mirror of
https://github.com/BerriAI/litellm.git
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fix(hosted_vllm): accept the vLLM production-stack router's file and batch objects (#49068)
* fix(files): default a missing file object type to "file" so OpenAI-compatible servers that omit it stop 500ing * fix(batches): accept the vLLM production-stack router's batch object The router leaves out the batch object's "object" field and reports "pending" and "running" statuses, so batch create and retrieve through a hosted_vllm deployment failed validation with a 500. A missing or null object now reads as "batch", pending maps to validating and running to in_progress, and any other unknown value is still rejected * fix(batches): return OpenAI batch objects from batch list behind the vLLM router * fix(batches): type the batch list conversion without a TypeVar and refuse an async client on the sync path * fix(batches): keep batch list items as OpenAI's Batch so an odd item never fails a list * refactor(types): reuse the shared JSON value alias on the batch type * fix: return the provider page for an async client in sync batch list and pass through non-string list statuses * test: inject the SDK's own transport in the sync batch list test so it passes on OpenAI SDK 3.x --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
This commit is contained in:
mika-lt
GitHub
parent
afec145edc
commit
d1d6d55063
7 changed files with 349 additions and 10 deletions
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@ -16,6 +16,7 @@ from openai._base_client import make_request_options
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from openai._constants import RAW_RESPONSE_HEADER
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from openai._legacy_response import LegacyAPIResponse
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from openai._types import RequestOptions
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from openai.pagination import SyncCursorPage
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from openai.types import CreateEmbeddingResponse
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from openai.types.beta.assistant_deleted import AssistantDeleted
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from openai.types.chat import ChatCompletion, ChatCompletionChunk
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@ -40,6 +41,7 @@ from litellm.types.utils import (
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LiteLLMBatch,
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ModelResponse,
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ModelResponseStream,
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openai_batch_status,
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)
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from litellm.utils import (
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CustomStreamWrapper,
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@ -2182,6 +2184,18 @@ class OpenAIFilesAPI(BaseLLM):
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return response
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def _with_openai_batch_fields(batches: Sequence[Batch]) -> Sequence[Batch]:
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return [
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batch.model_copy(
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update={
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"object": batch.object or "batch",
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"status": openai_batch_status(batch.status),
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}
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)
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for batch in batches
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]
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class OpenAIBatchesAPI(BaseLLM):
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"""
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OpenAI methods to support for batches
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@ -2372,7 +2386,7 @@ class OpenAIBatchesAPI(BaseLLM):
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):
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verbose_logger.debug("listing batches, after= %s, limit= %s", after, limit)
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response: Final = await openai_client.batches.list(after=after, limit=limit)
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return response
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return response.model_copy(update={"data": _with_openai_batch_fields(response.data)})
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def list_batches(
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self,
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@ -2384,7 +2398,7 @@ class OpenAIBatchesAPI(BaseLLM):
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organization: str | None,
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after: str | None = None,
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limit: int | None = None,
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client: OpenAI | None = None,
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client: OpenAI | AsyncOpenAI | None = None,
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litellm_params: Mapping[str, object] | None = None,
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):
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openai_client: Final[OpenAI | AsyncOpenAI | None] = self.get_openai_client(
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@ -2409,7 +2423,9 @@ class OpenAIBatchesAPI(BaseLLM):
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)
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return self.alist_batches(openai_client=openai_client, after=after, limit=limit)
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response: Final = openai_client.batches.list(after=after, limit=limit)
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return response
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if not isinstance(response, SyncCursorPage):
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return response
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return response.model_copy(update={"data": _with_openai_batch_fields(response.data)})
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class OpenAIAssistantsAPI(BaseLLM):
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@ -402,7 +402,7 @@ class OpenAIFileObject(LiteLLMBaseModel):
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filename: str
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"""The name of the file."""
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object: Literal["file"]
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object: Literal["file"] = "file"
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"""The object type, which is always `file`."""
