add replicate pricing

litellm/llms/replicate.py

@@ -125,6 +125,7 @@ def completion(
     ## Step1: Start Prediction: gets a prediction url
     ## Step2: Poll prediction url for response
     ## Step2: is handled with and without streaming
+    model_response["created"] = time.time() # for pricing this must remain right before calling api
     prediction_url = start_prediction(version_id, input_data, api_key, logging_obj=logging_obj)
     print_verbose(prediction_url)
 
@@ -134,7 +135,7 @@ def completion(
         return handle_prediction_response_streaming(prediction_url, api_key, print_verbose)
     else:
         result, logs = handle_prediction_response(prediction_url, api_key, print_verbose)
-
+        model_response["ended"] = time.time() # for pricing this must remain right after calling api
         ## LOGGING
         logging_obj.post_call(
                 input=prompt,
@@ -154,8 +155,7 @@ def completion(
         # Calculate usage
         prompt_tokens = len(encoding.encode(prompt))
         completion_tokens = len(encoding.encode(model_response["choices"][0]["message"]["content"]))
-        model_response["created"] = time.time()
+        model_response["model"] = "replicate/" + model
-        model_response["model"] = model
         model_response["usage"] = {
             "prompt_tokens": prompt_tokens,
             "completion_tokens": completion_tokens,
@@ -164,7 +164,6 @@ def completion(
         return model_response
 
 
-
 # # Example usage:
 # response = completion(
 #     api_key="",

litellm/tests/test_completion_cost.py

@@ -35,4 +35,34 @@ def test_completion_togetherai_cost():
         
     except Exception as e:
         pytest.fail(f"Error occurred: {e}")
-test_completion_togetherai_cost()
+# test_completion_togetherai_cost()
+
+
+def test_completion_replicate_llama_2():
+    model_name = "replicate/llama-2-70b-chat:2796ee9483c3fd7aa2e171d38f4ca12251a30609463dcfd4cd76703f22e96cdf"
+    try:
+        response = completion(
+            model=model_name, 
+            messages=messages, 
+            max_tokens=20,
+            custom_llm_provider="replicate"
+        )
+        print(response)
+        # Add any assertions here to check the response
+        response_str = response["choices"][0]["message"]["content"]
+        print(response_str)
+
+        # Add any assertions here to check the response
+        print(response)
+        print("Completion Cost: for togethercomputer/llama-2-70b-chat")
+        cost = completion_cost(completion_response=response)
+        formatted_string = f"${float(cost):.10f}"
+        print(formatted_string)
+        
+        if type(response_str) != str:
+            pytest.fail(f"Error occurred: {e}")
+    except Exception as e:
+        pytest.fail(f"Error occurred: {e}")
+
+
+# test_completion_replicate_llama_2()

litellm/utils.py

@@ -529,7 +529,7 @@ def client(original_function):
                 # TODO: Add to cache for streaming
                 return result
         
-            
+
             # [OPTIONAL] ADD TO CACHE
             if litellm.caching or litellm.caching_with_models or litellm.cache != None: # user init a cache object
                 litellm.cache.add_cache(result, *args, **kwargs)
@@ -594,6 +594,21 @@ def get_model_params_and_category(model_name):
 
     return None
 
+def get_replicate_completion_pricing(completion_response=None, run_time_in_seconds=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
+
+    start_time = completion_response['created']
+    end_time = completion_response["ended"]
+    run_time_in_seconds = end_time - start_time
+
+    print("total_replicate_run_time", run_time_in_seconds)
+
+    return a100_80gb_price_per_second_public*run_time_in_seconds
+
 
 def token_counter(model, text):
     # use tiktoken or anthropic's tokenizer depending on the model
@@ -647,6 +662,8 @@ def completion_cost(
         completion="",
         completion_response=None
     ):
+
+    # Handle Inputs to completion_cost
     prompt_tokens = 0
     completion_tokens = 0
     if completion_response != None:
@@ -657,10 +674,20 @@ def completion_cost(
     else:
         prompt_tokens = token_counter(model=model, text=prompt)
         completion_tokens = token_counter(model=model, text=completion)
+    
+    # Calculate cost based on prompt_tokens, completion_tokens
     if "togethercomputer" in model:
         # 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
+    ):
+        return get_replicate_completion_pricing(completion_response)
+
     prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar = cost_per_token(
         model=model, prompt_tokens=prompt_tokens, completion_tokens=completion_tokens
     )

model_prices_and_context_window.json

@@ -65,9 +65,7 @@
         "output_cost_per_token": 0.000015
     },
     "replicate/llama-2-70b-chat:2c1608e18606fad2812020dc541930f2d0495ce32eee50074220b87300bc16e1": {
-        "max_tokens": 4096,
+        "max_tokens": 4096
-        "input_cost_per_token": 0.00000608,
-        "output_cost_per_token": 0.00000608
     },
     "together-ai-up-to-3b": {
         "input_cost_per_token": 0.0000001,