Disable non-functioning torch.compile (#17)

generate.py

@@ -5,7 +5,7 @@
 import lightning as L
 
 
-@torch.inference_mode()
+@torch.no_grad()
 def generate(model, idx, max_new_tokens, max_seq_length, temperature=1.0, top_k=None):
     """
     Takes a conditioning sequence (prompt) as input and continues to generate as many tokens as requested.
@@ -79,7 +79,8 @@ def main(
     max_new_tokens: int = 20,
     top_k: int = 200,
     temperature: float = 0.8,
-    compile: bool = False,
+    # compilation fails as it does not support torch.complex64 for RoPE
+    # compile: bool = False,
     accelerator: str = "auto",
     precision: str = "32-true",
     checkpoint_path: str = "/srv/data/checkpoints/llama/converted_nano/7B/state_dict.pth",
@@ -96,7 +97,7 @@ def main(
         top_k: The number of top most probable tokens to consider in the sampling process.
         temperature: A value controlling the randomness of the sampling process. Higher values result in more random
             samples.
-        compile: Whether to compile the model.
+        # compile: Whether to compile the model.
         accelerator: The hardware to run on. Possible choices are:
             ``"cpu"``, ``"cuda"``, ``"mps"``, ``"gpu"``, ``"tpu"``, ``"auto"``.
         precision: Double precision (``"64"``), full precision (``"32"``), half precision AMP (``"16-mixed"``),
@@ -117,8 +118,8 @@ def main(
         model.load_state_dict(checkpoint, strict=(not original_model))
 
     model.eval()
-    if compile:
-        model = torch.compile(model)
+    # if compile:
+    #     model = torch.compile(model)
     model = fabric.setup_module(model, move_to_device=False)
 
     tokenizer = Tokenizer(tokenizer_path)

scripts/download.py

@@ -7,7 +7,7 @@ def download_original(wd: str):
     if not os.path.isfile(filepath):
         print(f"Downloading original implementation to {filepath!r}")
         urllib.request.urlretrieve(
-            url="https://gist.githubusercontent.com/lantiga/fd36849fb1c498da949a0af635318a7b/raw/c4509e48a53ebb6a195a6f073b5267a69e47b45a/llama_model.py",
+            url="https://gist.githubusercontent.com/lantiga/fd36849fb1c498da949a0af635318a7b/raw/fe9561d1abd8d2c61c82dd62155fe98d3ac74c43/llama_model.py",
             filename="original_model.py",
         )
         print("Done")

train.py

@@ -16,7 +16,8 @@
 eval_interval = 2000
 eval_iters = 200
 log_interval = 1
-compile = False
+# compilation fails as it does not support torch.complex64 for RoPE
+# compile = False
 
 # Hyperparameters
 learning_rate = 6e-4
@@ -60,8 +61,8 @@ def main():
     with fabric.device:
         model = LLaMA(config)
 
-    if compile:
-        model = torch.compile(model)
+    # if compile:
+    #     model = torch.compile(model)
 
     model = fabric.setup_module(model)