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purpose: OpenAIFilesPurpose
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@ -440,6 +440,11 @@ class OpenAIFileObject(LiteLLMBaseModel):
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_hidden_params: dict = PrivateAttr(default={"response_cost": 0.0}) # no cost for writing a file
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@field_validator("object", mode="before")
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@classmethod
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def _default_missing_object_type(cls, value: _JsonValue) -> _JsonValue:
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return "file" if value is None else value
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@property
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def hidden_params(self) -> dict[str, builtins.object]: # mutable-ok: API requires mutation
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return self._hidden_params
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@ -106,6 +106,7 @@ from .llms.openai import (
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OpenAIRealtimeStreamList,
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ResponsesAPIResponse,
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WebSearchOptions,
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_JsonValue,
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)
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from .rerank import RerankResponse as RerankResponse
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@ -4963,29 +4964,50 @@ def _batch_usage_from_chat_usage(usage: Usage) -> BatchUsage:
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)
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OPENAI_BATCH_STATUS_FOR_VLLM_ROUTER_STATUS: Final = MappingProxyType(
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{"pending": "validating", "running": "in_progress"}
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)
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def openai_batch_status(status: _JsonValue) -> _JsonValue:
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return OPENAI_BATCH_STATUS_FOR_VLLM_ROUTER_STATUS.get(status, status) if isinstance(status, str) else status
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class LiteLLMBatch(Batch):
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_hidden_params: dict = {}
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object: Literal["batch"] = "batch"
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@field_validator("object", mode="before")
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@classmethod
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def _default_missing_object_type(cls, value: _JsonValue) -> _JsonValue:
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return "batch" if value is None else value
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@field_validator("status", mode="before")
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@classmethod
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def _map_vllm_router_status(cls, value: _JsonValue) -> _JsonValue:
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return openai_batch_status(value)
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@property
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def hidden_params(self) -> dict[str, object]: # mutable-ok: API requires mutation
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def hidden_params(self) -> dict[str, _JsonValue]: # mutable-ok: API requires mutation
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return self._hidden_params
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@hidden_params.setter
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def hidden_params(self, hidden_params: dict[str, object]) -> None: # mutable-ok: API requires mutation
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def hidden_params(self, hidden_params: dict[str, _JsonValue]) -> None: # mutable-ok: API requires mutation
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self._hidden_params = hidden_params
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usage: Usage | None = None
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@field_validator("usage", mode="before")
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@classmethod
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def _usage_from_batch_token_counts(cls, value: object) -> object:
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def _usage_from_batch_token_counts(cls, value: _JsonValue) -> _JsonValue:
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counts: Final = _parse_batch_token_counts(value)
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return value if counts is None else _usage_with_batch_token_counts(Usage.model_validate(value), counts)
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@field_serializer("usage")
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def _serialize_usage_with_batch_fields(
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self, usage: Usage | None, info: FieldSerializationInfo
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) -> Mapping[str, object] | None:
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) -> Mapping[str, _JsonValue] | None:
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if not isinstance(usage, Usage):
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return usage
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chat_fields: Final = usage.model_dump(
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@ -37,13 +37,15 @@ import litellm.batches.main as bm
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import asyncio
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import datetime
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import json
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from collections.abc import Mapping
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from collections.abc import Awaitable, Callable, Mapping, Sequence
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from typing import Final
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import httpx
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import respx
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from pydantic import TypeAdapter
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from pydantic import TypeAdapter, ValidationError
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from typing_extensions import ReadOnly, TypedDict
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from litellm.integrations.custom_logger import CustomLogger
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from openai.types.batch import Batch
