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address greptile review feedback (greploop iteration 1)

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- Move trainingType injection to AzureOpenAIFineTuningAPI handler
- Guard normalization with is_azure flag to only apply to Azure responses
- Override acreate_fine_tuning_job in Azure handler to use is_azure=True
- Update test to directly test _ensure_training_type method
- Add test for OpenAI unchanged behavior

Made-with: Cursor
This commit is contained in:
Sameer Kankute 2026-03-23 12:26:14 +05:30
parent 9248ac8d7e
commit 230dcec902
5 changed files with 110 additions and 35 deletions
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9
litellm/fine_tuning/main.py
View file

@ -245,15 +245,6 @@ def create_fine_tuning_job(
)
# Azure OpenAI
elif custom_llm_provider == "azure":
# Azure requires trainingType (e.g. 1 = supervised). Omitting it yields a misleading
# "The specified base model does not support fine-tuning" error from the service.
if kwargs.get("trainingType") is None:
_eb = (
kwargs.get("extra_body") or optional_params.get("extra_body") or {}
)
if not (isinstance(_eb, dict) and _eb.get("trainingType") is not None):
kwargs["trainingType"] = 1
api_base = optional_params.api_base or litellm.api_base or get_secret_str("AZURE_API_BASE") # type: ignore
api_version = (

85
litellm/llms/azure/fine_tuning/handler.py
View file

@ -1,10 +1,15 @@
from typing import Optional, Union
from typing import Any, Coroutine, Dict, Optional, Union, cast
import httpx
from openai import AsyncAzureOpenAI, AsyncOpenAI, AzureOpenAI, OpenAI
from litellm._logging import verbose_logger
from litellm.llms.azure.common_utils import BaseAzureLLM
from litellm.llms.openai.fine_tuning.handler import OpenAIFineTuningAPI
from litellm.llms.openai.fine_tuning.handler import (
OpenAIFineTuningAPI,
_litellm_fine_tuning_job_from_response,
)
from litellm.types.utils import LiteLLMFineTuningJob
class AzureOpenAIFineTuningAPI(OpenAIFineTuningAPI, BaseAzureLLM):
@ -12,6 +17,82 @@ class AzureOpenAIFineTuningAPI(OpenAIFineTuningAPI, BaseAzureLLM):
AzureOpenAI methods to support fine tuning, inherits from OpenAIFineTuningAPI.
"""
@staticmethod
def _ensure_training_type(create_fine_tuning_job_data: Dict[str, Any]) -> None:
"""
Azure requires trainingType in extra_body. Default to 1 (supervised) if omitted.
"""
extra_body = create_fine_tuning_job_data.get("extra_body") or {}
if not isinstance(extra_body, dict):
extra_body = {}
if extra_body.get("trainingType") is None:
extra_body["trainingType"] = 1
create_fine_tuning_job_data["extra_body"] = extra_body
verbose_logger.debug(
"Azure fine-tuning: defaulting trainingType=1 (supervised)"
)
async def acreate_fine_tuning_job(
self,
create_fine_tuning_job_data: dict,
openai_client: Union[AsyncOpenAI, AsyncAzureOpenAI],
) -> LiteLLMFineTuningJob:
response = await openai_client.fine_tuning.jobs.create(
**create_fine_tuning_job_data
)
return _litellm_fine_tuning_job_from_response(response, is_azure=True)
def create_fine_tuning_job(
self,
_is_async: bool,
create_fine_tuning_job_data: dict,
api_key: Optional[str],
api_base: Optional[str],
api_version: Optional[str],
timeout: Union[float, httpx.Timeout],
max_retries: Optional[int],
organization: Optional[str],
client: Optional[
Union[OpenAI, AsyncOpenAI, AzureOpenAI, AsyncAzureOpenAI]
] = None,
) -> Union[LiteLLMFineTuningJob, Coroutine[Any, Any, LiteLLMFineTuningJob]]:
self._ensure_training_type(create_fine_tuning_job_data)
openai_client: Optional[
Union[OpenAI, AsyncOpenAI, AzureOpenAI, AsyncAzureOpenAI]
] = self.get_openai_client(
api_key=api_key,
api_base=api_base,
timeout=timeout,
max_retries=max_retries,
organization=organization,
client=client,
_is_async=_is_async,
api_version=api_version,
)
if openai_client is None:
raise ValueError(
"Azure OpenAI client is not initialized. Make sure api_key is passed or AZURE_API_KEY is set in the environment."
)
if _is_async is True:
if not isinstance(openai_client, (AsyncOpenAI, AsyncAzureOpenAI)):
raise ValueError(
"OpenAI client is not an instance of AsyncOpenAI. Make sure you passed an AsyncOpenAI client."
)
return self.acreate_fine_tuning_job(
create_fine_tuning_job_data=create_fine_tuning_job_data,
openai_client=openai_client,
)
verbose_logger.debug(
"creating fine tuning job, args= %s", create_fine_tuning_job_data
)
response = cast(OpenAI, openai_client).fine_tuning.jobs.create(
**create_fine_tuning_job_data
)
return _litellm_fine_tuning_job_from_response(response, is_azure=True)
def get_openai_client(
self,
api_key: Optional[str],

13
litellm/llms/openai/fine_tuning/handler.py
View file

@ -7,7 +7,9 @@ from litellm._logging import verbose_logger
from litellm.types.utils import LiteLLMFineTuningJob
def _normalize_fine_tuning_job_dict(data: Dict[str, Any]) -> Dict[str, Any]:
def _normalize_fine_tuning_job_dict(
data: Dict[str, Any], is_azure: bool = False
) -> Dict[str, Any]:
"""
Normalize Azure OpenAI FineTuningJob response to match OpenAI schema.
@ -16,6 +18,9 @@ def _normalize_fine_tuning_job_dict(data: Dict[str, Any]) -> Dict[str, Any]:
- result_files: null → []
- status: "pending" → "queued"
"""
if not is_azure:
return data
normalized = data.copy()
if normalized.get("organization_id") is None:
@ -30,9 +35,11 @@ def _normalize_fine_tuning_job_dict(data: Dict[str, Any]) -> Dict[str, Any]:
return normalized
def _litellm_fine_tuning_job_from_response(response: Any) -> LiteLLMFineTuningJob:
def _litellm_fine_tuning_job_from_response(
response: Any, is_azure: bool = False
) -> LiteLLMFineTuningJob:
return LiteLLMFineTuningJob(
**_normalize_fine_tuning_job_dict(response.model_dump())
**_normalize_fine_tuning_job_dict(response.model_dump(), is_azure=is_azure)
)

27
tests/batches_tests/test_fine_tuning_api.py
View file

@ -182,30 +182,17 @@ async def test_azure_create_fine_tune_jobs_async():
def test_azure_trainingtype_defaults_to_one():
"""
Azure requires trainingType in extra_body. When omitted, LiteLLM defaults it to 1.
Azure requires trainingType in extra_body. When omitted, AzureOpenAIFineTuningAPI defaults it to 1.
"""
from unittest.mock import MagicMock, patch
from litellm.fine_tuning.main import create_fine_tuning_job
from litellm.llms.azure.fine_tuning.handler import AzureOpenAIFineTuningAPI
with patch(
"litellm.fine_tuning.main.azure_fine_tuning_apis_instance.create_fine_tuning_job"
) as mock_create:
mock_create.return_value = MagicMock(
id="ftjob-test", status="queued", model="gpt-4o-mini"
)
handler = AzureOpenAIFineTuningAPI()
create_data = {"model": "gpt-4o-mini", "training_file": "file-test"}
create_fine_tuning_job(
model="gpt-4o-mini",
training_file="file-test",
custom_llm_provider="azure",
api_base="https://test.openai.azure.com",
api_key="test-key",
)
handler._ensure_training_type(create_data)
call_kwargs = mock_create.call_args[1]
create_data = call_kwargs["create_fine_tuning_job_data"]
assert "extra_body" in create_data
assert create_data["extra_body"].get("trainingType") == 1
assert "extra_body" in create_data
assert create_data["extra_body"]["trainingType"] == 1
@pytest.mark.asyncio()

11
tests/test_litellm/types/llms/test_types_llms_openai.py
View file

@ -424,8 +424,17 @@ def test_normalize_fine_tuning_job_dict_maps_azure_pending():
from litellm.llms.openai.fine_tuning.handler import _normalize_fine_tuning_job_dict
out = _normalize_fine_tuning_job_dict(
{"organization_id": None, "result_files": None, "status": "pending"}
{"organization_id": None, "result_files": None, "status": "pending"},
is_azure=True,
)
assert out["organization_id"] == ""
assert out["result_files"] == []
assert out["status"] == "queued"
def test_normalize_fine_tuning_job_dict_openai_unchanged():
from litellm.llms.openai.fine_tuning.handler import _normalize_fine_tuning_job_dict
data = {"organization_id": None, "result_files": None, "status": "pending"}
out = _normalize_fine_tuning_job_dict(data, is_azure=False)
assert out is data