Merge pull request #2475 from BerriAI/litellm_azure_dall_e_3_cost_tracking

fix(azure.py): support cost tracking for azure/dall-e-3

litellm/llms/azure.py

@@ -715,6 +715,16 @@ class AzureChatCompletion(BaseLLM):
                 model = model
             else:
                 model = None
+
+            ## BASE MODEL CHECK
+            if (
+                model_response is not None
+                and optional_params.get("base_model", None) is not None
+            ):
+                model_response._hidden_params["model"] = optional_params.pop(
+                    "base_model"
+                )
+
             data = {"model": model, "prompt": prompt, **optional_params}
             max_retries = data.pop("max_retries", 2)
             if not isinstance(max_retries, int):

litellm/tests/test_completion_cost.py

@@ -297,3 +297,34 @@ def test_whisper_azure():
         5,
     )
     assert cost == expected_cost
+
+
+def test_dalle_3_azure_cost_tracking():
+    litellm.set_verbose = True
+    # model = "azure/dall-e-3-test"
+    # response = litellm.image_generation(
+    #     model=model,
+    #     prompt="A cute baby sea otter",
+    #     api_version="2023-12-01-preview",
+    #     api_base=os.getenv("AZURE_SWEDEN_API_BASE"),
+    #     api_key=os.getenv("AZURE_SWEDEN_API_KEY"),
+    #     base_model="dall-e-3",
+    # )
+    # print(f"response: {response}")
+    response = litellm.ImageResponse(
+        created=1710265780,
+        data=[
+            {
+                "b64_json": None,
+                "revised_prompt": "A close-up image of an adorable baby sea otter. Its fur is thick and fluffy to provide buoyancy and insulation against the cold water. Its eyes are round, curious and full of life. It's lying on its back, floating effortlessly on the calm sea surface under the warm sun. Surrounding the otter are patches of colorful kelp drifting along the gentle waves, giving the scene a touch of vibrancy. The sea otter has its small paws folded on its chest, and it seems to be taking a break from its play.",
+                "url": "https://dalleprodsec.blob.core.windows.net/private/images/3e5d00f3-700e-4b75-869d-2de73c3c975d/generated_00.png?se=2024-03-13T17%3A49%3A51Z&sig=R9RJD5oOSe0Vp9Eg7ze%2FZ8QR7ldRyGH6XhMxiau16Jc%3D&ske=2024-03-19T11%3A08%3A03Z&skoid=e52d5ed7-0657-4f62-bc12-7e5dbb260a96&sks=b&skt=2024-03-12T11%3A08%3A03Z&sktid=33e01921-4d64-4f8c-a055-5bdaffd5e33d&skv=2020-10-02&sp=r&spr=https&sr=b&sv=2020-10-02",
+            }
+        ],
+    )
+    response.usage = {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0}
+    response._hidden_params = {"model": "dall-e-3", "model_id": None}
+    print(f"response hidden params: {response._hidden_params}")
+    cost = litellm.completion_cost(
+        completion_response=response, call_type="image_generation"
+    )
+    assert cost > 0

litellm/utils.py

@@ -3841,7 +3841,9 @@ def completion_cost(
                     * n
                 )
             else:
-                raise Exception(f"Model={model} not found in completion cost model map")
+                raise Exception(
+                    f"Model={image_gen_model_name} not found in completion cost model map"
+                )
         # Calculate cost based on prompt_tokens, completion_tokens
         if (
             "togethercomputer" in model