LLM: add chatglm-6b example for transformer_int4 usage (#8392)

* add example for chatglm-6b

* fix

python/llm/example/transformers_int4.py

@@ -16,12 +16,20 @@
 
 import torch
 import os
-from bigdl.llm.transformers import AutoModelForCausalLM
-from transformers import LlamaTokenizer
+import time
+import argparse
+from bigdl.llm.transformers import AutoModelForCausalLM, AutoModel
+from transformers import LlamaTokenizer, AutoTokenizer
 
 if __name__ == '__main__':
-    model_path = 'decapoda-research/llama-7b-hf'
-
+    parser = argparse.ArgumentParser(description='Transformer INT4 example')
+    parser.add_argument('--repo-id-or-model-path', type=str, default="decapoda-research/llama-7b-hf",
+                        choices=['decapoda-research/llama-7b-hf', 'THUDM/chatglm-6b'],
+                        help='The huggingface repo id for the larga language model to be downloaded'
+                             ', or the path to the huggingface checkpoint folder')
+    args = parser.parse_args()
+    model_path = args.repo_id_or_model_path
+    if model_path == 'decapoda-research/llama-7b-hf':
         # load_in_4bit=True in bigdl.llm.transformers will convert
         # the relevant layers in the model into int4 format
         model = AutoModelForCausalLM.from_pretrained(model_path, load_in_4bit=True)
@@ -30,7 +38,24 @@ if __name__ == '__main__':
         input_str = "Once upon a time, there existed a little girl who liked to have adventures. She wanted to go to places and meet new people, and have fun"
 
         with torch.inference_mode():
+            st = time.time()
             input_ids = tokenizer.encode(input_str, return_tensors="pt")
             output = model.generate(input_ids, do_sample=False, max_new_tokens=32)
             output_str = tokenizer.decode(output[0], skip_special_tokens=True)
+            end = time.time()
         print(output_str)
+        print(f'Inference time: {end-st} s')
+    elif model_path == 'THUDM/chatglm-6b':
+        model = AutoModel.from_pretrained(model_path, trust_remote_code=True, load_in_4bit=True)
+        tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+
+        input_str = "晚上睡不着应该怎么办"
+
+        with torch.inference_mode():
+            st = time.time()
+            input_ids = tokenizer.encode(input_str, return_tensors="pt")
+            output = model.generate(input_ids, do_sample=False, max_new_tokens=32)
+            output_str = tokenizer.decode(output[0], skip_special_tokens=True)
+            end = time.time()
+        print(output_str)
+        print(f'Inference time: {end-st} s')