fix(cost_calculator.py): fixes tgai unmapped model pricing

Fixes error where tgai helper function returned None. Enforces stronger type hints, refactors code, adds more unit testing.

litellm/__init__.py

@@ -709,6 +709,7 @@ all_embedding_models = (
 openai_image_generation_models = ["dall-e-2", "dall-e-3"]
 
 from .timeout import timeout
+from .cost_calculator import completion_cost
 from .utils import (
     client,
     exception_type,
@@ -718,7 +719,6 @@ from .utils import (
     create_pretrained_tokenizer,
     create_tokenizer,
     cost_per_token,
-    completion_cost,
     supports_function_calling,
     supports_parallel_function_calling,
     supports_vision,

litellm/cost_calculator.py

@@ -1,6 +1,7 @@
 # What is this?
 ## File for 'response_cost' calculation in Logging
-from typing import Optional, Union, Literal
+from typing import Optional, Union, Literal, List
+import litellm._logging
 from litellm.utils import (
     ModelResponse,
     EmbeddingResponse,
@@ -8,10 +9,279 @@ from litellm.utils import (
     TranscriptionResponse,
     TextCompletionResponse,
     CallTypes,
-    completion_cost,
+    cost_per_token,
     print_verbose,
+    CostPerToken,
+    token_counter,
 )
 import litellm
+from litellm import verbose_logger
+
+
+# Extract the number of billion parameters from the model name
+# only used for together_computer LLMs
+def get_model_params_and_category(model_name) -> str:
+    """
+    Helper function for calculating together ai pricing.
+
+    Returns
+    - str - model pricing category if mapped else received model name
+    """
+    import re
+
+    model_name = model_name.lower()
+    re_params_match = re.search(
+        r"(\d+b)", model_name
+    )  # catch all decimals like 3b, 70b, etc
+    category = None
+    if re_params_match is not None:
+        params_match = str(re_params_match.group(1))
+        params_match = params_match.replace("b", "")
+        if params_match is not None:
+            params_billion = float(params_match)
+        else:
+            return model_name
+        # Determine the category based on the number of parameters
+        if params_billion <= 3.0:
+            category = "together-ai-up-to-3b"
+        elif params_billion <= 7.0:
+            category = "together-ai-3.1b-7b"
+        elif params_billion <= 20.0:
+            category = "together-ai-7.1b-20b"
+        elif params_billion <= 40.0:
+            category = "together-ai-20.1b-40b"
+        elif params_billion <= 70.0:
+            category = "together-ai-40.1b-70b"
+        if category is not None:
+            return category
+
+    return model_name
+
+
+def get_replicate_completion_pricing(completion_response=None, total_time=0.0):
+    # see https://replicate.com/pricing
+    # for all litellm currently supported LLMs, almost all requests go to a100_80gb
+    a100_80gb_price_per_second_public = (
+        0.001400  # assume all calls sent to A100 80GB for now
+    )
+    if total_time == 0.0:  # total time is in ms
+        start_time = completion_response["created"]
+        end_time = getattr(completion_response, "ended", time.time())
+        total_time = end_time - start_time
+
+    return a100_80gb_price_per_second_public * total_time / 1000
+
+
+def completion_cost(
+    completion_response=None,
+    model: Optional[str] = None,
+    prompt="",
+    messages: List = [],
+    completion="",
+    total_time=0.0,  # used for replicate, sagemaker
+    call_type: Literal[
+        "embedding",
+        "aembedding",
+        "completion",
+        "acompletion",
+        "atext_completion",
+        "text_completion",
+        "image_generation",
+        "aimage_generation",
+        "moderation",
+        "amoderation",
+        "atranscription",
+        "transcription",
+        "aspeech",
+        "speech",
+    ] = "completion",
+    ### REGION ###
+    custom_llm_provider=None,
+    region_name=None,  # used for bedrock pricing
+    ### IMAGE GEN ###
+    size=None,
+    quality=None,
+    n=None,  # number of images
+    ### CUSTOM PRICING ###
+    custom_cost_per_token: Optional[CostPerToken] = None,
+    custom_cost_per_second: Optional[float] = None,
+) -> float:
+    """
