chore: initial public snapshot for github upload
This commit is contained in:
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# Vertex AI Batch Prediction Jobs
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Implementation to call VertexAI Batch endpoints in OpenAI Batch API spec
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Vertex Docs: https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/batch-prediction-gemini
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@@ -0,0 +1,378 @@
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import json
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from typing import Any, Coroutine, Dict, Optional, Union
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import httpx
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import litellm
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from litellm.llms.custom_httpx.http_handler import (
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_get_httpx_client,
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get_async_httpx_client,
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)
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from litellm.llms.vertex_ai.common_utils import get_vertex_base_url
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from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import VertexLLM
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from litellm.types.llms.openai import CreateBatchRequest
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from litellm.types.llms.vertex_ai import (
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VERTEX_CREDENTIALS_TYPES,
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VertexAIBatchPredictionJob,
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)
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from litellm.types.utils import LiteLLMBatch
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from .transformation import VertexAIBatchTransformation
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class VertexAIBatchPrediction(VertexLLM):
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def __init__(self, gcs_bucket_name: str, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.gcs_bucket_name = gcs_bucket_name
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def create_batch(
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self,
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_is_async: bool,
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create_batch_data: CreateBatchRequest,
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api_base: Optional[str],
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vertex_credentials: Optional[VERTEX_CREDENTIALS_TYPES],
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vertex_project: Optional[str],
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vertex_location: Optional[str],
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timeout: Union[float, httpx.Timeout],
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max_retries: Optional[int],
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) -> Union[LiteLLMBatch, Coroutine[Any, Any, LiteLLMBatch]]:
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sync_handler = _get_httpx_client()
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access_token, project_id = self._ensure_access_token(
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credentials=vertex_credentials,
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project_id=vertex_project,
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custom_llm_provider="vertex_ai",
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)
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default_api_base = self.create_vertex_batch_url(
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vertex_location=vertex_location or "us-central1",
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vertex_project=vertex_project or project_id,
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)
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if len(default_api_base.split(":")) > 1:
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endpoint = default_api_base.split(":")[-1]
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else:
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endpoint = ""
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_, api_base = self._check_custom_proxy(
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api_base=api_base,
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custom_llm_provider="vertex_ai",
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gemini_api_key=None,
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endpoint=endpoint,
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stream=None,
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auth_header=None,
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url=default_api_base,
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model=None,
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vertex_project=vertex_project or project_id,
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vertex_location=vertex_location or "us-central1",
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vertex_api_version="v1",
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)
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headers = {
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"Content-Type": "application/json; charset=utf-8",
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"Authorization": f"Bearer {access_token}",
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}
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vertex_batch_request: VertexAIBatchPredictionJob = VertexAIBatchTransformation.transform_openai_batch_request_to_vertex_ai_batch_request(
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request=create_batch_data
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)
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if _is_async is True:
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return self._async_create_batch(
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vertex_batch_request=vertex_batch_request,
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api_base=api_base,
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headers=headers,
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)
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response = sync_handler.post(
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url=api_base,
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headers=headers,
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data=json.dumps(vertex_batch_request),
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)
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if response.status_code != 200:
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raise Exception(f"Error: {response.status_code} {response.text}")
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_json_response = response.json()
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vertex_batch_response = VertexAIBatchTransformation.transform_vertex_ai_batch_response_to_openai_batch_response(
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response=_json_response
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)
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return vertex_batch_response
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async def _async_create_batch(
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self,
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vertex_batch_request: VertexAIBatchPredictionJob,
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api_base: str,
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headers: Dict[str, str],
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) -> LiteLLMBatch:
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client = get_async_httpx_client(
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llm_provider=litellm.LlmProviders.VERTEX_AI,
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)
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try:
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response = await client.post(
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url=api_base,
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headers=headers,
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data=json.dumps(vertex_batch_request),
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)
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except httpx.HTTPStatusError as e:
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error_body = e.response.text
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litellm.verbose_logger.error(
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"Vertex AI batch create failed: status=%s, body=%s",
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e.response.status_code,
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error_body[:1000],
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)
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raise
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if response.status_code != 200:
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raise Exception(f"Error: {response.status_code} {response.text}")
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_json_response = response.json()
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vertex_batch_response = VertexAIBatchTransformation.transform_vertex_ai_batch_response_to_openai_batch_response(
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response=_json_response
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)
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return vertex_batch_response
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def create_vertex_batch_url(
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self,
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vertex_location: str,
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vertex_project: str,
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) -> str:
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"""Return the base url for the vertex garden models"""
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# POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/batchPredictionJobs
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base_url = get_vertex_base_url(vertex_location)
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return f"{base_url}/v1/projects/{vertex_project}/locations/{vertex_location}/batchPredictionJobs"
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def retrieve_batch(
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self,
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_is_async: bool,
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batch_id: str,
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api_base: Optional[str],
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vertex_credentials: Optional[VERTEX_CREDENTIALS_TYPES],
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vertex_project: Optional[str],
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vertex_location: Optional[str],
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timeout: Union[float, httpx.Timeout],
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max_retries: Optional[int],
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logging_obj: Optional[Any] = None,
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) -> Union[LiteLLMBatch, Coroutine[Any, Any, LiteLLMBatch]]:
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sync_handler = _get_httpx_client()
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access_token, project_id = self._ensure_access_token(
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credentials=vertex_credentials,
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project_id=vertex_project,
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custom_llm_provider="vertex_ai",
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)
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default_api_base = self.create_vertex_batch_url(
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vertex_location=vertex_location or "us-central1",
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vertex_project=vertex_project or project_id,
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)
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# Append batch_id to the URL
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default_api_base = f"{default_api_base}/{batch_id}"
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if len(default_api_base.split(":")) > 1:
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endpoint = default_api_base.split(":")[-1]
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else:
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endpoint = ""
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_, api_base = self._check_custom_proxy(
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api_base=api_base,
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custom_llm_provider="vertex_ai",
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gemini_api_key=None,
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endpoint=endpoint,
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stream=None,
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auth_header=None,
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url=default_api_base,
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model=None,
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vertex_project=vertex_project or project_id,
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vertex_location=vertex_location or "us-central1",
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vertex_api_version="v1",
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)
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headers = {
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"Content-Type": "application/json; charset=utf-8",
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"Authorization": f"Bearer {access_token}",
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}
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if _is_async is True:
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return self._async_retrieve_batch(
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api_base=api_base,
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headers=headers,
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logging_obj=logging_obj,
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)
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# Log the request using logging_obj if available
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if logging_obj is not None:
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from litellm.litellm_core_utils.litellm_logging import Logging
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if isinstance(logging_obj, Logging):
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logging_obj.pre_call(
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input="",
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api_key="",
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additional_args={
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"complete_input_dict": {},
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"api_base": api_base,
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"headers": headers,
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"request_str": (
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f"\nGET Request Sent from LiteLLM:\n"
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f"curl -X GET \\\n"
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f"{api_base} \\\n"
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f"-H 'Authorization: Bearer ***REDACTED***' \\\n"
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f"-H 'Content-Type: application/json; charset=utf-8'\n"
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),
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},
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)
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response = sync_handler.get(
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url=api_base,
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headers=headers,
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)
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if response.status_code != 200:
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raise Exception(f"Error: {response.status_code} {response.text}")
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_json_response = response.json()
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vertex_batch_response = VertexAIBatchTransformation.transform_vertex_ai_batch_response_to_openai_batch_response(
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response=_json_response
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)
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return vertex_batch_response
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async def _async_retrieve_batch(
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self,
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api_base: str,
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headers: Dict[str, str],
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logging_obj: Optional[Any] = None,
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) -> LiteLLMBatch:
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client = get_async_httpx_client(
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llm_provider=litellm.LlmProviders.VERTEX_AI,
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)
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# Log the request using logging_obj if available
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if logging_obj is not None:
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from litellm.litellm_core_utils.litellm_logging import Logging
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if isinstance(logging_obj, Logging):
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logging_obj.pre_call(
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input="",
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api_key="",
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additional_args={
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"complete_input_dict": {},
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"api_base": api_base,
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"headers": headers,
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"request_str": (
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f"\nGET Request Sent from LiteLLM:\n"
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f"curl -X GET \\\n"
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f"{api_base} \\\n"
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f"-H 'Authorization: Bearer ***REDACTED***' \\\n"
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f"-H 'Content-Type: application/json; charset=utf-8'\n"
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),
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},
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)
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response = await client.get(
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url=api_base,
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headers=headers,
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)
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if response.status_code != 200:
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raise Exception(f"Error: {response.status_code} {response.text}")
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_json_response = response.json()
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vertex_batch_response = VertexAIBatchTransformation.transform_vertex_ai_batch_response_to_openai_batch_response(
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response=_json_response
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)
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return vertex_batch_response
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def list_batches(
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self,
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_is_async: bool,
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after: Optional[str],
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limit: Optional[int],
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api_base: Optional[str],
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vertex_credentials: Optional[VERTEX_CREDENTIALS_TYPES],
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vertex_project: Optional[str],
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vertex_location: Optional[str],
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timeout: Union[float, httpx.Timeout],
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max_retries: Optional[int],
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):
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sync_handler = _get_httpx_client()
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access_token, project_id = self._ensure_access_token(
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credentials=vertex_credentials,
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project_id=vertex_project,
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custom_llm_provider="vertex_ai",
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)
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default_api_base = self.create_vertex_batch_url(
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vertex_location=vertex_location or "us-central1",
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vertex_project=vertex_project or project_id,
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)
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if len(default_api_base.split(":")) > 1:
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endpoint = default_api_base.split(":")[-1]
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else:
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endpoint = ""
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_, api_base = self._check_custom_proxy(
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api_base=api_base,
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custom_llm_provider="vertex_ai",
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gemini_api_key=None,
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endpoint=endpoint,
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stream=None,
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auth_header=None,
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url=default_api_base,
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)
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headers = {
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"Content-Type": "application/json; charset=utf-8",
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"Authorization": f"Bearer {access_token}",
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}
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params: Dict[str, Any] = {}
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if limit is not None:
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params["pageSize"] = str(limit)
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if after is not None:
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params["pageToken"] = after
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if _is_async is True:
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return self._async_list_batches(
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api_base=api_base,
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headers=headers,
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params=params,
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)
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response = sync_handler.get(
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url=api_base,
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headers=headers,
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params=params,
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)
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if response.status_code != 200:
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raise Exception(f"Error: {response.status_code} {response.text}")
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_json_response = response.json()
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vertex_batch_response = VertexAIBatchTransformation.transform_vertex_ai_batch_list_response_to_openai_list_response(
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response=_json_response
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)
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return vertex_batch_response
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async def _async_list_batches(
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self,
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api_base: str,
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headers: Dict[str, str],
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params: Dict[str, Any],
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):
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client = get_async_httpx_client(
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llm_provider=litellm.LlmProviders.VERTEX_AI,
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)
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response = await client.get(
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url=api_base,
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headers=headers,
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params=params,
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)
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if response.status_code != 200:
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raise Exception(f"Error: {response.status_code} {response.text}")
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_json_response = response.json()
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vertex_batch_response = VertexAIBatchTransformation.transform_vertex_ai_batch_list_response_to_openai_list_response(
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response=_json_response
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)
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return vertex_batch_response
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@@ -0,0 +1,227 @@
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from litellm._uuid import uuid
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from typing import Any, Dict
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from litellm.llms.vertex_ai.common_utils import (
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_convert_vertex_datetime_to_openai_datetime,
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)
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from litellm.types.llms.openai import BatchJobStatus, CreateBatchRequest
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from litellm.types.llms.vertex_ai import *
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from litellm.types.utils import LiteLLMBatch
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class VertexAIBatchTransformation:
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"""
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Transforms OpenAI Batch requests to Vertex AI Batch requests
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API Ref: https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/batch-prediction-gemini
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"""
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@classmethod
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def transform_openai_batch_request_to_vertex_ai_batch_request(
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cls,
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request: CreateBatchRequest,
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) -> VertexAIBatchPredictionJob:
|
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"""
|
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Transforms OpenAI Batch requests to Vertex AI Batch requests
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"""
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request_display_name = f"litellm-vertex-batch-{uuid.uuid4()}"
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input_file_id = request.get("input_file_id")
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if input_file_id is None:
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raise ValueError("input_file_id is required, but not provided")
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input_config: InputConfig = InputConfig(
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gcsSource=GcsSource(uris=[input_file_id]), instancesFormat="jsonl"
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)
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model: str = cls._get_model_from_gcs_file(input_file_id)
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output_config: OutputConfig = OutputConfig(
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predictionsFormat="jsonl",
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gcsDestination=GcsDestination(
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outputUriPrefix=cls._get_gcs_uri_prefix_from_file(input_file_id)
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),
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)
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return VertexAIBatchPredictionJob(
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inputConfig=input_config,
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outputConfig=output_config,
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model=model,
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displayName=request_display_name,
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)
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@classmethod
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def transform_vertex_ai_batch_response_to_openai_batch_response(
|
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cls, response: VertexBatchPredictionResponse
|
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) -> LiteLLMBatch:
|
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return LiteLLMBatch(
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id=cls._get_batch_id_from_vertex_ai_batch_response(response),
|
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completion_window="24hrs",
|
||||
created_at=_convert_vertex_datetime_to_openai_datetime(
|
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vertex_datetime=response.get("createTime", "")
|
||||
),
|
||||
endpoint="",
|
||||
input_file_id=cls._get_input_file_id_from_vertex_ai_batch_response(
|
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response
|
||||
),
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object="batch",
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status=cls._get_batch_job_status_from_vertex_ai_batch_response(response),
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error_file_id=None, # Vertex AI doesn't seem to have a direct equivalent
|
||||
output_file_id=cls._get_output_file_id_from_vertex_ai_batch_response(
|
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response
|
||||
),
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def transform_vertex_ai_batch_list_response_to_openai_list_response(
|
||||
cls, response: Dict[str, Any]
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Transforms Vertex AI batch list response into OpenAI-compatible list response.
|
||||
"""
|
||||
|
||||
batch_jobs = response.get("batchPredictionJobs", []) or []
|
||||
data = [
|
||||
cls.transform_vertex_ai_batch_response_to_openai_batch_response(job)
|
||||
for job in batch_jobs
|
||||
]
|
||||
|
||||
first_id = data[0].id if len(data) > 0 else None
|
||||
last_id = data[-1].id if len(data) > 0 else None
|
||||
next_page_token = response.get("nextPageToken")
|
||||
|
||||
return {
|
||||
"object": "list",
|
||||
"data": data,
|
||||
"first_id": first_id,
|
||||
"last_id": last_id,
|
||||
"has_more": bool(next_page_token),
|
||||
"next_page_token": next_page_token,
|
||||
}
|
||||
|
||||
@classmethod
|
||||
def _get_batch_id_from_vertex_ai_batch_response(
|
||||
cls, response: VertexBatchPredictionResponse
|
||||
) -> str:
|
||||
"""
|
||||
Gets the batch id from the Vertex AI Batch response safely
|
||||
|
||||
vertex response: `projects/510528649030/locations/us-central1/batchPredictionJobs/3814889423749775360`
|
||||
returns: `3814889423749775360`
|
||||
"""
|
||||
_name = response.get("name", "")
|
||||
if not _name:
|
||||
return ""
|
||||
|
||||
# Split by '/' and get the last part if it exists
|
||||
parts = _name.split("/")
|
||||
return parts[-1] if parts else _name
|
||||
|
||||
@classmethod
|
||||
def _get_input_file_id_from_vertex_ai_batch_response(
|
||||
cls, response: VertexBatchPredictionResponse
|
||||
) -> str:
|
||||
"""
|
||||
Gets the input file id from the Vertex AI Batch response
|
||||
"""
|
||||
input_file_id: str = ""
|
||||
input_config = response.get("inputConfig")
|
||||
if input_config is None:
|
||||
return input_file_id
|
||||
|
||||
gcs_source = input_config.get("gcsSource")
|
||||
if gcs_source is None:
|
||||
return input_file_id
|
||||
|
||||
uris = gcs_source.get("uris", "")
|
||||
if len(uris) == 0:
|
||||
return input_file_id
|
||||
|
||||
return uris[0]
|
||||
|
||||
@classmethod
|
||||
def _get_output_file_id_from_vertex_ai_batch_response(
|
||||
cls, response: VertexBatchPredictionResponse
|
||||
) -> str:
|
||||
"""
|
||||
Gets the output file id from the Vertex AI Batch response
|
||||
"""
|
||||
|
||||
output_file_id: str = (
|
||||
response.get("outputInfo", OutputInfo()).get("gcsOutputDirectory", "")
|
||||
+ "/predictions.jsonl"
|
||||
)
|
||||
if output_file_id != "/predictions.jsonl":
|
||||
return output_file_id
|
||||
|
||||
output_config = response.get("outputConfig")
|
||||
if output_config is None:
|
||||
return output_file_id
|
||||
|
||||
gcs_destination = output_config.get("gcsDestination")
|
||||
if gcs_destination is None:
|
||||
return output_file_id
|
||||
|
||||
output_uri_prefix = gcs_destination.get("outputUriPrefix", "")
|
||||
return output_uri_prefix
|
||||
|
||||
@classmethod
|
||||
def _get_batch_job_status_from_vertex_ai_batch_response(
|
||||
cls, response: VertexBatchPredictionResponse
|
||||
) -> BatchJobStatus:
|
||||
"""
|
||||
Gets the batch job status from the Vertex AI Batch response
|
||||
|
||||
ref: https://cloud.google.com/vertex-ai/docs/reference/rest/v1/JobState
|
||||
"""
|
||||
state_mapping: Dict[str, BatchJobStatus] = {
|
||||
"JOB_STATE_UNSPECIFIED": "failed",
|
||||
"JOB_STATE_QUEUED": "validating",
|
||||
"JOB_STATE_PENDING": "validating",
|
||||
"JOB_STATE_RUNNING": "in_progress",
|
||||
"JOB_STATE_SUCCEEDED": "completed",
|
||||
"JOB_STATE_FAILED": "failed",
|
||||
"JOB_STATE_CANCELLING": "cancelling",
|
||||
"JOB_STATE_CANCELLED": "cancelled",
|
||||
"JOB_STATE_PAUSED": "in_progress",
|
||||
"JOB_STATE_EXPIRED": "expired",
|
||||
"JOB_STATE_UPDATING": "in_progress",
|
||||
"JOB_STATE_PARTIALLY_SUCCEEDED": "completed",
|
||||
}
|
||||
|
||||
vertex_state = response.get("state", "JOB_STATE_UNSPECIFIED")
|
||||
return state_mapping[vertex_state]
|
||||
|
||||
@classmethod
|
||||
def _get_gcs_uri_prefix_from_file(cls, input_file_id: str) -> str:
|
||||
"""
|
||||
Gets the gcs uri prefix from the input file id
|
||||
|
||||
Example:
|
||||
input_file_id: "gs://litellm-testing-bucket/vtx_batch.jsonl"
|
||||
returns: "gs://litellm-testing-bucket"
|
||||
|
||||
input_file_id: "gs://litellm-testing-bucket/batches/vtx_batch.jsonl"
|
||||
returns: "gs://litellm-testing-bucket/batches"
|
||||
"""
|
||||
# Split the path and remove the filename
|
||||
path_parts = input_file_id.rsplit("/", 1)
|
||||
return path_parts[0]
|
||||
|
||||
@classmethod
|
||||
def _get_model_from_gcs_file(cls, gcs_file_uri: str) -> str:
|
||||
"""
|
||||
Extracts the model from the gcs file uri
|
||||
|
||||
When files are uploaded using LiteLLM (/v1/files), the model is stored in the gcs file uri
|
||||
|
||||
Why?
|
||||
- Because Vertex Requires the `model` param in create batch jobs request, but OpenAI does not require this
|
||||
|
||||
|
||||
gcs_file_uri format: gs://litellm-testing-bucket/litellm-vertex-files/publishers/google/models/gemini-1.5-flash-001/e9412502-2c91-42a6-8e61-f5c294cc0fc8
|
||||
returns: "publishers/google/models/gemini-1.5-flash-001"
|
||||
"""
|
||||
from urllib.parse import unquote
|
||||
|
||||
decoded_uri = unquote(gcs_file_uri)
|
||||
|
||||
model_path = decoded_uri.split("publishers/")[1]
|
||||
parts = model_path.split("/")
|
||||
model = f"publishers/{'/'.join(parts[:3])}"
|
||||
return model
|
||||
Reference in New Issue
Block a user