text"] = delta["content"]
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text_choices["reasoning_content"] = delta.get("reasoning_content")
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text_choices["index"] = chunk["choices"][0]["index"]
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text_choices["finish_reason"] = chunk["choices"][0]["finish_reason"]
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text_choices["index"] = chat_choice["index"]
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text_choices["finish_reason"] = chat_choice["finish_reason"]
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for field in STREAM_FINISH_CHOICE_FIELDS:
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if field in chat_choice:
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text_choices[field] = chat_choice[field]
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response["choices"] = [text_choices]
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else:
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response["choices"] = []
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@ -7,6 +7,7 @@ import threading
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import time
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from collections.abc import Mapping, Sequence
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from dataclasses import dataclass
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from itertools import chain
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from types import CodeType, FrameType, MappingProxyType
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from typing import AsyncIterator, Callable, Final, Iterator, Literal, NoReturn, Optional, cast
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from unittest.mock import AsyncMock, MagicMock, Mock, patch
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@ -28,6 +29,7 @@ from litellm.litellm_core_utils.streaming_handler import (
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_ProviderChunkParsed,
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)
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from litellm.llms.custom_httpx.http_handler import HTTPHandler
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from litellm.llms.hosted_vllm.chat.transformation import HostedVLLMChatConfig
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from litellm.responses.mcp.chat_completions_handler import _add_mcp_metadata_to_response
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from litellm.types.utils import (
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CompletionTokensDetailsWrapper,
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@ -5901,6 +5903,70 @@ def test_chunk_creator_keeps_upstream_index_on_single_choice_chunk(logging_obj:
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assert result.choices[0].delta.content == "hello"
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def _hosted_vllm_stream_choices(
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logging_obj: Logging, upstream_choices: Sequence[Mapping[str, object]]
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) -> tuple[dict[str, object], ...]:
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sse_lines: Final = (
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*(
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"data: " + json.dumps({"id": "chatcmpl-1", "object": "chat.completion.chunk", "model": "m", "choices": [c]})
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for c in upstream_choices
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),
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"data: [DONE]",
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)
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completion_stream: Final = HostedVLLMChatConfig().get_model_response_iterator(
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streaming_response=iter(sse_lines), sync_stream=True, json_mode=False
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)
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return tuple(
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chunk.choices[0].model_dump()
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for chunk in CustomStreamWrapper(
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completion_stream=completion_stream, model="m", custom_llm_provider="hosted_vllm", logging_obj=logging_obj
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)
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)
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@pytest.mark.parametrize("stop_reason", [",5", 151645])
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@pytest.mark.parametrize("final_content", [",3", ""], ids=["content-then-finish", "finish-only"])
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def test_hosted_vllm_stream_keeps_stop_reason_and_every_token_id(
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logging_obj: Logging, final_content: str, stop_reason: str | int
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) -> None:
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emitted: Final = _hosted_vllm_stream_choices(
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logging_obj,
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(
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{"index": 0, "delta": {"role": "assistant", "content": ""}, "finish_reason": None},
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{"index": 0, "delta": {"content": ""}, "finish_reason": None, "token_ids": [16]},
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{"index": 0, "delta": {"content": "1,2"}, "finish_reason": None, "token_ids": [11, 17]},
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{
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"index": 0,
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"delta": {"content": final_content},
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"finish_reason": "stop",
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"stop_reason": stop_reason,
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"token_ids": [20],
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},
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),
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)
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assert (emitted[-1]["finish_reason"], emitted[-1].get("stop_reason")) == ("stop", stop_reason)
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assert list(chain.from_iterable(choice.get("token_ids") or () for choice in emitted)) == [16, 11, 17, 20]
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def _held_back_stop_string_stream(token_ids: Mapping[str, None]) -> tuple[Mapping[str, object], ...]:
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return (
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{"index": 0, "delta": {"role": "assistant", "content": ""}, "finish_reason": None},
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{"index": 0, "delta": {"content": ""}, "finish_reason": None, **token_ids},
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{"index": 0, "delta": {"content": "1,2"}, "finish_reason": None, **token_ids},
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{"index": 0, "delta": {"content": ""}, "finish_reason": "stop", "stop_reason": ",3", **token_ids},
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)
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def test_hosted_vllm_stream_with_null_token_ids_emits_the_same_chunks_as_without_them(logging_obj: Logging) -> None:
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with_null_ids: Final = _hosted_vllm_stream_choices(logging_obj, _held_back_stop_string_stream({"token_ids": None}))
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without_ids: Final = _hosted_vllm_stream_choices(logging_obj, _held_back_stop_string_stream({}))
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assert [(c["delta"], c["finish_reason"]) for c in with_null_ids] == [
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(c["delta"], c["finish_reason"]) for c in without_ids
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]
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def _later_chunk_outputs(
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custom_llm_provider: str, later_chunks: Sequence[ChatCompletionChunk], logging_obj: Logging
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) -> tuple[ModelResponseStream, ...]:
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@ -576,6 +576,16 @@ def test_serialize_streaming_chunk_simple_uses_fast_path_bytes():
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}
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def test_serialize_streaming_chunk_keeps_provider_choice_fields():
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chunk = _simple_chunk(content="1,2,3,4")
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chunk.choices[0]["stop_reason"] = ",5"
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chunk.choices[0]["token_ids"] = [20]
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assert json.loads(_serialize_streaming_chunk(chunk))["choices"] == [
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{"index": 0, "delta": {"role": "assistant", "content": "1,2,3,4"}, "stop_reason": ",5", "token_ids": [20]}
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]
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def test_serialize_streaming_chunk_invalid_input_raises_attribute_error():
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"""The helper is typed as ``BaseModel`` — handing it a plain dict trips
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the attribute-access path (no ``model_dump_json``)."""
