[Optimization] Text-generation support qwen

examples/huggingface/pytorch/text-generation/quantization/run_generation.py

@@ -119,7 +119,7 @@
                                 excluded_precisions=excluded_precisions,  # default is []
                                )
 elif args.woq:
-    quantization_config = WeightOnlyQuantConfig() #default is A32W4G32
+    quantization_config = WeightOnlyQuantConfig(compute_type="fp32", weight_type="int4_fullrange", group_size=32) #default is A32W4G32
 # bitsandbytes
 elif args.bitsandbytes:
     # GPU device is need for `load_in_4bit` and `load_in_8bit`.
@@ -133,6 +133,8 @@
 if quantization_config is not None:
     user_model = AutoModelForCausalLM.from_pretrained(args.model,
                                                       quantization_config=quantization_config,
+                                                      trust_remote_code=args.trust_remote_code,
+                                                      torchscript=True if args.sq else False,
                                                       use_llm_runtime=False
                                                       )
     if args.sq:
@@ -145,8 +147,8 @@
                                                       load_in_8bit=args.load_in_8bit,
                                                       use_llm_runtime=False
                                                       )
-elif not args.int8 or not args.int8_bf16_mixed:
-    user_model = AutoModelForCausalLM.from_pretrained(args.model, config=config, use_llm_runtime=False)
+elif not args.int8 and not args.int8_bf16_mixed:
+    user_model = AutoModelForCausalLM.from_pretrained(args.model, config=config, trust_remote_code=args.trust_remote_code, use_llm_runtime=False)
     # peft
     if args.peft_model_id is not None:
         from peft import PeftModel

intel_extension_for_transformers/transformers/utils/utility.py

@@ -97,6 +97,10 @@ def generate_dummy_past_key_values(input_bs, model):
             else:
                 new_shape = [input_bs * num_attention_heads, 1, d_k]
             pkv = pkv + (torch.ones(size=new_shape),)
+    elif model.config.model_type == "qwen":
+        new_shape = [input_bs, 1, num_attention_heads, d_k]
+        dummy_tensor = torch.ones(size=new_shape)
+        pkv = tuple(dummy_tensor for _ in range(nb_pkv))
     else:
         new_shape = [input_bs, num_attention_heads, 1, d_k]
         dummy_tensor = torch.ones(size=new_shape)