+    Calculate the cost of a given completion call fot GPT-3.5-turbo, llama2, any litellm supported llm.
+
+    Parameters:
+        completion_response (litellm.ModelResponses): [Required] The response received from a LiteLLM completion request.
+
+        [OPTIONAL PARAMS]
+        model (str): Optional. The name of the language model used in the completion calls
+        prompt (str): Optional. The input prompt passed to the llm
+        completion (str): Optional. The output completion text from the llm
+        total_time (float): Optional. (Only used for Replicate LLMs) The total time used for the request in seconds
+        custom_cost_per_token: Optional[CostPerToken]: the cost per input + output token for the llm api call.
+        custom_cost_per_second: Optional[float]: the cost per second for the llm api call.
+
+    Returns:
+        float: The cost in USD dollars for the completion based on the provided parameters.
+
+    Exceptions:
+        Raises exception if model not in the litellm model cost map. Register model, via custom pricing or PR - https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json
+
+
+    Note:
+        - If completion_response is provided, the function extracts token information and the model name from it.
+        - If completion_response is not provided, the function calculates token counts based on the model and input text.
+        - The cost is calculated based on the model, prompt tokens, and completion tokens.
+        - For certain models containing "togethercomputer" in the name, prices are based on the model size.
+        - For un-mapped Replicate models, the cost is calculated based on the total time used for the request.
+    """
+    try:
+        if (
+            (call_type == "aimage_generation" or call_type == "image_generation")
+            and model is not None
+            and isinstance(model, str)
+            and len(model) == 0
+            and custom_llm_provider == "azure"
+        ):
+            model = "dall-e-2"  # for dall-e-2, azure expects an empty model name
+        # Handle Inputs to completion_cost
+        prompt_tokens = 0
+        completion_tokens = 0
+        custom_llm_provider = None
+        if completion_response is not None:
+            # get input/output tokens from completion_response
+            prompt_tokens = completion_response.get("usage", {}).get("prompt_tokens", 0)
+            completion_tokens = completion_response.get("usage", {}).get(
+                "completion_tokens", 0
+            )
+            total_time = completion_response.get("_response_ms", 0)
+            verbose_logger.debug(
+                f"completion_response response ms: {completion_response.get('_response_ms')} "
+            )
+            model = model or completion_response.get(
+                "model", None
+            )  # check if user passed an override for model, if it's none check completion_response['model']
+            if hasattr(completion_response, "_hidden_params"):
+                if (
+                    completion_response._hidden_params.get("model", None) is not None
+                    and len(completion_response._hidden_params["model"]) > 0
+                ):
+                    model = completion_response._hidden_params.get("model", model)
+                custom_llm_provider = completion_response._hidden_params.get(
+                    "custom_llm_provider", ""
+                )
+                region_name = completion_response._hidden_params.get(
+                    "region_name", region_name
+                )
+                size = completion_response._hidden_params.get(
+                    "optional_params", {}
+                ).get(
+                    "size", "1024-x-1024"
+                )  # openai default
+                quality = completion_response._hidden_params.get(
+                    "optional_params", {}
+                ).get(
+                    "quality", "standard"
+                )  # openai default
+                n = completion_response._hidden_params.get("optional_params", {}).get(
+                    "n", 1
+                )  # openai default
+        else:
+            if len(messages) > 0:
+                prompt_tokens = token_counter(model=model, messages=messages)