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@ -13582,7 +13582,7 @@ def _stream_usage_generator_chunks():
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def test_is_injected_stream_usage_artifact():
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from litellm.proxy.proxy_server import _is_injected_stream_usage_artifact
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from litellm.types.utils import ModelResponseStream, Usage
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from litellm.types.utils import Delta, ModelResponseStream, StreamingChoices, Usage
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content_chunk, finish_chunk, empty_choices_usage_chunk = _stream_usage_test_chunks()
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assert _is_injected_stream_usage_artifact(empty_choices_usage_chunk) is True
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@ -13605,6 +13605,16 @@ def test_is_injected_stream_usage_artifact():
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assert _is_injected_stream_usage_artifact({"usage": {"prompt_tokens": 1}}) is False
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held_back_token_ids_chunk = ModelResponseStream(
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model="qwen3", choices=[StreamingChoices(delta=Delta(content=None), token_ids=[16])]
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)
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assert _is_injected_stream_usage_artifact(held_back_token_ids_chunk) is False
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null_token_ids_chunk = ModelResponseStream(
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model="qwen3", choices=[StreamingChoices(delta=Delta(content=None), token_ids=None)]
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)
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assert _is_injected_stream_usage_artifact(null_token_ids_chunk) is True
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async def _collect_async_data_generator_frames(request_data: dict) -> list:
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from litellm.proxy.proxy_server import async_data_generator
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@ -2,8 +2,11 @@ from collections.abc import AsyncIterator, Iterator
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from typing import Final
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import pytest
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from openai.types.completion import Completion
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from litellm.exceptions import APIConnectionError
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from litellm.litellm_core_utils.litellm_logging import Logging
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from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper
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from litellm.text_completions.streaming_iterator import TextCompletionStreamWrapper
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from litellm.types.utils import Choices, Delta, ModelResponseStream, StreamingChoices, Usage
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@ -85,6 +88,44 @@ async def test_text_completion_stream_usage_is_opt_in(
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]
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def _completion_chunk(text: str, finish_reason: str | None, **choice_extras: object) -> Completion:
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return Completion.construct(
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id="cmpl-1",
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created=1,
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model="m",
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object="text_completion",
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choices=[{"index": 0, "text": text, "finish_reason": finish_reason, "logprobs": None, **choice_extras}],
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)
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@pytest.mark.parametrize("final_text", [", 5, 6", ""], ids=["text-then-finish", "finish-only"])
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def test_text_completion_stream_keeps_stop_reason_on_the_finish_chunk(final_text: str) -> None:
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logging_obj: Final = Logging(
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model="m",
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messages=[{"role": "user", "content": "1, 2, 3, 4"}],
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stream=True,
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call_type="text_completion",
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start_time=0.0,
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litellm_call_id="call-1",
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function_id="function-1",
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)
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upstream: Final = (_completion_chunk(", 5", None), _completion_chunk(final_text, "stop", stop_reason=", 7"))
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chat_stream: Final = CustomStreamWrapper(
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completion_stream=iter(upstream),
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model="m",
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custom_llm_provider="text-completion-openai",
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logging_obj=logging_obj,
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)
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responses: Final = list(TextCompletionStreamWrapper(completion_stream=chat_stream, model="m"))
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assert responses[-1].choices[0].model_dump(exclude_none=True) == {
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"finish_reason": "stop",
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"index": 0,
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"stop_reason": ", 7",
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}
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def test_text_completion_conversion_rejects_non_streaming_choices() -> None:
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chunk: Final = Choices()
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wrapper: Final = TextCompletionStreamWrapper(completion_stream=None, model="test-model")
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