script for phi3 (#1541)

Co-authored-by: zhenwei-intel <zhenwei.liu@intel.com>

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

@@ -10,6 +10,7 @@
 from intel_extension_for_transformers.transformers import AutoModelForCausalLM, AutoRoundConfig, RtnConfig, GPTQConfig
 from intel_extension_for_transformers.transformers.llm.quantization.utils import convert_dtype_str2torch
 from transformers.utils import check_min_version
+import contextlib
 
 parser = argparse.ArgumentParser()
 parser.add_argument(
@@ -241,8 +242,10 @@
     generate_kwargs = dict(do_sample=False, temperature=0.9, num_beams=args.num_beams)
     if args.profile_token_latency:
         ipex.transformers.optimize.convert_function(user_model, "greedy_search", _greedy_search)
+        ipex.transformers.optimize.convert_function(user_model, "_greedy_search", _greedy_search)
         if args.disable_optimize_transformers:
             ipex.transformers.optimize.convert_function(user_model, "beam_search", _beam_search)
+            ipex.transformers.optimize.convert_function(user_model, "_beam_search", _beam_search)
         user_model.config.token_latency = True
 
     total_time = 0.0
@@ -253,7 +256,11 @@
         dtype=amp_dtype if amp_enabled else None,
     ):
         for i in range(num_iter + num_warmup):
-            with torch.autograd.profiler_legacy.profile(enabled=args.do_profiling, use_xpu=(args.device=="xpu"), record_shapes=False) as prof:
+            if args.do_profiling:
+                context = torch.autograd.profiler_legacy.profile(enabled=args.do_profiling, use_xpu=True, record_shapes=True)
+            else:
+                context = contextlib.nullcontext()
+            with context as prof:
                 input_ids = tokenizer(
                     prompt, return_tensors="pt").input_ids.to(args.device)
                 tic = time.time()