add save & load support for NPU optimized model (#11999)

* add save &  load support

* fix style

python/llm/src/ipex_llm/transformers/npu_model.py

@@ -174,6 +174,7 @@ def from_pretrained(cls, *args, **kwargs):
                 intra_pp=intra_pp,
                 transpose_value_cache=transpose_value_cache,
             )
+            model.save_low_bit = types.MethodType(save_low_bit, model)
         else:
             from ipex_llm.transformers.npu_models.convert import optimize_llm
             optimize_llm(model)
@@ -209,10 +210,16 @@ def load_low_bit(cls, pretrained_model_name_or_path: str, *model_args, **kwargs)
         ignore_argument(kwargs, "lightweight_bmm")
         ignore_argument(kwargs, "cpu_embedding")
         ignore_argument(kwargs, "embedding_qtype")
-        ignore_argument(kwargs, "optimize_model")
         ignore_argument(kwargs, "modules_to_not_convert")
         ignore_argument(kwargs, "speculative")
         ignore_argument(kwargs, "pipeline_parallel_stages")
+        optimize_model = kwargs.pop("optimize_model", False)
+        max_output_len = kwargs.pop("max_output_len", 1024)
+        max_prompt_len = kwargs.pop("max_prompt_len", 512)
+        inter_pp = kwargs.pop("inter_pp", None)
+        intra_pp = kwargs.pop("intra_pp", None)
+        transpose_value_cache = kwargs.pop("transpose_value_cache", True)
+        modules_to_not_convert = kwargs.pop("modules_to_not_convert", [])
 
         from transformers.models.auto.configuration_auto import AutoConfig
         from transformers.modeling_utils import no_init_weights, get_state_dict_dtype
@@ -351,12 +358,34 @@ def load_low_bit(cls, pretrained_model_name_or_path: str, *model_args, **kwargs)
         logger.info(f"Converting model, it may takes up to several minutes ...")
         from intel_npu_acceleration_library.compiler import create_npu_kernels
 
-        with torch.no_grad():
-            optimize_llm(model)
-            cls.load_convert(qtype, model, quant_device, *model_args, **kwargs)
-            create_npu_kernels(model)
+        if optimize_model:
+            invalidInputError(
+                max_prompt_len < max_output_len,
+                (
+                    f"max_prompt_len ({max_prompt_len}) should be less"
+                    " than max_output_len ({max_output_len})"
+                ),
+            )
+            from ipex_llm.transformers.npu_models.convert_mp import optimize_llm_pre
+
+            if hasattr(model, "llm"):
+                llm = model.llm
+            else:
+                llm = model
+
+            with torch.no_grad():
+                optimize_llm_pre(model, qtype)
+                cls.load_convert(qtype, model, quant_device, modules_to_not_convert,
+                                 *model_args, **kwargs)
+                create_npu_kernels(llm)
 
-        model = model.eval()
+        else:
+            from ipex_llm.transformers.npu_models.convert import optimize_llm
+            optimize_llm(model)
+            with torch.no_grad():
+                cls.load_convert(qtype, model, quant_device, modules_to_not_convert,
+                                 *model_args, **kwargs)
+                create_npu_kernels(model)
 
         if is_sharded:
             loaded_state_dict_keys = sharded_metadata["all_checkpoint_keys"]
@@ -415,6 +444,17 @@ def load_low_bit(cls, pretrained_model_name_or_path: str, *model_args, **kwargs)
         for param in model.parameters():
             param.requires_grad_(False)
 
+        if optimize_model:
+            from ipex_llm.transformers.npu_models.convert_mp import optimize_llm
+            optimize_llm(
+                llm,
+                max_output_len=max_output_len,
+                max_prompt_len=max_prompt_len,
+                inter_pp=inter_pp,
+                intra_pp=intra_pp,
+                transpose_value_cache=transpose_value_cache,
+            )
+
         return model