Load default model during app init #4

sidekick/query.py

@@ -79,8 +79,8 @@ def __init__(
         is_regenerate_with_options: bool = False,
     ):
         self.db_url = db_url
-        self.engine = create_engine(db_url)
-        self.sql_database = SQLDatabase(self.engine)
+        self.engine = create_engine(db_url) if db_url else None
+        self.sql_database = SQLDatabase(self.engine) if self.engine else None
         self.context_builder = None
         self.data_input_path = _check_file_info(data_input_path)
         self.sample_queries_path = sample_queries_path

sidekick/utils.py

@@ -15,8 +15,7 @@
 from sentence_transformers import SentenceTransformer
 from sidekick.logger import logger
 from sklearn.metrics.pairwise import cosine_similarity
-from transformers import (AutoConfig, AutoModelForCausalLM, AutoTokenizer,
-                          BitsAndBytesConfig)
+from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
 
 
 def generate_sentence_embeddings(model_path: str, x, batch_size: int = 32, device: Optional[str] = None):
@@ -269,9 +268,9 @@ def get_table_keys(file_path: str, table_key: str):
         return res, data
 
 
-def is_resource_low():
-    free_in_GB = int(torch.cuda.mem_get_info()[0] / 1024**3)
-    total_memory = int(torch.cuda.get_device_properties(0).total_memory / 1024**3)
+def is_resource_low(device_index: int = 0):
+    free_in_GB = int(torch.cuda.mem_get_info(device_index)[0] / 1024**3)
+    total_memory = int(torch.cuda.get_device_properties(device_index).total_memory / 1024**3)
     logger.info(f"Total Memory: {total_memory}GB")
     logger.info(f"Free GPU memory: {free_in_GB}GB")
     off_load = True
@@ -296,20 +295,21 @@ def load_causal_lm_model(
         }
         model_name = model_choices_map[model_type]
         logger.info(f"Loading model: {model_name}")
+        device_index = 0
         # Load h2oGPT.SQL model
-        device = {"": 0} if torch.cuda.is_available() else "cpu" if device == "auto" else device
+        device = {"": device_index} if torch.cuda.is_available() else "cpu" if device == "auto" else device
         total_memory = int(torch.cuda.get_device_properties(0).total_memory / 1024**3)
         free_in_GB = int(torch.cuda.mem_get_info()[0] / 1024**3)
         logger.info(f"Free GPU memory: {free_in_GB}GB")
         n_gpus = torch.cuda.device_count()
+        logger.info(f"Total GPUs: {n_gpus}")
         _load_in_8bit = load_in_8bit
 
         # 22GB (Least requirement on GPU) is a magic number for the current model size.
         if off_load and re_generate and total_memory < 22:
             # To prevent the system from crashing in-case memory runs low.
             # TODO: Performance when offloading to CPU.
-            max_memory = f"{4}GB"
-            max_memory = {i: max_memory for i in range(n_gpus)}
+            max_memory = {device_index: f"{4}GB"}
             logger.info(f"Max Memory: {max_memory}, offloading to CPU")
             with init_empty_weights():
                 config = AutoConfig.from_pretrained(model_name, cache_dir=cache_path, offload_folder=cache_path)
@@ -322,8 +322,7 @@ def load_causal_lm_model(
             _load_in_8bit = True
             load_in_4bit = False
         else:
-            max_memory = f"{int(free_in_GB)-2}GB"
-            max_memory = {i: max_memory for i in range(n_gpus)}
+            max_memory = {device_index: f"{int(free_in_GB)-2}GB"}
             _offload_state_dict = False
             _llm_int8_enable_fp32_cpu_offload = False
 

ui/app.py

@@ -11,6 +11,7 @@
 from h2o_wave import Q, app, data, handle_on, main, on, ui
 from h2o_wave.core import expando_to_dict
 from sidekick.prompter import db_setup_api, query_api
+from sidekick.query import SQLGenerator
 from sidekick.utils import get_table_keys, save_query, setup_dir, update_tables
 
 # Load the config file and initialize required paths
@@ -197,6 +198,8 @@ async def chatbot(q: Q):
     question = f"{q.args.chatbot}"
     logging.info(f"Question: {question}")
 
+    if q.args.table_dropdown or q.args.model_choice_dropdown:
+        return
     # For regeneration, currently there are 2 modes
     # 1. Quick fast approach by throttling the temperature
     # 2. "Try harder mode (THM)" Slow approach by using the diverse beam search
@@ -662,6 +665,23 @@ async def on_event(q: Q):
     return event_handled
 
 
+def on_startup():
+    logging.info("SQL-Assistant started!")
+    logging.info(f"Initializing default model")
+
+    _ = SQLGenerator(
+        None,
+        None,
+        model_name="h2ogpt-sql-sqlcoder2",
+        job_path=base_path,
+        data_input_path="",
+        sample_queries_path="",
+        is_regenerate_with_options="",
+        is_regenerate="",
+    )
+    return
+
+
 @app("/", on_shutdown=on_shutdown)
 async def serve(q: Q):
     # Run only once per client connection.
@@ -670,6 +690,7 @@ async def serve(q: Q):
         setup_dir(base_path)
         await init(q)
         q.client.initialized = True
+        on_startup()
         logging.info("App initialized.")
 
     # Handle routing.