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from litellm.types.utils import LiteLLMBatch
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# --------------------------------------------------------------------------- #
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@ -1204,3 +1206,183 @@ async def test_delete_batch_output_file(respx_mock: respx.MockRouter, monkeypatc
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assert delete_route.call_count == 1
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assert delete_response.id == "file-output123"
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assert delete_response.deleted is True
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VLLM_ROUTER_API_BASE: Final = "http://vllm-router.test/v1"
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VLLM_ROUTER_BATCH: Final = MappingProxyType(
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{
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"id": "batch_5f8dac",
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"status": "pending",
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"input_file_id": "file-b73f4c",
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"created_at": 1793239207,
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"endpoint": "/v1/chat/completions",
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"completion_window": "24h",
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"output_file_id": None,
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"total_requests": None,
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"completed_requests": 0,
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"failed_requests": 0,
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"metadata": {},
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}
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)
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async def _create_on_vllm_router() -> LiteLLMBatch:
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return await litellm.acreate_batch(
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completion_window="24h",
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endpoint="/v1/chat/completions",
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input_file_id="file-b73f4c",
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custom_llm_provider="hosted_vllm",
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api_key="sk-test",
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api_base=VLLM_ROUTER_API_BASE,
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)
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async def _retrieve_from_vllm_router() -> LiteLLMBatch:
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return await litellm.aretrieve_batch(
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batch_id="batch_5f8dac", custom_llm_provider="hosted_vllm", api_key="sk-test", api_base=VLLM_ROUTER_API_BASE
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)
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VLLM_ROUTER_BATCH_CALLS: Final = (
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pytest.param("POST", "/batches", _create_on_vllm_router, id="acreate_batch"),
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pytest.param("GET", "/batches/batch_5f8dac", _retrieve_from_vllm_router, id="aretrieve_batch"),
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)
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@pytest.mark.parametrize("method, path, call_router", VLLM_ROUTER_BATCH_CALLS)
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@pytest.mark.parametrize(
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"router_status, openai_status",
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(
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("pending", "validating"),
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("running", "in_progress"),
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("completed", "completed"),
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("failed", "failed"),
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("cancelled", "cancelled"),
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),
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)
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async def test_hosted_vllm_router_batch_comes_back_as_an_openai_batch(
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respx_mock: respx.MockRouter,
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method: str,
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path: str,
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call_router: Callable[[], Awaitable[LiteLLMBatch]],
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router_status: str,
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openai_status: str,
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):
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respx_mock.route(method=method, url=f"{VLLM_ROUTER_API_BASE}{path}").respond(
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200, json={**VLLM_ROUTER_BATCH, "status": router_status}
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)
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batch: Final = await call_router()
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assert (batch.id, batch.object, batch.status) == ("batch_5f8dac", "batch", openai_status)
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@pytest.mark.parametrize("method, path, call_router", VLLM_ROUTER_BATCH_CALLS)
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@pytest.mark.parametrize("field, value", (("status", "queued"), ("object", "file")))
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async def test_hosted_vllm_router_batch_with_an_unknown_value_is_rejected(
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respx_mock: respx.MockRouter,
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method: str,
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path: str,
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call_router: Callable[[], Awaitable[LiteLLMBatch]],
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field: str,
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value: str,
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):
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respx_mock.route(method=method, url=f"{VLLM_ROUTER_API_BASE}{path}").respond(
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200, json={**VLLM_ROUTER_BATCH, field: value}
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)
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with pytest.raises(ValidationError) as exc_info:
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await call_router()
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assert [error["loc"] for error in exc_info.value.errors()] == [(field,)]
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async def _list_on_vllm_router() -> Sequence[Batch]:
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page: Final = await litellm.alist_batches(
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custom_llm_provider="hosted_vllm", api_key="sk-test", api_base=VLLM_ROUTER_API_BASE
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)
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return page.data
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async def _list_on_vllm_router_sync() -> Sequence[Batch]:
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return litellm.list_batches(custom_llm_provider="hosted_vllm", api_key="sk-test", api_base=VLLM_ROUTER_API_BASE).data
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@pytest.mark.parametrize("list_router", (_list_on_vllm_router, _list_on_vllm_router_sync))
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async def test_hosted_vllm_router_batch_list_comes_back_as_openai_batches(
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respx_mock: respx.MockRouter, list_router: Callable[[], Awaitable[Sequence[Batch]]]
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):
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respx_mock.get(f"{VLLM_ROUTER_API_BASE}/batches").respond(
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200,
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json={
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"object": "list",
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"data": [
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{**VLLM_ROUTER_BATCH, "id": "batch_5f8dac", "status": "pending"},
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{**VLLM_ROUTER_BATCH, "id": "batch_c74802", "status": "running"},
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],
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"first_id": "batch_5f8dac",
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"last_id": "batch_c74802",
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"has_more": False,
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},
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)
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batches: Final = await list_router()
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assert [(batch.id, batch.object, batch.status) for batch in batches] == [
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("batch_5f8dac", "batch", "validating"),
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("batch_c74802", "batch", "in_progress"),
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]
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async def test_hosted_vllm_router_batch_list_passes_through_a_status_that_is_not_a_string(
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respx_mock: respx.MockRouter,
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):
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respx_mock.get(f"{VLLM_ROUTER_API_BASE}/batches").respond(
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200,
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json={
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"object": "list",
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"data": [{**VLLM_ROUTER_BATCH, "status": ["pending"]}],
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"first_id": "batch_5f8dac",
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"last_id": "batch_5f8dac",
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"has_more": False,
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},
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)
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batches: Final = await _list_on_vllm_router()
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assert [(batch.id, batch.object, batch.status) for batch in batches] == [("batch_5f8dac", "batch", ["pending"])]
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async def test_openai_batch_list_items_come_back_as_openai_sent_them(respx_mock: respx.MockRouter):
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respx_mock.get("http://openai.test/v1/batches").respond(
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200,
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json={
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"object": "list",
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"data": [
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{
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**_OPENAI_BATCH_JSON,
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"status": "completed",
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"usage": {
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"input_tokens": 10,
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"input_tokens_details": {"cached_tokens": 4},
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"output_tokens": 5,
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"output_tokens_details": {"reasoning_tokens": 2},
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"total_tokens": 15,
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},
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},
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{**_OPENAI_BATCH_JSON, "id": "batch_def456", "status": "queued"},
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],
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"first_id": "batch_abc123",
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"last_id": "batch_def456",
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"has_more": False,
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},
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)
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page: Final = await litellm.alist_batches(
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custom_llm_provider="openai", api_key="sk-test", api_base="http://openai.test/v1"
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)
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first, second = page.data
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assert first.usage is not None
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assert (first.usage.input_tokens_details.cached_tokens, first.usage.output_tokens_details.reasoning_tokens) == (4, 2)
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assert (second.id, second.object, second.status) == ("batch_def456", "batch", "queued")
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@ -1,3 +1,4 @@
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from collections.abc import Awaitable, Callable
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from types import MappingProxyType
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from typing import Final