+            elif len(prompt) > 0:
+                prompt_tokens = token_counter(model=model, text=prompt)
+            completion_tokens = token_counter(model=model, text=completion)
+        if model is None:
+            raise ValueError(
+                f"Model is None and does not exist in passed completion_response. Passed completion_response={completion_response}, model={model}"
+            )
+
+        if (
+            call_type == CallTypes.image_generation.value
+            or call_type == CallTypes.aimage_generation.value
+        ):
+            ### IMAGE GENERATION COST CALCULATION ###
+            if custom_llm_provider == "vertex_ai":
+                # https://cloud.google.com/vertex-ai/generative-ai/pricing
+                # Vertex Charges Flat $0.20 per image
+                return 0.020
+
+            # fix size to match naming convention
+            if "x" in size and "-x-" not in size:
+                size = size.replace("x", "-x-")
+            image_gen_model_name = f"{size}/{model}"
+            image_gen_model_name_with_quality = image_gen_model_name
+            if quality is not None:
+                image_gen_model_name_with_quality = f"{quality}/{image_gen_model_name}"
+            size = size.split("-x-")
+            height = int(size[0])  # if it's 1024-x-1024 vs. 1024x1024
+            width = int(size[1])
+            verbose_logger.debug(f"image_gen_model_name: {image_gen_model_name}")
+            verbose_logger.debug(
+                f"image_gen_model_name_with_quality: {image_gen_model_name_with_quality}"
+            )
+            if image_gen_model_name in litellm.model_cost:
+                return (
+                    litellm.model_cost[image_gen_model_name]["input_cost_per_pixel"]
+                    * height
+                    * width
+                    * n
+                )
+            elif image_gen_model_name_with_quality in litellm.model_cost:
+                return (
+                    litellm.model_cost[image_gen_model_name_with_quality][
+                        "input_cost_per_pixel"
+                    ]
+                    * height
+                    * width
+                    * n
+                )
+            else:
+                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
+            or "together_ai" in model
+            or custom_llm_provider == "together_ai"
+        ):
+            # together ai prices based on size of llm
+            # get_model_params_and_category takes a model name and returns the category of LLM size it is in model_prices_and_context_window.json
+            model = get_model_params_and_category(model)
+        # replicate llms are calculate based on time for request running
+        # see https://replicate.com/pricing
+        elif (
+            model in litellm.replicate_models or "replicate" in model
+        ) and model not in litellm.model_cost:
+            # for unmapped replicate model, default to replicate's time tracking logic
+            return get_replicate_completion_pricing(completion_response, total_time)
+
+        if model is None:
+            raise ValueError(
+                f"Model is None and does not exist in passed completion_response. Passed completion_response={completion_response}, model={model}"
+            )
+
+        (
+            prompt_tokens_cost_usd_dollar,
+            completion_tokens_cost_usd_dollar,
+        ) = cost_per_token(
+            model=model,
+            prompt_tokens=prompt_tokens,
+            completion_tokens=completion_tokens,
+            custom_llm_provider=custom_llm_provider,
+            response_time_ms=total_time,
+            region_name=region_name,
+            custom_cost_per_second=custom_cost_per_second,
+            custom_cost_per_token=custom_cost_per_token,
+        )
+        _final_cost = prompt_tokens_cost_usd_dollar + completion_tokens_cost_usd_dollar
+        print_verbose(
+            f"final cost: {_final_cost}; prompt_tokens_cost_usd_dollar: {prompt_tokens_cost_usd_dollar}; completion_tokens_cost_usd_dollar: {completion_tokens_cost_usd_dollar}"
+        )
+        return _final_cost
+    except Exception as e:
+        raise e
 
