Merge f57a3e2619 into 7050ed204b

run.py

@@ -1,7 +1,7 @@
 # Copyright 2024 X.AI Corp.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.  
+# you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
 #
 #     http://www.apache.org/licenses/LICENSE-2.0
@@ -30,59 +30,54 @@ def validate_checkpoint(path, expected_hash):
 
 
 def main():
-  # Validate checkpoint integrity
+      # Validate checkpoint integrity
   validate_checkpoint(CKPT_PATH, CKPT_HASH)
-  
-  grok_1_model = LanguageModelConfig(
-    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))
+    grok_1_model = LanguageModelConfig(
+        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
@@ -92,7 +87,7 @@ def main():
   gen = inference_runner.run()
 
   # Rest of inference code
-  
+
 if __name__ == "__main__":
   logging.basicConfig(level=logging.INFO)
-  main()
+    main()