Add options to save_torchscript

save_torchscript.py

@@ -1,6 +1,11 @@
 #!/usr/bin/env python
 """Saves the model as a TorchScript.
 
+Usage examples:
+    ./save_torchscript.py
+    ./save_torchscript.py --dim=300
+    ./save_torchscript.py --gzip_output=True --zero_params=True
+
 The resulting file can be loaded in C++ code and then used for training or
 inference with:
     #include <torch/script.h>
@@ -8,33 +13,53 @@ inference with:
 
 Note that the serialized model includes the initial parameters and with the default
 ModelArgs the file is 59M and gzips down to 55M. If you want to serialize/distribute
-the model parameters separately and you can zero out the parameters before saving it
+the model parameters separately you can zero out the parameters before saving it and
-and it will gzip down to 780K:
+it will gzip down to 780K.
-    for p in model.parameters():
-        p.detach().zero_()
 """
-import glob
+import gzip
 import os
-import sys
+import shutil
-from typing import List
+from inspect import signature
 
 import torch
 
 from model import ModelArgs, Transformer
 
+# Model args
+dim = 288
+n_layers = 6
+n_heads = 6
+n_kv_heads = n_heads
+multiple_of = 32
+max_seq_len = 256
+dropout = 0.0
+vocab_size = 32000
+norm_eps = 1e-5
+# Save config
+model_path = "model.pt"
+zero_params = False
+gzip_output = False
+
+exec(open("configurator.py").read())
+
 
 def main() -> None:
-    model = Transformer(
+    model_args = {k: globals()[k] for k in signature(ModelArgs).parameters}
-        ModelArgs(
+    model = Transformer(ModelArgs(**model_args))
-            dim=288,
+
-            n_layers=6,
+    # If requested zero params before saving the model. This is usful in
-            n_heads=6,
+    # conjunction with gzip_output.
-            multiple_of=32,
+    if zero_params:
-            dropout=0.0,
+        for p in model.parameters():
-            vocab_size=32000,
+            p.detach().zero_()
-        )
+
-    )
+    torch.jit.save(torch.jit.script(model), model_path)
-    torch.jit.save(torch.jit.script(model), "model.pt")
+
+    if gzip_output:
+        with open(model_path, "rb") as f_in:
+            with gzip.open(f"{model_path}.gz", "wb") as f_out:
+                shutil.copyfileobj(f_in, f_out)
+        os.unlink(model_path)
 
 
 if __name__ == "__main__":