Merge 6ed2d78bea into 7050ed204b

run.py

@@ -13,60 +13,86 @@
 # limitations under the License.
 
 import logging
+import hashlib
 
 from model import LanguageModelConfig, TransformerConfig, QuantizedWeight8bit as QW8Bit
 from runners import InferenceRunner, ModelRunner, sample_from_model
 
 
 CKPT_PATH = "./checkpoints/"
+CKPT_HASH = "expected_checkpoint_hash"
+
+
+def validate_checkpoint(path, expected_hash):
+  calculated_hash = hashlib.sha256(open(path, 'rb').read()).hexdigest()
+  if calculated_hash != expected_hash:
+    raise ValueError("Invalid checkpoint file!")
 
 
 def main():
-    grok_1_model = LanguageModelConfig(
+  # Validate checkpoint integrity
-        vocab_size=128 * 1024,
+  validate_checkpoint(CKPT_PATH, CKPT_HASH)
-        pad_token=0,
-        eos_token=2,
-        sequence_len=8192,
-        embedding_init_scale=1.0,
-        output_multiplier_scale=0.5773502691896257,
-        embedding_multiplier_scale=78.38367176906169,
-        model=TransformerConfig(
-            emb_size=48 * 128,
-            widening_factor=8,
-            key_size=128,
-            num_q_heads=48,
-            num_kv_heads=8,
-            num_layers=64,
-            attn_output_multiplier=0.08838834764831845,
-            shard_activations=True,
-            # MoE.
-            num_experts=8,
-            num_selected_experts=2,
-            # Activation sharding.
-            data_axis="data",
-            model_axis="model",
-        ),
-    )
-    inference_runner = InferenceRunner(
-        pad_sizes=(1024,),
-        runner=ModelRunner(
-            model=grok_1_model,
-            bs_per_device=0.125,
-            checkpoint_path=CKPT_PATH,
-        ),
-        name="local",
-        load=CKPT_PATH,
-        tokenizer_path="./tokenizer.model",
-        local_mesh_config=(1, 8),
-        between_hosts_config=(1, 1),
-    )
-    inference_runner.initialize()
-    gen = inference_runner.run()
   
-    inp = "The answer to life the universe and everything is of course"
+  grok_1_model = LanguageModelConfig(
-    print(f"Output for prompt: {inp}", sample_from_model(gen, inp, max_len=100, temperature=0.01))
+    vocab_size=128 * 1024,
+    pad_token=0, 
+    eos_token=2,
+    sequence_len=8192,
+    embedding_init_scale=1.0,
+    output_multiplier_scale=0.5773502691896257,
+    embedding_multiplier_scale=78.38367176906169,
+    model=TransformerConfig(
+      emb_size=48 * 128,
+      widening_factor=8,
+      key_size=128,
+      num_q_heads=48,
+      num_kv_heads=8,
+      num_layers=64,
+      attn_output_multiplier=0.08838834764831845,
+      shard_activations=True,
+      # MoE.
+      num_experts=8,
+      num_selected_experts=2,
+      # Activation sharding.
+      data_axis="data",
+      model_axis="model",
+    ),
+  )
   
+  inference_runner = InferenceRunner(
+    pad_sizes=(1024,),
+    runner=ModelRunner(
+      model=grok_1_model,
+      bs_per_device=0.125, 
+      checkpoint_path=CKPT_PATH,
+    # Limit inference rate
+    inference_runner.rate_limit = 100
+    ),
+    
+    name="local",
+    load=CKPT_PATH,
+    tokenizer_path="./tokenizer.model",
+    local_mesh_config=(1, 8),
+    between_hosts_config=(1, 1),
+  )
+  
+  inference_runner.initialize()
+  
+  gen = inference_runner.run()
+
+  inp = "The answer to life the universe and everything is of course"
+  print(f"Output for prompt: {inp}", sample_from_model(gen, inp, max_len=100, temperature=0.01))
+
+  # Add authentication
+  @app.route("/inference")
+  @auth.login_required
+  def inference():
+    ...
+  
+  gen = inference_runner.run()
+
+  # Rest of inference code
   
 if __name__ == "__main__":
-    logging.basicConfig(level=logging.INFO)
+  logging.basicConfig(level=logging.INFO)
-    main()
+  main()