remove debug messages

generate.py

@@ -104,17 +104,22 @@ def sample(logits, temperature: float = 1.0, top_k: Optional[int] = None):
 
 
 def prefill(
-    model: Transformer, x: torch.Tensor, input_pos: torch.Tensor, **sampling_kwargs
+        model: Transformer,
+        x: torch.Tensor,
+        input_pos: torch.Tensor,
+        *,
+        sequential_prefill = True,
+        **sampling_kwargs
 ) -> torch.Tensor:
-    print(f"x: {x}, input_pos: {input_pos}")
+    # print(f"x: {x}, input_pos: {input_pos}")
     width = x.size(1)
     assert input_pos.size(0) == width
     sequential_prefill = True
 
     if sequential_prefill:
         for i in range(width):
             x_sliced, ip_sliced = x[:, i].view(-1, 1), input_pos[i].view(-1)
-            print(f"<sliced> x: {x_sliced}, input_pos: {ip_sliced}")
+            #print(f"<sliced> x: {x_sliced}, input_pos: {ip_sliced}")
             logits = model(x_sliced, ip_sliced)  # (x[:, i], input_pos[i])
     else:
         # input_pos: [B, S]
@@ -157,13 +162,6 @@ def decode_n_tokens(
     return new_tokens, new_probs
 
 
-# try:
-#     from .thin_wrapper import model_forward
-#
-# except:
-#     print("compiled model load not successful, running eager model")
-
-
 def model_forward(model, x, input_pos):
     return model(x, input_pos)
 
@@ -374,7 +372,7 @@ def _main(
     encoded = encode_tokens(
         tokenizer, generator_args.prompt, bos=True, device=builder_args.device
     )
-    print(encoded)
+    # print(encoded)
     prompt_length = encoded.size(0)
 
     model_size = sum(