fix qwen load error (#164)

Author: LJ-underdog

neural_speed/convert/convert-hf-to-gguf.py

@@ -64,6 +64,7 @@ def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None = None
             fname_tokenizer,
             cache_dir=fname_tokenizer,
             local_files_only=True,
+            trust_remote_code=True
         )
 
         # Initialize lists and dictionaries for added tokens
@@ -402,7 +403,7 @@ def _set_vocab_gpt2(self):
         toktypes: list[int] = []
 
         from transformers import AutoTokenizer
-        tokenizer = AutoTokenizer.from_pretrained(dir_model)
+        tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
         vocab_size = hparams.get("vocab_size", len(tokenizer.vocab))
         assert max(tokenizer.vocab.values()) < vocab_size
 

neural_speed/convert/convert_bloom.py

@@ -77,7 +77,7 @@ def main(args_in: Optional[List[str]] = None) -> None:
                                                  torch_dtype=torch.float16 if ftype == 1 else torch.float32,
                                                  low_cpu_mem_usage=True,
                                                  trust_remote_code=True)
-    tokenizer = AutoTokenizer.from_pretrained(dir_model)
+    tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
     hparams = config.to_dict()
     print("Loading model: ", dir_model)
 

neural_speed/convert/convert_dolly.py

@@ -79,8 +79,9 @@ def main(args_in: Optional[List[str]] = None) -> None:
         from modelscope import AutoModelForCausalLM, AutoTokenizer
     else:
         from transformers import AutoModelForCausalLM, AutoTokenizer
-    model = AutoModelForCausalLM.from_pretrained(dir_model, torch_dtype=torch.float16 if ftype == 1 else torch.float32)
-    tokenizer = AutoTokenizer.from_pretrained(dir_model)
+    model = AutoModelForCausalLM.from_pretrained(dir_model, torch_dtype=torch.float16 if ftype == 1 else torch.float32,
+                                                 trust_remote_code=True)
+    tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
     print("Loading model: ", dir_model)
     model.eval()
     for p in model.parameters():

neural_speed/convert/convert_gptj.py

@@ -74,7 +74,7 @@ def main(args_in: Optional[List[str]] = None) -> None:
     else:
         from transformers import AutoModelForCausalLM, AutoTokenizer
     print("Loading model: ", dir_model)
-    model = AutoModelForCausalLM.from_pretrained(dir_model, low_cpu_mem_usage=True)
+    model = AutoModelForCausalLM.from_pretrained(dir_model, low_cpu_mem_usage=True, trust_remote_code=True)
     tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
     hparams = model.config.to_dict()
     list_vars = model.state_dict()

neural_speed/convert/convert_gptneox.py

@@ -81,8 +81,9 @@ def main(args_in: Optional[List[str]] = None) -> None:
     else:
         from transformers import AutoModelForCausalLM, AutoTokenizer
     print("Loading model: ", dir_model)
-    model = AutoModelForCausalLM.from_pretrained(dir_model, torch_dtype=torch.float16 if ftype == 1 else torch.float32)
-    tokenizer = AutoTokenizer.from_pretrained(dir_model)
+    model = AutoModelForCausalLM.from_pretrained(dir_model, torch_dtype=torch.float16 if ftype == 1 else torch.float32,
+                                                 trust_remote_code=True)
+    tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
     model.eval()
     for p in model.parameters():
         p.requires_grad = False

neural_speed/convert/convert_opt.py

@@ -79,8 +79,9 @@ def main(args_in: Optional[List[str]] = None) -> None:
     else:
         from transformers import AutoModelForCausalLM, AutoTokenizer
     print("Loading model: ", dir_model)
-    model = AutoModelForCausalLM.from_pretrained(dir_model, torch_dtype=torch.float16 if ftype == 1 else torch.float32)
-    tokenizer = AutoTokenizer.from_pretrained(dir_model)
+    model = AutoModelForCausalLM.from_pretrained(dir_model, torch_dtype=torch.float16 if ftype == 1 else torch.float32,
+                                                 trust_remote_code=True)
+    tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
     model.eval()
     hparams = model.config.to_dict()
 

neural_speed/convert/convert_phi.py

@@ -86,7 +86,7 @@ def write_vocab_gguf(dir_model, hparams, gguf_writer):
         toktypes: list[int] = []
 
         from transformers import AutoTokenizer  # type: ignore[attr-defined]
-        tokenizer = AutoTokenizer.from_pretrained(dir_model)
+        tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
         vocab_size = hparams.get("vocab_size", len(tokenizer.vocab))
         assert max(tokenizer.vocab.values()) < vocab_size
 

neural_speed/convert/convert_quantized_bloom.py

@@ -121,7 +121,7 @@ def bytes_to_unicode():
 
 model_name = "/mnt/disk1/data2/zhenweil/models/bloom/bloom-7b1"
 prompt = "Once upon a time, a little girl"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
 config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
 inputs = tokenizer(prompt, return_tensors="pt").input_ids
 streamer = TextStreamer(tokenizer)

neural_speed/convert/convert_quantized_qwen.py

@@ -159,15 +159,18 @@ def main(args_in: Optional[List[str]] = None) -> None:
             "i", hparams["kv_channels"] if "kv_channels" in hparams else int(hparams["hidden_size"] /
                                                                              hparams["num_attention_heads"])))
     f.write(struct.pack("i", ftype))
-    f.write(struct.pack("i", hparams["seq_length"] if "seq_length" in hparams else hparams["max_position_embeddings"]))
+    f.write(struct.pack("i", hparams["max_position_embeddings"]))
     f.write(struct.pack("f", 0.0))
     f.write(struct.pack("f", 0.0))
     f.write(struct.pack("i", 0))
     f.write(struct.pack("i", 0))  # word_embed_proj_dim (for opt)
     f.write(struct.pack("i", 0))  # do_layer_norm_before (for opt)
 
     f.write(struct.pack("i", 0))
-    f.write(struct.pack("i", hparams["intermediate_size"]))
+    if hparams['model_type']=='qwen2':
+        fout.write(struct.pack("i", hparams["intermediate_size"]))
+    else:
+        fout.write(struct.pack("i", int(hparams["intermediate_size"]/2)))
     f.write(struct.pack("i", 0))
     f.write(struct.pack("i", 0))  # n_experts
     f.write(struct.pack("i", 0))  # n_expert_used

neural_speed/convert/convert_qwen.py

@@ -81,8 +81,8 @@ def main(args_in: Optional[List[str]] = None) -> None:
     else:
         from transformers import AutoModelForCausalLM, AutoTokenizer
     print("Loading model: ", dir_model)
-    model = AutoModelForCausalLM.from_pretrained(dir_model)
-    tokenizer = AutoTokenizer.from_pretrained(dir_model)
+    model = AutoModelForCausalLM.from_pretrained(dir_model, trust_remote_code=True)
+    tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
     model.eval()
     for p in model.parameters():
         p.requires_grad = False
@@ -111,15 +111,18 @@ def main(args_in: Optional[List[str]] = None) -> None:
                                                                              hparams["num_attention_heads"])))
     fout.write(struct.pack("i", ftype))
     fout.write(
-        struct.pack("i", hparams["seq_length"] if "seq_length" in hparams else hparams["max_position_embeddings"]))
+        struct.pack("i", hparams["max_position_embeddings"]))
     fout.write(struct.pack("f", 0.0))
     fout.write(struct.pack("f", 0.0))
     fout.write(struct.pack("i", 0))
     fout.write(struct.pack("i", 0))  # word_embed_proj_dim (for opt)
     fout.write(struct.pack("i", 0))  # do_layer_norm_before (for opt)
 
     fout.write(struct.pack("i", 0))
-    fout.write(struct.pack("i", hparams["intermediate_size"]))
+    if hparams['model_type']=='qwen2':
+        fout.write(struct.pack("i", hparams["intermediate_size"]))
+    else:
+        fout.write(struct.pack("i", int(hparams["intermediate_size"]/2)))
     fout.write(struct.pack("i", 0))
     fout.write(struct.pack("i", 0))  # n_experts
     fout.write(struct.pack("i", 0))  # n_expert_used