[Not for land] Util for saving quantized model

torchchat.py

@@ -49,6 +49,7 @@
         "where": "Return directory containing downloaded model artifacts",
         "server": "[WIP] Starts a locally hosted REST server for model interaction",
         "eval": "Evaluate a model via lm-eval",
+        "save_quant": "Quantize a model and save it to disk",
     }
     for verb, description in VERB_HELP.items():
         subparser = subparsers.add_parser(verb, help=description)
@@ -115,5 +116,9 @@
         from torchchat.cli.download import remove_main
 
         remove_main(args)
+    elif args.command == "save_quant":
+        from torchchat.save_quant import main as save_quant_main
+
+        save_quant_main(args)
     else:
         parser.print_help()

torchchat/save_quant.py

@@ -0,0 +1,58 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import os
+from pathlib import Path
+from typing import Optional
+
+import torch
+import torch.nn as nn
+
+from torchchat.cli.builder import (
+    _initialize_model,
+    BuilderArgs,
+)
+
+from torchchat.utils.build_utils import set_precision
+
+from torchao.quantization import quantize_, int8_weight_only
+
+"""
+Exporting Flow
+"""
+
+
+def main(args):
+    builder_args = BuilderArgs.from_args(args)
+    print(f"{builder_args=}")
+
+    quant_format = "int8_wo"
+    # Quant option from cli, can be None
+    model = _initialize_model(builder_args, args.quantize)
+    if not args.quantize:
+        # Not using quantization option from cli;
+        # Use quantize_() to quantize the model instead.
+        print("Quantizing model using torchao quantize_")
+        quantize_(model, int8_weight_only())
+    else:
+        print(f"{args.quantize=}")
+
+    print(f"Model: {model}")
+
+    # Save model
+    model_dir = os.path.dirname(builder_args.checkpoint_path)
+    model_dir = Path(model_dir + "-" + quant_format)
+    try:
+        os.mkdir(model_dir)
+    except FileExistsError:
+        pass
+    dest = model_dir / "model.pth"
+    state_dict = model.state_dict()
+    print(f"{state_dict.keys()=}")
+
+    print(f"Saving checkpoint to {dest}. This may take a while.")
+    torch.save(state_dict, dest)
+    print("Done.")