args for quantization

llms/mistral/README.md

@@ -23,16 +23,17 @@ tar -xf mistral-7B-v0.1.tar
 Then, convert the weights with:
 
 ```
-python convert.py
+python convert.py --torch-path <path_to_torch>
 ```
 
-To generate a 4-bit quantized model, use:
+To generate a 4-bit quantized model, use ``-q``. For a full list of options:
 
 ```
-python convert.py -q
+python convert.py --help
 ```
 
-The conversion script will save the converted weights in the same location.
+By default, the conversion script will make the directory `mlx_model` and save
+the converted `weights.npz`, `tokenizer.model`, and `config.json` there.
 
 > [!TIP]
 > Alternatively, you can also download a few converted checkpoints from the
@@ -46,7 +47,7 @@ Once you've converted the weights to MLX format, you can generate text with
 the Mistral model:
 
 ```
-python mistral.py --prompt "It is a truth universally acknowledged,"  --temp 0
+python mistral.py --prompt "It is a truth universally acknowledged,"
 ```
 
 Run `python mistral.py --help` for more details.

llms/mistral/convert.py

@@ -1,7 +1,9 @@
 # Copyright © 2023 Apple Inc.
 
 import argparse
+import copy
 import json
+import shutil
 from pathlib import Path
 
 import mlx.core as mx
@@ -12,7 +14,7 @@ from mistral import Mistral, ModelArgs
 from mlx.utils import tree_flatten, tree_map, tree_unflatten
 
 
-def quantize(weights, config):
+def quantize(weights, config, args):
     quantized_config = copy.deepcopy(config)
 
     # Load the model:
@@ -22,10 +24,13 @@ def quantize(weights, config):
     model.update(tree_unflatten(list(weights.items())))
 
     # Quantize the model:
-    nn.QuantizedLinear.quantize_module(model)
+    nn.QuantizedLinear.quantize_module(model, args.q_group_size, args.q_bits)
 
     # Update the config:
-    quantized_config["quantization"] = {"group_size": 64, "bits": 4}
+    quantized_config["quantization"] = {
+        "group_size": args.q_group_size,
+        "bits": args.q_bits,
+    }
     quantized_weights = dict(tree_flatten(model.parameters()))
 
     return quantized_weights, quantized_config
@@ -34,10 +39,16 @@ def quantize(weights, config):
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="Convert Mistral weights to MLX.")
     parser.add_argument(
-        "--model-path",
+        "--torch-path",
         type=str,
         default="mistral-7B-v0.1/",
-        help="The path to the Mistral model. The MLX weights will also be saved there.",
+        help="The path to the PyTorch Mistral model.",
+    )
+    parser.add_argument(
+        "--mlx-path",
+        type=str,
+        default="mlx_model",
+        help="The path to save the MLX model.",
     )
     parser.add_argument(
         "-q",
@@ -45,20 +56,46 @@ if __name__ == "__main__":
         help="Generate a 4-bit quantized model.",
         action="store_true",
     )
+    parser.add_argument(
+        "--q_group_size",
+        help="Group size for quantization.",
+        type=int,
+        default=64,
+    )
+    parser.add_argument(
+        "--q_bits",
+        help="Bits per weight for quantization.",
+        type=int,
+        default=4,
+    )
     args = parser.parse_args()
 
-    model_path = Path(args.model_path)
+    torch_path = Path(args.torch_path)
-    state = torch.load(str(model_path / "consolidated.00.pth"))
+    state = torch.load(str(torch_path / "consolidated.00.pth"))
+    mlx_path = Path(args.mlx_path)
+    mlx_path.mkdir(parents=True, exist_ok=True)
+
     weights = {k: v.to(torch.float16).numpy() for k, v in state.items()}
+    with open(torch_path / "params.json", "r") as f:
+        config = json.loads(f.read())
+
     if args.quantize:
         print("[INFO] Quantizing")
-        weights, params = quantize(weights, params)
+        weights, config = quantize(weights, config, args)
 
-    np.savez(str(model_path / "weights.npz"), **weights)
+    # Save weights
+    import pdb
+
+    pdb.set_trace()
+    np.savez(str(mlx_path / "weights.npz"), **weights)
+
+    # Copy tokenizer
+    shutil.copyfile(
+        str(torch_path / "tokenizer.model"),
+        str(mlx_path / "tokenizer.model"),
+    )
 
     # Save config.json with model_type
-    with open(model_path / "params.json", "r") as f:
+    with open(mlx_path / "config.json", "w") as f:
-        config = json.loads(f.read())
         config["model_type"] = "mistral"
-    with open(model_path / "config.json", "w") as f:
         json.dump(config, f, indent=4)

llms/mistral/mistral.py

@@ -8,7 +8,7 @@ from typing import List, Optional, Tuple
 
 import mlx.core as mx
 import mlx.nn as nn
-from mlx.utils import tree_map, tree_unflatten
+from mlx.utils import tree_unflatten
 from sentencepiece import SentencePieceProcessor
 
 
@@ -189,7 +189,7 @@ class Tokenizer:
         return out
 
 
-def load_model(folder: str, dtype=mx.float16):
+def load_model(folder: str):
     model_path = Path(folder)
     tokenizer = Tokenizer(str(model_path / "tokenizer.model"))
     with open(model_path / "config.json", "r") as f:
@@ -200,7 +200,6 @@ def load_model(folder: str, dtype=mx.float16):
         model_args = ModelArgs(**config)
     weights = mx.load(str(model_path / "weights.npz"))
     weights = tree_unflatten(list(weights.items()))
-    weights = tree_map(lambda p: p.astype(dtype), weights)
     model = Mistral(model_args)
     if quantization is not None:
         nn.QuantizedLinear.quantize_module(model, **quantization)
@@ -230,7 +229,7 @@ if __name__ == "__main__":
     parser.add_argument(
         "--model-path",
         type=str,
-        default="mistral-7B-v0.1",
+        default="mlx_model",
         help="The path to the model weights and tokenizer",
     )
     parser.add_argument(
@@ -239,7 +238,7 @@ if __name__ == "__main__":
         default="In the beginning the Universe was created.",
     )
     parser.add_argument(
-        "--max_tokens",
+        "--max-tokens",
         "-m",
         type=int,
         default=100,
@@ -249,7 +248,7 @@ if __name__ == "__main__":
         "--temp",
         help="The sampling temperature.",
         type=float,
-        default=1.0,
+        default=0.0,
     )
     parser.add_argument(
         "--tokens_per_eval",