update weighted_average_column_for_model to account for when there was an error reported, insert null for response tokens when error, correctly parse x-forwarded-for, correctly convert model reported by hf-textgen

llm_server/database.py

@@ -48,9 +48,11 @@ def init_db():
 def log_prompt(ip, token, prompt, response, gen_time, parameters, headers, backend_response_code, response_tokens: int = None, is_error: bool = False):
     prompt_tokens = len(tokenizer.encode(prompt))
 
-    # TODO: insert None for response tokens when error
+    if not is_error:
         if not response_tokens:
             response_tokens = len(tokenizer.encode(response))
+    else:
+        response_tokens = None
 
     # Sometimes we may want to insert null into the DB, but
     # usually we want to insert a float.
@@ -127,7 +129,7 @@ def average_column_for_model(table_name, column_name, model_name):
     return result[0]
 
 
-def weighted_average_column_for_model(table_name, column_name, model_name):
+def weighted_average_column_for_model(table_name, column_name, model_name, exclude_zeros: bool = False):
     conn = sqlite3.connect(opts.database_path)
     cursor = conn.cursor()
     cursor.execute(f"SELECT DISTINCT model FROM {table_name}")
@@ -144,7 +146,7 @@ def weighted_average_column_for_model(table_name, column_name, model_name):
         total_weight = 0
         weighted_sum = 0
         for i, (value, rowid) in enumerate(results):
-            if value is None:
+            if value is None or (exclude_zeros and value == 0):
                 continue
             weight = i + 1
             total_weight += weight

llm_server/llm/info.py

@@ -9,20 +9,15 @@ def get_running_model():
             backend_response = requests.get(f'{opts.backend_url}/api/v1/model', timeout=3, verify=opts.verify_ssl)
         except Exception as e:
             return False, e
-        try:
-            r_json = backend_response.json()
-            return r_json['result'], None
-        except Exception as e:
-            return False, e
     elif opts.mode == 'hf-textgen':
         try:
             backend_response = requests.get(f'{opts.backend_url}/info', verify=opts.verify_ssl)
         except Exception as e:
             return False, e
+    else:
+        raise Exception
     try:
         r_json = backend_response.json()
         return r_json['model_id'].replace('/', '_'), None
     except Exception as e:
         return False, e
-    else:
-        raise Exception

llm_server/routes/v1/generate.py

@@ -28,7 +28,7 @@ def generate():
     if request.headers.get('cf-connecting-ip'):
         client_ip = request.headers.get('cf-connecting-ip')
     elif request.headers.get('x-forwarded-for'):
-        client_ip = request.headers.get('x-forwarded-for')
+        client_ip = request.headers.get('x-forwarded-for').split(',')[0]
     else:
         client_ip = request.remote_addr
 

llm_server/routes/v1/generate_stats.py

@@ -34,7 +34,7 @@ def generate_stats():
 
     if opts.average_generation_time_mode == 'database':
         average_generation_time = float(redis.get('average_generation_elapsed_sec'))
-        average_output_tokens = float(redis.get('average_output_tokens'))
+        # average_output_tokens = float(redis.get('average_output_tokens'))
         # average_generation_time_from_tps = (average_output_tokens / average_tps)
 
         # What to use in our math that calculates the wait time.

llm_server/threads.py

@@ -38,7 +38,7 @@ class MainBackgroundThread(Thread):
                 try:
                     r = requests.get(f'{opts.backend_url}/info', timeout=3, verify=opts.verify_ssl)
                     j = r.json()
-                    opts.running_model = j['model_id']
+                    opts.running_model = j['model_id'].replace('/', '_')
                     redis.set('backend_online', 1)
                     redis.set_dict('backend_info', j)
                 except Exception as e:
@@ -48,13 +48,15 @@ class MainBackgroundThread(Thread):
             else:
                 raise Exception
 
-            average_generation_elapsed_sec = weighted_average_column_for_model('prompts', 'generation_time', opts.running_model) or 0
+            # exclude_zeros=True filters out rows where an error message was returned. Previously, if there was an error, 0
+            # was entered into the column. The new code enters null instead but we need to be backwards compatible for now
+            average_generation_elapsed_sec = weighted_average_column_for_model('prompts', 'generation_time', opts.running_model, exclude_zeros=True) or 0
             redis.set('average_generation_elapsed_sec', average_generation_elapsed_sec)
 
             # overall = average_column_for_model('prompts', 'generation_time', opts.running_model)
             # print(f'Weighted: {average_generation_elapsed_sec}, overall: {overall}')
 
-            average_output_tokens = weighted_average_column_for_model('prompts', 'response_tokens', opts.running_model) or 0
+            average_output_tokens = weighted_average_column_for_model('prompts', 'response_tokens', opts.running_model, exclude_zeros=True) or 0
             redis.set('average_output_tokens', average_output_tokens)
 
             # overall = average_column_for_model('prompts', 'response_tokens', opts.running_model)