 
 def response_cost_calculator(
@@ -47,7 +317,7 @@ def response_cost_calculator(
 ) -> Optional[float]:
     try:
         response_cost: float = 0.0
-        if cache_hit is not None and cache_hit == True:
+        if cache_hit is not None and cache_hit is True:
             response_cost = 0.0
         else:
             response_object._hidden_params["optional_params"] = optional_params
@@ -62,9 +332,11 @@ def response_cost_calculator(
                 if (
                     model in litellm.model_cost
                     and custom_pricing is not None
-                    and custom_llm_provider == True
+                    and custom_llm_provider is True
                 ):  # override defaults if custom pricing is set
                     base_model = model
+                elif base_model is None:
+                    base_model = model
                 # base_model defaults to None if not set on model_info
                 response_cost = completion_cost(
                     completion_response=response_object,

litellm/tests/test_completion_cost.py

@@ -517,3 +517,52 @@ def test_groq_response_cost_tracking(is_streaming):
     assert response_cost > 0.0
 
     print(f"response_cost: {response_cost}")
+
+
+def test_together_ai_qwen_completion_cost():
+    input_kwargs = {
+        "completion_response": litellm.ModelResponse(
+            **{
+                "id": "890db0c33c4ef94b-SJC",
+                "choices": [
+                    {
+                        "finish_reason": "eos",
+                        "index": 0,
+                        "message": {
+                            "content": "I am Qwen, a large language model created by Alibaba Cloud.",
+                            "role": "assistant",
+                        },
+                    }
+                ],
+                "created": 1717900130,
+                "model": "together_ai/qwen/Qwen2-72B-Instruct",
+                "object": "chat.completion",
+                "system_fingerprint": None,
+                "usage": {
+                    "completion_tokens": 15,
+                    "prompt_tokens": 23,
+                    "total_tokens": 38,
+                },
+            }
+        ),
+        "model": "qwen/Qwen2-72B-Instruct",
+        "prompt": "",
+        "messages": [],
+        "completion": "",
+        "total_time": 0.0,
+        "call_type": "completion",
+        "custom_llm_provider": "together_ai",
+        "region_name": None,
+        "size": None,
+        "quality": None,
+        "n": None,
+        "custom_cost_per_token": None,
+        "custom_cost_per_second": None,
+    }
+
+    try:
+        litellm.completion_cost(**input_kwargs)
+    except litellm.NotFoundError:
+        pass
+    except Exception:
+        pytest.fail("This should have returned a 'not found error'")

litellm/utils.py

@@ -3929,54 +3929,6 @@ def client(original_function):
         return wrapper
 
 
-####### USAGE CALCULATOR ################
-
-
-# Extract the number of billion parameters from the model name
-# only used for together_computer LLMs
-def get_model_params_and_category(model_name):
-    import re
-
-    model_name = model_name.lower()
-    params_match = re.search(
-        r"(\d+b)", model_name
-    )  # catch all decimals like 3b, 70b, etc
-    category = None
-    if params_match != None:
-        params_match = params_match.group(1)
-        params_match = params_match.replace("b", "")
-        params_billion = float(params_match)
-        # Determine the category based on the number of parameters
-        if params_billion <= 3.0:
-            category = "together-ai-up-to-3b"
-        elif params_billion <= 7.0:
-            category = "together-ai-3.1b-7b"
-        elif params_billion <= 20.0:
-            category = "together-ai-7.1b-20b"
-        elif params_billion <= 40.0:
-            category = "together-ai-20.1b-40b"
-        elif params_billion <= 70.0:
-            category = "together-ai-40.1b-70b"
-        return category
-
-    return None
-
-
-def get_replicate_completion_pricing(completion_response=None, total_time=0.0):
-    # see https://replicate.com/pricing
-    a100_40gb_price_per_second_public = 0.001150
-    # for all litellm currently supported LLMs, almost all requests go to a100_80gb
-    a100_80gb_price_per_second_public = (
-        0.001400  # assume all calls sent to A100 80GB for now
-    )
-    if total_time == 0.0:  # total time is in ms
-        start_time = completion_response["created"]
-        end_time = getattr(completion_response, "ended", time.time())
-        total_time = end_time - start_time
-
-    return a100_80gb_price_per_second_public * total_time / 1000
-
-
 @lru_cache(maxsize=128)
 def _select_tokenizer(model: str):
     if model in litellm.cohere_models and "command-r" in model:
@@ -4363,7 +4315,7 @@ def _cost_per_token_custom_pricing_helper(
 
 
 def cost_per_token(
-    model="",
+    model: str = "",
     prompt_tokens=0,
     completion_tokens=0,
     response_time_ms=None,
@@ -4388,6 +4340,8 @@ def cost_per_token(
     Returns:
         tuple: A tuple containing the cost in USD dollars for prompt tokens and completion tokens, respectively.
     """
+    if model is None:
+        raise Exception("Invalid arg. Model cannot be none.")
     ## CUSTOM PRICING ##
     response_cost = _cost_per_token_custom_pricing_helper(
         prompt_tokens=prompt_tokens,
@@ -4560,213 +4514,6 @@ def cost_per_token(
         )
 
