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import json
import traceback
from datetime import datetime
from typing import Literal, Optional, Union
import httpx
from openai.types.fine_tuning.fine_tuning_job import FineTuningJob
import litellm
from litellm._logging import verbose_logger
from litellm.llms.custom_httpx.http_handler import HTTPHandler, get_async_httpx_client
from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import VertexLLM
from litellm.types.fine_tuning import OpenAIFineTuningHyperparameters
from litellm.types.llms.openai import FineTuningJobCreate
from litellm.types.llms.vertex_ai import (
FineTuneHyperparameters,
FineTuneJobCreate,
FineTunesupervisedTuningSpec,
ResponseSupervisedTuningSpec,
ResponseTuningJob,
)
class VertexFineTuningAPI(VertexLLM):
"""
Vertex methods to support for batches
"""
def __init__(self) -> None:
super().__init__()
self.async_handler = get_async_httpx_client(
llm_provider=litellm.LlmProviders.VERTEX_AI,
params={"timeout": 600.0},
)
def convert_response_created_at(self, response: ResponseTuningJob):
try:
create_time_str = response.get("createTime", "") or ""
create_time_datetime = datetime.fromisoformat(
create_time_str.replace("Z", "+00:00")
)
# Convert to Unix timestamp (seconds since epoch)
created_at = int(create_time_datetime.timestamp())
return created_at
except Exception:
return 0
def convert_openai_request_to_vertex(
self,
create_fine_tuning_job_data: FineTuningJobCreate,
original_hyperparameters: dict = {},
kwargs: Optional[dict] = None,
) -> FineTuneJobCreate:
"""
convert request from OpenAI format to Vertex format
https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/tuning
supervised_tuning_spec = FineTunesupervisedTuningSpec(
"""
supervised_tuning_spec = FineTunesupervisedTuningSpec(
training_dataset_uri=create_fine_tuning_job_data.training_file,
)
if create_fine_tuning_job_data.validation_file:
supervised_tuning_spec["validation_dataset"] = (
create_fine_tuning_job_data.validation_file
)
_vertex_hyperparameters = (
self._transform_openai_hyperparameters_to_vertex_hyperparameters(
create_fine_tuning_job_data=create_fine_tuning_job_data,
kwargs=kwargs,
original_hyperparameters=original_hyperparameters,
)
)
if _vertex_hyperparameters and len(_vertex_hyperparameters) > 0:
supervised_tuning_spec["hyperParameters"] = _vertex_hyperparameters
fine_tune_job = FineTuneJobCreate(
baseModel=create_fine_tuning_job_data.model,
supervisedTuningSpec=supervised_tuning_spec,
tunedModelDisplayName=create_fine_tuning_job_data.suffix,
)
return fine_tune_job
def _transform_openai_hyperparameters_to_vertex_hyperparameters(
self,
create_fine_tuning_job_data: FineTuningJobCreate,
original_hyperparameters: dict = {},
kwargs: Optional[dict] = None,
) -> FineTuneHyperparameters:
_oai_hyperparameters = create_fine_tuning_job_data.hyperparameters
_vertex_hyperparameters = FineTuneHyperparameters()
if _oai_hyperparameters:
if _oai_hyperparameters.n_epochs:
_vertex_hyperparameters["epoch_count"] = int(
_oai_hyperparameters.n_epochs
)
if _oai_hyperparameters.learning_rate_multiplier:
_vertex_hyperparameters["learning_rate_multiplier"] = float(
_oai_hyperparameters.learning_rate_multiplier
)
_adapter_size = original_hyperparameters.get("adapter_size", None)
if _adapter_size:
_vertex_hyperparameters["adapter_size"] = _adapter_size
return _vertex_hyperparameters
def convert_vertex_response_to_open_ai_response(
self, response: ResponseTuningJob
) -> FineTuningJob:
status: Literal[
"validating_files", "queued", "running", "succeeded", "failed", "cancelled"
] = "queued"
if response["state"] == "JOB_STATE_PENDING":
status = "queued"
if response["state"] == "JOB_STATE_SUCCEEDED":
status = "succeeded"
if response["state"] == "JOB_STATE_FAILED":
status = "failed"
if response["state"] == "JOB_STATE_CANCELLED":
status = "cancelled"
if response["state"] == "JOB_STATE_RUNNING":
status = "running"
created_at = self.convert_response_created_at(response)
_supervisedTuningSpec: ResponseSupervisedTuningSpec = (
response.get("supervisedTuningSpec", None) or {}
)
training_uri: str = _supervisedTuningSpec.get("trainingDatasetUri", "") or ""
return FineTuningJob(
id=response.get("name", "") or "",
created_at=created_at,
fine_tuned_model=response.get("tunedModelDisplayName", ""),
finished_at=None,
hyperparameters=self._translate_vertex_response_hyperparameters(
vertex_hyper_parameters=_supervisedTuningSpec.get("hyperParameters", {})
or {}
),
model=response.get("baseModel", "") or "",
object="fine_tuning.job",
organization_id="",
result_files=[],
seed=0,
status=status,
trained_tokens=None,
training_file=training_uri,
validation_file=None,
estimated_finish=None,
integrations=[],
)
def _translate_vertex_response_hyperparameters(
self, vertex_hyper_parameters: FineTuneHyperparameters
) -> OpenAIFineTuningHyperparameters:
"""
translate vertex responsehyperparameters to openai hyperparameters
"""
_dict_remaining_hyperparameters: dict = dict(vertex_hyper_parameters)
return OpenAIFineTuningHyperparameters(
n_epochs=_dict_remaining_hyperparameters.pop("epoch_count", 0),
**_dict_remaining_hyperparameters,
)
async def acreate_fine_tuning_job(
self,
fine_tuning_url: str,
headers: dict,
request_data: FineTuneJobCreate,
):
try:
verbose_logger.debug(
"about to create fine tuning job: %s, request_data: %s",
fine_tuning_url,
json.dumps(request_data, indent=4),
)
if self.async_handler is None:
raise ValueError(
"VertexAI Fine Tuning - async_handler is not initialized"
)
response = await self.async_handler.post(
headers=headers,
url=fine_tuning_url,
json=request_data, # type: ignore
)
if response.status_code != 200:
raise Exception(
f"Error creating fine tuning job. Status code: {response.status_code}. Response: {response.text}"
)
verbose_logger.debug(
"got response from creating fine tuning job: %s", response.json()
)
vertex_response = ResponseTuningJob( # type: ignore
**response.json(),
)
verbose_logger.debug("vertex_response %s", vertex_response)
open_ai_response = self.convert_vertex_response_to_open_ai_response(
vertex_response
)
return open_ai_response
except Exception as e:
verbose_logger.error("asyncerror creating fine tuning job %s", e)
trace_back_str = traceback.format_exc()
verbose_logger.error(trace_back_str)
raise e
def create_fine_tuning_job(
self,
_is_async: bool,
create_fine_tuning_job_data: FineTuningJobCreate,
vertex_project: Optional[str],
vertex_location: Optional[str],
vertex_credentials: Optional[str],
api_base: Optional[str],
timeout: Union[float, httpx.Timeout],
kwargs: Optional[dict] = None,
original_hyperparameters: Optional[dict] = {},
):
verbose_logger.debug(
"creating fine tuning job, args= %s", create_fine_tuning_job_data
)
_auth_header, vertex_project = self._ensure_access_token(
credentials=vertex_credentials,
project_id=vertex_project,
custom_llm_provider="vertex_ai_beta",
)
auth_header, _ = self._get_token_and_url(
model="",
auth_header=_auth_header,
gemini_api_key=None,
vertex_credentials=vertex_credentials,
vertex_project=vertex_project,
vertex_location=vertex_location,
stream=False,
custom_llm_provider="vertex_ai_beta",
api_base=api_base,
)
headers = {
"Authorization": f"Bearer {auth_header}",
"Content-Type": "application/json",
}
fine_tune_job = self.convert_openai_request_to_vertex(
create_fine_tuning_job_data=create_fine_tuning_job_data,
kwargs=kwargs,
original_hyperparameters=original_hyperparameters or {},
)
fine_tuning_url = f"https://{vertex_location}-aiplatform.googleapis.com/v1/projects/{vertex_project}/locations/{vertex_location}/tuningJobs"
if _is_async is True:
return self.acreate_fine_tuning_job( # type: ignore
fine_tuning_url=fine_tuning_url,
headers=headers,
request_data=fine_tune_job,
)
sync_handler = HTTPHandler(timeout=httpx.Timeout(timeout=600.0, connect=5.0))
verbose_logger.debug(
"about to create fine tuning job: %s, request_data: %s",
fine_tuning_url,
fine_tune_job,
)
response = sync_handler.post(
headers=headers,
url=fine_tuning_url,
json=fine_tune_job, # type: ignore
)
if response.status_code != 200:
raise Exception(
f"Error creating fine tuning job. Status code: {response.status_code}. Response: {response.text}"
)
verbose_logger.debug(
"got response from creating fine tuning job: %s", response.json()
)
vertex_response = ResponseTuningJob( # type: ignore
**response.json(),
)
verbose_logger.debug("vertex_response %s", vertex_response)
open_ai_response = self.convert_vertex_response_to_open_ai_response(
vertex_response
)
return open_ai_response
async def pass_through_vertex_ai_POST_request(
self,
request_data: dict,
vertex_project: str,
vertex_location: str,
vertex_credentials: str,
request_route: str,
):
_auth_header, vertex_project = await self._ensure_access_token_async(
credentials=vertex_credentials,
project_id=vertex_project,
custom_llm_provider="vertex_ai_beta",
)
auth_header, _ = self._get_token_and_url(
model="",
auth_header=_auth_header,
gemini_api_key=None,
vertex_credentials=vertex_credentials,
vertex_project=vertex_project,
vertex_location=vertex_location,
stream=False,
custom_llm_provider="vertex_ai_beta",
api_base="",
)
headers = {
"Authorization": f"Bearer {auth_header}",
"Content-Type": "application/json",
}
url = None
if request_route == "/tuningJobs":
url = f"https://{vertex_location}-aiplatform.googleapis.com/v1/projects/{vertex_project}/locations/{vertex_location}/tuningJobs"
elif "/tuningJobs/" in request_route and "cancel" in request_route:
url = f"https://{vertex_location}-aiplatform.googleapis.com/v1/projects/{vertex_project}/locations/{vertex_location}/tuningJobs{request_route}"
elif "generateContent" in request_route:
url = f"https://{vertex_location}-aiplatform.googleapis.com/v1/projects/{vertex_project}/locations/{vertex_location}{request_route}"
elif "predict" in request_route:
url = f"https://{vertex_location}-aiplatform.googleapis.com/v1/projects/{vertex_project}/locations/{vertex_location}{request_route}"
elif "/batchPredictionJobs" in request_route:
url = f"https://{vertex_location}-aiplatform.googleapis.com/v1/projects/{vertex_project}/locations/{vertex_location}{request_route}"
elif "countTokens" in request_route:
url = f"https://{vertex_location}-aiplatform.googleapis.com/v1/projects/{vertex_project}/locations/{vertex_location}{request_route}"
elif "cachedContents" in request_route:
_model = request_data.get("model")
if _model is not None and "/publishers/google/models/" not in _model:
request_data["model"] = (
f"projects/{vertex_project}/locations/{vertex_location}/publishers/google/models/{_model}"
)
url = f"https://{vertex_location}-aiplatform.googleapis.com/v1beta1/projects/{vertex_project}/locations/{vertex_location}{request_route}"
else:
raise ValueError(f"Unsupported Vertex AI request route: {request_route}")
if self.async_handler is None:
raise ValueError("VertexAI Fine Tuning - async_handler is not initialized")
response = await self.async_handler.post(
headers=headers,
url=url,
json=request_data, # type: ignore
)
if response.status_code != 200:
raise Exception(
f"Error creating fine tuning job. Status code: {response.status_code}. Response: {response.text}"
)
response_json = response.json()
return response_json
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