expose model info

Author: Le Xu

development/app/app.py

@@ -238,6 +238,7 @@ def completion():
         latency = 0.0
         if simulator is not None:
             latency = simulator.execute(Request(arrived_at, input_tokens, output_tokens, arrived_next=arrived_next))
+            print(f"input_tokens {input_tokens} model metadata {simulator.model_metadata}")
 
         # Simulated response
         response = {
@@ -698,7 +699,9 @@ def metrics():
     if gpu_device != "disabled":
         # Load the tokenizer for your model
         from transformers import AutoTokenizer
-
+        from transformers import AutoConfig
+        
+        num_layers, num_heads, hidden_size = None
         default_model = 'bert-base-uncased'
         try:
             # can we make this as an application argument.
@@ -709,15 +712,30 @@ def metrics():
                 token=HUGGINGFACE_TOKEN,
                 model_max_length=16384,  # Suppress warning
                 clean_up_tokenization_spaces=True)
+            config = AutoConfig.from_pretrained(token_model)
+            # Extract required details
+            num_layers = config.get("num_hidden_layers", config.get("n_layers"))
+            num_heads = config.get("num_attention_heads", config.get("n_heads"))
+            hidden_size = config.get("hidden_size", config.get("dim"))
         except Exception as e:
             logger.error(f"Failed to initialize tokenizer, will use default tokenizer model: {e}")
             tokenizer = AutoTokenizer.from_pretrained(
                 default_model,
                 model_max_length=16384,  # Suppress warning
                 clean_up_tokenization_spaces=True)
+            config = AutoConfig.from_pretrained(default_model)
+            # Extract required details
+            num_layers = config.get("num_hidden_layers", config.get("n_layers"))
+            num_heads = config.get("num_attention_heads", config.get("n_heads"))
+            hidden_size = config.get("hidden_size", config.get("dim"))
 
         # TODO: check whether able to use argparse to build SimulationConfig
         simulator = Simulator(SimulationConfig.create_from_cli_args())
+        
+        simulator.model_metadata['num_layers'] = num_layers
+        simulator.model_metadata['num_heads'] = num_heads
+        simulator.model_metadata['hidden_size'] = hidden_size
+        
         overrides = {
             "total": 100.0,
             "running": 0,

development/app/simulator.py

@@ -4,7 +4,7 @@
 import json
 import sys
 import threading
-from typing import List
+from typing import List, Dict
 
 from vidur.config import SimulationConfig
 from vidur.entities import Cluster, Request
@@ -56,6 +56,8 @@ def __init__(self, config: SimulationConfig) -> None:
         self._expect_next_tick = sys.float_info.max
         self._queue_buffer: List[Request] = []
         self._queue = None
+        
+        self._model_metadata = {}
 
         # self._init_event_queue()
         atexit.register(self._write_output)
@@ -68,6 +70,10 @@ def scheduler(self) -> BaseGlobalScheduler:
     def metric_store(self) -> MetricsStore:
         return self._metric_store
 
+    @property
+    def model_metadata(self) -> Dict:
+        return self._model_metadata
+    
     def start(self):
         logger.info(
             f"Starting simulation with cluster: {self._cluster}, model: {self._config.cluster_config.replica_config.model_name}, seed: {self._config.seed}"