 
-def completion_cost(
-    completion_response=None,
-    model=None,
-    prompt="",
-    messages: List = [],
-    completion="",
-    total_time=0.0,  # used for replicate, sagemaker
-    call_type: Literal[
-        "embedding",
-        "aembedding",
-        "completion",
-        "acompletion",
-        "atext_completion",
-        "text_completion",
-        "image_generation",
-        "aimage_generation",
-        "moderation",
-        "amoderation",
-        "atranscription",
-        "transcription",
-        "aspeech",
-        "speech",
-    ] = "completion",
-    ### REGION ###
-    custom_llm_provider=None,
-    region_name=None,  # used for bedrock pricing
-    ### IMAGE GEN ###
-    size=None,
-    quality=None,
-    n=None,  # number of images
-    ### CUSTOM PRICING ###
-    custom_cost_per_token: Optional[CostPerToken] = None,
-    custom_cost_per_second: Optional[float] = None,
-) -> float:
-    """
-    Calculate the cost of a given completion call fot GPT-3.5-turbo, llama2, any litellm supported llm.
-
-    Parameters:
-        completion_response (litellm.ModelResponses): [Required] The response received from a LiteLLM completion request.
-
-        [OPTIONAL PARAMS]
-        model (str): Optional. The name of the language model used in the completion calls
-        prompt (str): Optional. The input prompt passed to the llm
-        completion (str): Optional. The output completion text from the llm
-        total_time (float): Optional. (Only used for Replicate LLMs) The total time used for the request in seconds
-        custom_cost_per_token: Optional[CostPerToken]: the cost per input + output token for the llm api call.
-        custom_cost_per_second: Optional[float]: the cost per second for the llm api call.
-
-    Returns:
-        float: The cost in USD dollars for the completion based on the provided parameters.
-
-    Exceptions:
-        Raises exception if model not in the litellm model cost map. Register model, via custom pricing or PR - https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json
-
-
-    Note:
-        - If completion_response is provided, the function extracts token information and the model name from it.
-        - If completion_response is not provided, the function calculates token counts based on the model and input text.
-        - The cost is calculated based on the model, prompt tokens, and completion tokens.
-        - For certain models containing "togethercomputer" in the name, prices are based on the model size.
-        - For un-mapped Replicate models, the cost is calculated based on the total time used for the request.
-    """
-    try:
-        if (
-            (call_type == "aimage_generation" or call_type == "image_generation")
-            and model is not None
-            and isinstance(model, str)
-            and len(model) == 0
-            and custom_llm_provider == "azure"
-        ):
-            model = "dall-e-2"  # for dall-e-2, azure expects an empty model name
-        # Handle Inputs to completion_cost
-        prompt_tokens = 0
-        completion_tokens = 0
-        custom_llm_provider = None
-        if completion_response is not None:
-            # get input/output tokens from completion_response
-            prompt_tokens = completion_response.get("usage", {}).get("prompt_tokens", 0)
-            completion_tokens = completion_response.get("usage", {}).get(
-                "completion_tokens", 0
-            )
-            total_time = completion_response.get("_response_ms", 0)
-            verbose_logger.debug(
-                f"completion_response response ms: {completion_response.get('_response_ms')} "
-            )
-            model = model or completion_response.get(
-                "model", None
-            )  # check if user passed an override for model, if it's none check completion_response['model']
-            if hasattr(completion_response, "_hidden_params"):
-                if (
-                    completion_response._hidden_params.get("model", None) is not None
-                    and len(completion_response._hidden_params["model"]) > 0
-                ):
-                    model = completion_response._hidden_params.get("model", model)
-                custom_llm_provider = completion_response._hidden_params.get(
-                    "custom_llm_provider", ""
-                )
-                region_name = completion_response._hidden_params.get(
-                    "region_name", region_name
-                )
-                size = completion_response._hidden_params.get(
-                    "optional_params", {}
-                ).get(
-                    "size", "1024-x-1024"
-                )  # openai default
-                quality = completion_response._hidden_params.get(
-                    "optional_params", {}
-                ).get(
-                    "quality", "standard"
-                )  # openai default
-                n = completion_response._hidden_params.get("optional_params", {}).get(
-                    "n", 1
-                )  # openai default
-        else:
-            if len(messages) > 0:
-                prompt_tokens = token_counter(model=model, messages=messages)
-            elif len(prompt) > 0:
-                prompt_tokens = token_counter(model=model, text=prompt)
-            completion_tokens = token_counter(model=model, text=completion)
-        if model == None:
-            raise ValueError(
-                f"Model is None and does not exist in passed completion_response. Passed completion_response={completion_response}, model={model}"
-            )
-
-        if (
-            call_type == CallTypes.image_generation.value
-            or call_type == CallTypes.aimage_generation.value
-        ):
-            ### IMAGE GENERATION COST CALCULATION ###
-            if custom_llm_provider == "vertex_ai":
-                # https://cloud.google.com/vertex-ai/generative-ai/pricing
-                # Vertex Charges Flat $0.20 per image
-                return 0.020
-
-            # fix size to match naming convention
-            if "x" in size and "-x-" not in size:
-                size = size.replace("x", "-x-")
-            image_gen_model_name = f"{size}/{model}"
-            image_gen_model_name_with_quality = image_gen_model_name
-            if quality is not None:
-                image_gen_model_name_with_quality = f"{quality}/{image_gen_model_name}"
-            size = size.split("-x-")
-            height = int(size[0])  # if it's 1024-x-1024 vs. 1024x1024
-            width = int(size[1])
-            verbose_logger.debug(f"image_gen_model_name: {image_gen_model_name}")
-            verbose_logger.debug(
-                f"image_gen_model_name_with_quality: {image_gen_model_name_with_quality}"
-            )
-            if image_gen_model_name in litellm.model_cost:
-                return (
-                    litellm.model_cost[image_gen_model_name]["input_cost_per_pixel"]
-                    * height
-                    * width
-                    * n
-                )
-            elif image_gen_model_name_with_quality in litellm.model_cost:
-                return (
-                    litellm.model_cost[image_gen_model_name_with_quality][
-                        "input_cost_per_pixel"
-                    ]
-                    * height
-                    * width
-                    * n
-                )
-            else:
-                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
-            or "together_ai" in model
-            or custom_llm_provider == "together_ai"
-        ):
-            # together ai prices based on size of llm
-            # get_model_params_and_category takes a model name and returns the category of LLM size it is in model_prices_and_context_window.json
-            model = get_model_params_and_category(model)
-        # replicate llms are calculate based on time for request running
-        # see https://replicate.com/pricing
-        elif (
-            model in litellm.replicate_models or "replicate" in model
-        ) and model not in litellm.model_cost:
-            # for unmapped replicate model, default to replicate's time tracking logic
-            return get_replicate_completion_pricing(completion_response, total_time)
-
-        (
-            prompt_tokens_cost_usd_dollar,
-            completion_tokens_cost_usd_dollar,
-        ) = cost_per_token(
-            model=model,
-            prompt_tokens=prompt_tokens,
-            completion_tokens=completion_tokens,
-            custom_llm_provider=custom_llm_provider,
-            response_time_ms=total_time,
-            region_name=region_name,
-            custom_cost_per_second=custom_cost_per_second,
-            custom_cost_per_token=custom_cost_per_token,
-        )
-        _final_cost = prompt_tokens_cost_usd_dollar + completion_tokens_cost_usd_dollar
-        print_verbose(
-            f"final cost: {_final_cost}; prompt_tokens_cost_usd_dollar: {prompt_tokens_cost_usd_dollar}; completion_tokens_cost_usd_dollar: {completion_tokens_cost_usd_dollar}"
-        )
-        return _final_cost
-    except Exception as e:
-        raise e
-
-
 def supports_httpx_timeout(custom_llm_provider: str) -> bool:
     """
     Helper function to know if a provider implementation supports httpx timeout