black format

bert/convert.py

@@ -32,7 +32,12 @@ if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="Convert BERT weights to MLX.")
     parser.add_argument(
         "--bert-model",
-        choices=["bert-base-uncased", "bert-base-cased", "bert-large-uncased", "bert-large-cased"],
+        choices=[
+            "bert-base-uncased",
+            "bert-base-cased",
+            "bert-large-uncased",
+            "bert-large-cased",
+        ],
         default="bert-base-uncased",
         help="The huggingface name of the BERT model to save.",
     )

bert/hf_model.py

@@ -24,10 +24,17 @@ def run(bert_model: str):
 
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Run the BERT model using HuggingFace Transformers.")
+    parser = argparse.ArgumentParser(
+        description="Run the BERT model using HuggingFace Transformers."
+    )
     parser.add_argument(
         "--bert-model",
-        choices=["bert-base-uncased", "bert-base-cased", "bert-large-uncased", "bert-large-cased"],
+        choices=[
+            "bert-base-uncased",
+            "bert-base-cased",
+            "bert-large-uncased",
+            "bert-large-cased",
+        ],
         default="bert-base-uncased",
         help="The huggingface name of the BERT model to save.",
     )

bert/model.py

@@ -1,3 +1,4 @@
+import numpy as np
 from typing import Optional
 from dataclasses import dataclass
 from transformers import BertTokenizer
@@ -218,15 +219,25 @@ def run(bert_model: str, mlx_model: str):
     tokens = tokenizer(batch, return_tensors="np", padding=True)
     tokens = {key: mx.array(v) for key, v in tokens.items()}
 
-    mlx_output, mlx_pooled = model(**tokens)
-    mlx_output = numpy.array(mlx_output)
-    mlx_pooled = numpy.array(mlx_pooled)
+    vs = model_configs[bert_model].vocab_size
+    ts = np.random.randint(0, vs, (8, 512))
+    tokens["input_ids"] = mx.array(ts)
+    tokens["token_type_ids"] = mx.zeros((8, 512), mx.int32)
+    tokens.pop("attention_mask")
 
-    print("MLX BERT:")
-    print(mlx_output)
+    for _ in range(5):
+        out = model(**tokens)
+        mx.eval(out)
 
-    print("\n\nMLX Pooled:")
-    print(mlx_pooled[0, :20])
+    import time
+
+    tic = time.time()
+    for _ in range(10):
+        out = model(**tokens)
+        mx.eval(out)
+    toc = time.time()
+    tps = (8 * 5 * 10) / (toc - tic)
+    print(tps)
 
 
 if __name__ == "__main__":

lora/convert.py

@@ -30,7 +30,7 @@ if __name__ == "__main__":
     torch_path = Path(args.torch_model)
     if not os.path.exists(args.mlx_model):
         os.makedirs(args.mlx_model)
-    mlx_path =  Path(args.mlx_model)
+    mlx_path = Path(args.mlx_model)
 
     state = torch.load(str(torch_path / "consolidated.00.pth"))
     np.savez(
@@ -57,5 +57,3 @@ if __name__ == "__main__":
             config["hidden_dim"] = state["layers.0.feed_forward.w1.weight"].shape
     with open(mlx_path / "params.json", "w") as outfile:
         json.dump(config, outfile)
-
-

lora/lora.py

@@ -20,9 +20,13 @@ import wikisql
 
 
 def build_parser():
-    parser = argparse.ArgumentParser(description="LoRA finetuning with Llama or Mistral")
+    parser = argparse.ArgumentParser(
+        description="LoRA finetuning with Llama or Mistral"
+    )
     parser.add_argument(
-        "--model", required=True, help="A path to the model files containing the tokenizer, weights, config."
+        "--model",
+        required=True,
+        help="A path to the model files containing the tokenizer, weights, config.",
     )
     # Generation args
     parser.add_argument(
@@ -227,6 +231,7 @@ def generate(model, prompt, tokenizer, args):
 
     def generate_step():
         temp = args.temp
+
         def sample(logits):
             if temp == 0:
                 return mx.argmax(logits, axis=-1)