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from urllib.parse import parse_qs, urlparse
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@ -124,6 +125,61 @@ async def test_afile_retrieve_rejects_a_provider_file_without_its_size():
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assert [error["loc"] for error in exc_info.value.errors()] == [("bytes",)]
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VLLM_ROUTER_API_BASE: Final = "http://vllm-router.test/v1"
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VLLM_ROUTER_FILE: Final = MappingProxyType(
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{"id": "file-b73f4c", "bytes": 156, "created_at": 1793238707, "filename": "batch_requests.jsonl", "purpose": "batch"}
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)
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async def _upload_to_vllm_router() -> OpenAIFileObject:
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return await litellm.acreate_file(
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file=("batch_requests.jsonl", b'{"custom_id": "request-1"}'),
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purpose="batch",
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custom_llm_provider="hosted_vllm",
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api_key="sk-test",
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api_base=VLLM_ROUTER_API_BASE,
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)
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async def _retrieve_from_vllm_router() -> OpenAIFileObject:
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return await litellm.afile_retrieve(
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file_id="file-b73f4c", custom_llm_provider="hosted_vllm", api_key="sk-test", api_base=VLLM_ROUTER_API_BASE
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)
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VLLM_ROUTER_FILE_CALLS: Final = (
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pytest.param("POST", "/files", _upload_to_vllm_router, id="acreate_file"),
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pytest.param("GET", "/files/file-b73f4c", _retrieve_from_vllm_router, id="afile_retrieve"),
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)
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@pytest.mark.parametrize("method, path, call_router", VLLM_ROUTER_FILE_CALLS)
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@respx.mock
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async def test_hosted_vllm_file_without_an_object_type_comes_back_as_a_file(
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method: str, path: str, call_router: Callable[[], Awaitable[OpenAIFileObject]]
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):
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respx.route(method=method, url=f"{VLLM_ROUTER_API_BASE}{path}").respond(200, json=dict(VLLM_ROUTER_FILE))
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file_object: Final = await call_router()
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assert file_object.model_dump(exclude_none=True) == {**VLLM_ROUTER_FILE, "object": "file"}
|
||||
|
||||
|
||||
@pytest.mark.parametrize("method, path, call_router", VLLM_ROUTER_FILE_CALLS)
|
||||
@respx.mock
|
||||
async def test_hosted_vllm_file_with_a_different_object_type_is_rejected(
|
||||
method: str, path: str, call_router: Callable[[], Awaitable[OpenAIFileObject]]
|
||||
):
|
||||
respx.route(method=method, url=f"{VLLM_ROUTER_API_BASE}{path}").respond(
|
||||
200, json={**VLLM_ROUTER_FILE, "object": "batch"}
|
||||
)
|
||||
|
||||
with pytest.raises(ValidationError) as exc_info:
|
||||
await call_router()
|
||||
|
||||
assert [error["loc"] for error in exc_info.value.errors()] == [("object",)]
|
||||
|
||||
|
||||
_FILE_BODY: Final = b'{"prompt": "Hello", "completion": "Hi"}'
|
||||
_FINE_TUNE_FILE_JSON: Final = MappingProxyType(
|
||||
{
|
||||
|
|
|
|||
|
|
@ -24,6 +24,7 @@ from litellm.llms.openai.openai import (
|
|||
AsyncAssistantEventHandler,
|
||||
AsyncCursorPage,
|
||||
MessageData,
|
||||
OpenAIBatchesAPI,
|
||||
OpenAIChatCompletion,
|
||||
OpenAIConfig,
|
||||
OpenAIMessage as Message,
|
||||
|
|
@ -2320,3 +2321,43 @@ def test_openai_config_hoists_a_later_developer_message_only_for_a_model_off_ope
|
|||
model_info=None,
|
||||
)
|
||||
assert [message["role"] for message in messages] == expected_roles
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_sync_batch_list_given_an_async_client_returns_a_page_the_caller_can_await(
|
||||
http_backend: ModuleType,
|
||||
):
|
||||
def respond(request: _Request) -> httpx.Response | SDKResponse:
|
||||
assert str(request.url).startswith("http://vllm-router.test/v1/batches")
|
||||
return http_backend.Response(
|
||||
200,
|
||||
json={
|
||||
"object": "list",
|
||||
"data": [
|
||||
{
|
||||
"id": "batch_5f8dac",
|
||||
"status": "pending",
|
||||
"input_file_id": "file-b73f4c",
|
||||
"created_at": 1793239207,
|
||||
"endpoint": "/v1/chat/completions",
|
||||
"completion_window": "24h",
|
||||
}
|
||||
],
|
||||
"first_id": "batch_5f8dac",
|
||||
"last_id": "batch_5f8dac",
|
||||
"has_more": False,
|
||||
},
|
||||
)
|
||||
|
||||
async with http_backend.AsyncClient(transport=http_backend.MockTransport(respond)) as http_client:
|
||||
page: Final = OpenAIBatchesAPI().list_batches(
|
||||
_is_async=False,
|
||||
api_key="sk-test",
|
||||
api_base="http://vllm-router.test/v1",
|
||||
timeout=5.0,
|
||||
max_retries=0,
|
||||
organization=None,
|
||||
client=AsyncOpenAI(api_key="sk-test", base_url="http://vllm-router.test/v1", http_client=http_client),
|
||||
)
|
||||
|
||||
assert [(batch.id, batch.status) for batch in (await page).data] == [("batch_5f8dac", "pending")]
|
||||
|
|
|
|||
|
|
@ -607,6 +607,23 @@ def test_openai_file_object_accepts_pending_status():
|
|||
assert file_obj.status == "pending"
|
||||
|
||||
|
||||
@pytest.mark.parametrize("object_type", ({}, {"object": None}), ids=["missing", "null"])
|
||||
def test_openai_file_object_without_an_object_type_validates_as_a_file(object_type: dict[str, None]) -> None:
|
||||
from litellm.types.llms.openai import OpenAIFileObject
|
||||
|
||||
server_file: Final = {
|
||||
"id": "file-b73f4c",
|
||||
"bytes": 156,
|
||||
"created_at": 1793238707,
|
||||
"filename": "batch_requests.jsonl",
|
||||
"purpose": "batch",
|
||||
}
|
||||
|
||||
file_obj: Final = OpenAIFileObject.model_validate({**server_file, **object_type})
|
||||
|
||||
assert file_obj.model_dump(exclude_none=True) == {**server_file, "object": "file"}
|
||||
|
||||
|
||||
class TestOpenAIFileObjectBatchGuardrailSerialization:
|
||||
"""The proxy-only `litellm_batch_guardrail` key must reach the wire only when something set it."""
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue