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TestOpenAIGPTImage1

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This commit is contained in:
Ishaan Jaff 2025-04-23 12:57:58 -07:00
parent 36ee132514
commit 233d1d5303
5 changed files with 59 additions and 38 deletions
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16
litellm/cost_calculator.py
View file

@ -57,6 +57,7 @@ from litellm.llms.vertex_ai.image_generation.cost_calculator import (
from litellm.responses.utils import ResponseAPILoggingUtils
from litellm.types.llms.openai import (
HttpxBinaryResponseContent,
ImageGenerationRequestQuality,
OpenAIRealtimeStreamList,
OpenAIRealtimeStreamResponseBaseObject,
OpenAIRealtimeStreamSessionEvents,
@ -642,9 +643,9 @@ def completion_cost( # noqa: PLR0915
or isinstance(completion_response, dict)
): # tts returns a custom class
if isinstance(completion_response, dict):
usage_obj: Optional[
Union[dict, Usage]
] = completion_response.get("usage", {})
usage_obj: Optional[Union[dict, Usage]] = (
completion_response.get("usage", {})
)
else:
usage_obj = getattr(completion_response, "usage", {})
if isinstance(usage_obj, BaseModel) and not _is_known_usage_objects(
@ -913,7 +914,7 @@ def completion_cost( # noqa: PLR0915
def get_response_cost_from_hidden_params(
hidden_params: Union[dict, BaseModel]
hidden_params: Union[dict, BaseModel],
) -> Optional[float]:
if isinstance(hidden_params, BaseModel):
_hidden_params_dict = hidden_params.model_dump()
@ -1101,6 +1102,11 @@ def default_image_cost_calculator(
f"{quality}/{base_model_name}" if quality else base_model_name
)
# gpt-image-1 models use low, medium, high quality. If user did not specify quality, use medium fot gpt-image-1 model family
model_name_with_v2_quality = (
f"{ImageGenerationRequestQuality.MEDIUM}/{base_model_name}"
)
verbose_logger.debug(
f"Looking up cost for models: {model_name_with_quality}, {base_model_name}"
)
@ -1110,6 +1116,8 @@ def default_image_cost_calculator(
cost_info = litellm.model_cost[model_name_with_quality]
elif base_model_name in litellm.model_cost:
cost_info = litellm.model_cost[base_model_name]
elif model_name_with_v2_quality in litellm.model_cost:
cost_info = litellm.model_cost[model_name_with_v2_quality]
else:
# Try without provider prefix
model_without_provider = f"{size_str}/{model.split('/')[-1]}"

27
litellm/main.py
View file

@ -182,6 +182,7 @@ from .types.llms.openai import (
ChatCompletionPredictionContentParam,
ChatCompletionUserMessage,
HttpxBinaryResponseContent,
ImageGenerationRequestQuality,
)
from .types.utils import (
LITELLM_IMAGE_VARIATION_PROVIDERS,
@ -2688,9 +2689,9 @@ def completion( # type: ignore # noqa: PLR0915
"aws_region_name" not in optional_params
or optional_params["aws_region_name"] is None
):
optional_params[
"aws_region_name"
] = aws_bedrock_client.meta.region_name
optional_params["aws_region_name"] = (
aws_bedrock_client.meta.region_name
)
bedrock_route = BedrockModelInfo.get_bedrock_route(model)
if bedrock_route == "converse":
@ -4412,9 +4413,9 @@ def adapter_completion(
new_kwargs = translation_obj.translate_completion_input_params(kwargs=kwargs)
response: Union[ModelResponse, CustomStreamWrapper] = completion(**new_kwargs) # type: ignore
translated_response: Optional[
Union[BaseModel, AdapterCompletionStreamWrapper]
] = None
translated_response: Optional[Union[BaseModel, AdapterCompletionStreamWrapper]] = (
None
)
if isinstance(response, ModelResponse):
translated_response = translation_obj.translate_completion_output_params(
response=response
@ -4567,7 +4568,7 @@ def image_generation( # noqa: PLR0915
prompt: str,
model: Optional[str] = None,
n: Optional[int] = None,
quality: Optional[str] = None,
quality: Optional[Union[str, ImageGenerationRequestQuality]] = None,
response_format: Optional[str] = None,
size: Optional[str] = None,
style: Optional[str] = None,
@ -5834,9 +5835,9 @@ def stream_chunk_builder( # noqa: PLR0915
]
if len(content_chunks) > 0:
response["choices"][0]["message"][
"content"
] = processor.get_combined_content(content_chunks)
response["choices"][0]["message"]["content"] = (
processor.get_combined_content(content_chunks)
)
reasoning_chunks = [
chunk
@ -5847,9 +5848,9 @@ def stream_chunk_builder( # noqa: PLR0915
]
if len(reasoning_chunks) > 0:
response["choices"][0]["message"][
"reasoning_content"
] = processor.get_combined_reasoning_content(reasoning_chunks)
response["choices"][0]["message"]["reasoning_content"] = (
processor.get_combined_reasoning_content(reasoning_chunks)
)
audio_chunks = [
chunk

45
litellm/types/llms/openai.py
View file

@ -824,12 +824,12 @@ class OpenAIChatCompletionChunk(ChatCompletionChunk):
class Hyperparameters(BaseModel):
batch_size: Optional[Union[str, int]] = None # "Number of examples in each batch."
learning_rate_multiplier: Optional[
Union[str, float]
] = None # Scaling factor for the learning rate
n_epochs: Optional[
Union[str, int]
] = None # "The number of epochs to train the model for"
learning_rate_multiplier: Optional[Union[str, float]] = (
None # Scaling factor for the learning rate
)
n_epochs: Optional[Union[str, int]] = (
None # "The number of epochs to train the model for"
)
class FineTuningJobCreate(BaseModel):
@ -856,18 +856,18 @@ class FineTuningJobCreate(BaseModel):
model: str # "The name of the model to fine-tune."
training_file: str # "The ID of an uploaded file that contains training data."
hyperparameters: Optional[
Hyperparameters
] = None # "The hyperparameters used for the fine-tuning job."
suffix: Optional[
str
] = None # "A string of up to 18 characters that will be added to your fine-tuned model name."
validation_file: Optional[
str
] = None # "The ID of an uploaded file that contains validation data."
integrations: Optional[
List[str]
] = None # "A list of integrations to enable for your fine-tuning job."
hyperparameters: Optional[Hyperparameters] = (
None # "The hyperparameters used for the fine-tuning job."
)
suffix: Optional[str] = (
None # "A string of up to 18 characters that will be added to your fine-tuned model name."
)
validation_file: Optional[str] = (
None # "The ID of an uploaded file that contains validation data."
)
integrations: Optional[List[str]] = (
None # "A list of integrations to enable for your fine-tuning job."
)
seed: Optional[int] = None # "The seed controls the reproducibility of the job."
@ -1259,3 +1259,12 @@ class OpenAIRealtimeStreamResponseBaseObject(TypedDict):
OpenAIRealtimeStreamList = List[
Union[OpenAIRealtimeStreamResponseBaseObject, OpenAIRealtimeStreamSessionEvents]
]
class ImageGenerationRequestQuality(str, Enum):
LOW = "low"
MEDIUM = "medium"
HIGH = "high"
AUTO = "auto"
STANDARD = "standard"
HD = "hd"

6
tests/image_gen_tests/base_image_generation_test.py
View file

@ -66,8 +66,8 @@ class BaseImageGenTest(ABC):
logged_standard_logging_payload = custom_logger.standard_logging_payload
print("logged_standard_logging_payload", logged_standard_logging_payload)
assert logged_standard_logging_payload is not None
# assert logged_standard_logging_payload["response_cost"] is not None
# assert logged_standard_logging_payload["response_cost"] > 0
assert logged_standard_logging_payload["response_cost"] is not None
assert logged_standard_logging_payload["response_cost"] > 0
from openai.types.images_response import ImagesResponse
@ -85,4 +85,4 @@ class BaseImageGenTest(ABC):
if "Your task failed as a result of our safety system." in str(e):
pass
else:
pytest.fail(f"An exception occurred - {str(e)}")
pytest.fail(f"An exception occurred - {str(e)}")

3
tests/image_gen_tests/test_image_generation.py
View file

@ -161,6 +161,9 @@ class TestOpenAIDalle3(BaseImageGenTest):
def get_base_image_generation_call_args(self) -> dict:
return {"model": "dall-e-3"}
class TestOpenAIGPTImage1(BaseImageGenTest):
def get_base_image_generation_call_args(self) -> dict:
return {"model": "gpt-image-1"}
class TestAzureOpenAIDalle3(BaseImageGenTest):
def get_base_image_generation_call_args(self) -> dict: