update for transformers 4.46

basic_demo/trans_batch_demo.py

@@ -4,19 +4,18 @@ Here is an example of using batch request glm-4-9b,
 here you need to build the conversation format yourself and then call the batch function to make batch requests.
 Please note that in this demo, the memory consumption is significantly higher.
 
+Note:
+    Using with glm-4-9b-chat-hf will require `transformers>=4.46.0".
+
 """
 
-from typing import Optional, Union
-from transformers import AutoModel, AutoTokenizer, LogitsProcessorList
+from typing import Union
+from transformers import AutoTokenizer, LogitsProcessorList, AutoModelForCausalLM
 
-MODEL_PATH = 'THUDM/glm-4-9b-chat'
-
-tokenizer = AutoTokenizer.from_pretrained(
-    MODEL_PATH,
-    trust_remote_code=True,
-    encode_special_tokens=True)
-model = AutoModel.from_pretrained(MODEL_PATH, trust_remote_code=True, device_map="auto").eval()
+MODEL_PATH = 'THUDM/glm-4-9b-chat-hf'
 
+tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
+model = AutoModelForCausalLM.from_pretrained(MODEL_PATH, device_map="auto").eval()
 
 def process_model_outputs(inputs, outputs, tokenizer):
     responses = []
@@ -36,11 +35,17 @@ def batch(
         do_sample: bool = True,
         top_p: float = 0.8,
         temperature: float = 0.8,
-        logits_processor: Optional[LogitsProcessorList] = LogitsProcessorList(),
+        logits_processor=None,
 ):
+    if logits_processor is None:
+        logits_processor = LogitsProcessorList()
     messages = [messages] if isinstance(messages, str) else messages
-    batched_inputs = tokenizer(messages, return_tensors="pt", padding="max_length", truncation=True,
-                               max_length=max_input_tokens).to(model.device)
+    batched_inputs = tokenizer(
+        messages,
+        return_tensors="pt",
+        padding="max_length",
+        truncation=True,
+        max_length=max_input_tokens).to(model.device)
 
     gen_kwargs = {
         "max_new_tokens": max_new_tokens,

basic_demo/trans_cli_demo.py

@@ -10,26 +10,21 @@ Note: The script includes a modification to handle markdown to plain text conver
 ensuring that the CLI interface displays formatted text correctly.
 
 If you use flash attention, you should install the flash-attn and  add attn_implementation="flash_attention_2" in model loading.
+
+Note:
+    Using with glm-4-9b-chat-hf will require `transformers>=4.46.0".
 """
 
-import os
 import torch
 from threading import Thread
-from transformers import (
-    AutoTokenizer,
-    StoppingCriteria,
-    StoppingCriteriaList,
-    TextIteratorStreamer,
-    GlmForCausalLM
-)
+from transformers import AutoTokenizer, AutoModelForCausalLM, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
 
-MODEL_PATH = os.environ.get('MODEL_PATH', 'THUDM/glm-4-9b-chat')
+MODEL_PATH = "/share/home/zyx/Models/glm-4-9b-chat-hf"
 
 tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
 
-model = GlmForCausalLM.from_pretrained(
-    MODEL_PATH,
-    # attn_implementation="flash_attention_2", # Use Flash Attention
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_PATH, # attn_implementation="flash_attention_2", # Use Flash Attention
     torch_dtype=torch.bfloat16,  # using flash-attn must use bfloat16 or float16
     device_map="auto").eval()
 

basic_demo/trans_stress_test.py

@@ -4,21 +4,13 @@ from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStream
 import torch
 from threading import Thread
 
-MODEL_PATH = 'THUDM/glm-4-9b-chat'
+MODEL_PATH = 'THUDM/glm-4-9b-chat-hf'
 
 
 def stress_test(token_len, n, num_gpu):
     device = torch.device(f"cuda:{num_gpu - 1}" if torch.cuda.is_available() and num_gpu > 0 else "cpu")
-    tokenizer = AutoTokenizer.from_pretrained(
-        MODEL_PATH,
-        trust_remote_code=True,
-        padding_side="left"
-    )
-    model = AutoModelForCausalLM.from_pretrained(
-        MODEL_PATH,
-        trust_remote_code=True,
-        torch_dtype=torch.bfloat16
-    ).to(device).eval()
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, paddsing_side="left")
+    model = AutoModelForCausalLM.from_pretrained(MODEL_PATH, torch_dtype=torch.bfloat16).to(device).eval()
 
     # Use INT4 weight infer
     # model = AutoModelForCausalLM.from_pretrained(