Fix convert and tokenizer

qwen/.gitignore

@@ -0,0 +1,2 @@
+weights.npz
+config.json

qwen/convert.py

@@ -1,6 +1,8 @@
 import argparse
 from transformers import AutoModelForCausalLM
 import numpy as np
+import torch
+import json
 
 
 def replace_key(key: str) -> str:
@@ -13,12 +15,18 @@ def replace_key(key: str) -> str:
 
 def convert(model_path: str = "Qwen/Qwen-1_8B"):
     model = AutoModelForCausalLM.from_pretrained(
-        model_path, trust_remote_code=True
+        model_path, 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)
 
+    # write config
+    config = model.config
+    config_dict = config.to_dict()
+    with open("config.json", "w") as f:
+        json.dump(config_dict, f)
+
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="Convert Qwen model to npz")

qwen/qwen.py

@@ -2,6 +2,7 @@
 # This inference script is mainly for compatibility with the huggingface model of qwen.
 
 import argparse
+import json
 import mlx.core as mx
 import mlx.nn as nn
 
@@ -43,10 +44,8 @@ class QWenAttntion(nn.Module):
 
         self.proj_size = args.kv_channels * self.num_attention_heads
 
-        self.c_attn = nn.Linear(
-            self.hidden_size, self.proj_size * 3, bias=True)
-        self.c_proj = nn.Linear(
-            self.hidden_size, self.proj_size, bias=not args.no_bias)
+        self.c_attn = nn.Linear(self.hidden_size, self.proj_size * 3, bias=True)
+        self.c_proj = nn.Linear(self.hidden_size, self.proj_size, bias=not args.no_bias)
 
         self.scale = self.hidden_size_per_attention_head**-0.5
 
@@ -72,9 +71,6 @@ class QWenAttntion(nn.Module):
             q = self.rotary_emb(q)
             k = self.rotary_emb(k)
 
-        q = q.astype(mx.float32)
-        k = k.astype(mx.float32)
-
         scores = (q * self.scale) @ k.transpose(0, 1, 3, 2)
 
         if mask is not None:
@@ -146,8 +142,7 @@ class QWen(nn.Module):
 
         mask = None
         if x.shape[1] > 1:
-            mask = nn.MultiHeadAttention.create_additive_causal_mask(
-                x.shape[1])
+            mask = nn.MultiHeadAttention.create_additive_causal_mask(x.shape[1])
             mask = mask.astype(x.dtype)
 
         if cache is None:
@@ -178,12 +173,30 @@ def generate(prompt: mx.array, model: QWen, temp: 0.0):
         yield y
 
 
-def load_model(tokenizer_path: str = "Qwen/Qwen-1_8B"):
-    model = QWen(ModelArgs())
+def load_model(
+    tokenizer_path: str = "Qwen/Qwen-1_8B", config_path: str = "config.json"
+):
+    model_args = ModelArgs()
+
+    with open(config_path, "r") as f:
+        config = json.load(f)
+        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 = mx.load("weights.npz")
     model.update(tree_unflatten(list(weights.items())))
     tokenizer = AutoTokenizer.from_pretrained(
-        tokenizer_path, trust_remote_code=True)
+        tokenizer_path, trust_remote_code=True, eos_token="<|endoftext|>"
+    )
     return model, tokenizer
 
 
@@ -239,8 +252,7 @@ if __name__ == "__main__":
         if (len(tokens) % 10) == 0:
             mx.eval(tokens)
             eos_index = next(
-                (i for i, t in enumerate(tokens)
-                 if t.item() == tokenizer.eos_token_id),
+                (i for i, t in enumerate(tokens) if t.item() == tokenizer.eos_token_id),
                 None,
             )