Quantize example (#162)

* testing quantization

* conversion + quantization working

* one config processor

* quantization in mistral / nits in llama

* args for quantization

* llama / mistral conversion in good shape

* phi2 quantized

* mixtral

* qwen conversion

llms/qwen/convert.py

@@ -1,8 +1,14 @@
 import argparse
+import copy
 import json
+from pathlib import Path
 
+import mlx.core as mx
+import mlx.nn as nn
 import numpy as np
 import torch
+from mlx.utils import tree_flatten, tree_map, tree_unflatten
+from qwen import ModelArgs, Qwen
 from transformers import AutoModelForCausalLM
 
 
@@ -14,19 +20,58 @@ def replace_key(key: str) -> str:
     return key
 
 
-def convert(model_path: str = "Qwen/Qwen-1_8B"):
+def quantize(weights, config, args):
+    quantized_config = copy.deepcopy(config)
+
+    # Load the model:
+    model_args = ModelArgs()
+    model_args.vocab_size = config["vocab_size"]
+    model_args.hidden_size = config["hidden_size"]
+    model_args.num_attention_heads = config["num_attention_heads"]
+    model_args.num_hidden_layers = config["num_hidden_layers"]
+    model_args.kv_channels = config["kv_channels"]
+    model_args.max_position_embeddings = config["max_position_embeddings"]
+    model_args.layer_norm_epsilon = config["layer_norm_epsilon"]
+    model_args.intermediate_size = config["intermediate_size"]
+    model_args.no_bias = config["no_bias"]
+    model = Qwen(model_args)
+
+    weights = tree_map(mx.array, weights)
+    model.update(tree_unflatten(list(weights.items())))
+
+    # Quantize the model:
+    nn.QuantizedLinear.quantize_module(model, args.q_group_size, args.q_bits)
+
+    # Update the config:
+    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
+
+
+def convert(args):
+    mlx_path = Path(args.mlx_path)
+    mlx_path.mkdir(parents=True, exist_ok=True)
+
     model = AutoModelForCausalLM.from_pretrained(
-        model_path, trust_remote_code=True, torch_dtype=torch.float16
+        args.model, trust_remote_code=True, torch_dtype=torch.float16
     )
     state_dict = model.state_dict()
     weights = {replace_key(k): v.numpy() for k, v in state_dict.items()}
-    np.savez("weights.npz", **weights)
+    config = model.config.to_dict()
+
+    if args.quantize:
+        print("[INFO] Quantizing")
+        weights, config = quantize(weights, config, args)
+
+    np.savez(str(mlx_path / "weights.npz"), **weights)
 
     # write config
-    config = model.config
-    config_dict = config.to_dict()
-    with open("config.json", "w") as f:
-        json.dump(config_dict, f, indent=4)
+    with open(mlx_path / "config.json", "w") as f:
+        json.dump(config, f, indent=4)
 
 
 if __name__ == "__main__":
@@ -37,7 +82,29 @@ if __name__ == "__main__":
         help="The huggingface model to be converted",
         default="Qwen/Qwen-1_8B",
     )
-
+    parser.add_argument(
+        "--mlx-path",
+        type=str,
+        default="mlx_model",
+        help="The path to save the MLX model.",
+    )
+    parser.add_argument(
+        "-q",
+        "--quantize",
+        help="Generate a 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()
-
-    convert(args.model)
+    convert(args)