[NPU] update fused layers for GW (#12459)

* update fused layers for GW

* fix

* fix llama condition for glm model

* update

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

@@ -136,6 +136,7 @@ def from_pretrained(cls, *args, **kwargs):
         mock_device = kwargs.pop('device', None)  # For mock on CPU
         convert_model = kwargs.pop('convert_model', False)
         save_directory = kwargs.pop('save_directory', None)
+        fuse_layers = kwargs.pop('fuse_layers', None)
 
         invalidInputError(
             quantization_group_size in [0, 32, 64, 128],
@@ -204,6 +205,7 @@ def from_pretrained(cls, *args, **kwargs):
                     "transpose_value_cache": transpose_value_cache,
                     "convert_model": convert_model,
                     "save_directory": save_directory,
+                    "fuse_layers": fuse_layers
                 }
                 model = cls.optimize_npu_model(*args, **optimize_kwargs)
             else:
@@ -243,6 +245,7 @@ def optimize_npu_model(cls, *args, **kwargs):
         transpose_value_cache = kwargs.pop("transpose_value_cache", True)
         convert_model = kwargs.pop('convert_model', False)
         save_directory = kwargs.pop('save_directory', None)
+        fuse_layers = kwargs.pop('fuse_layers', None)
 
         if hasattr(model, "llm"):
             llm = model.llm
@@ -282,7 +285,8 @@ def optimize_npu_model(cls, *args, **kwargs):
                         group_size=quantization_group_size,
                         qtype=qtype,
                         convert_model=convert_model,
-                        save_directory=save_directory)
+                        save_directory=save_directory,
+                        fuse_layers=fuse_layers)
         model.save_low_bit = types.MethodType(save_low_bit, model)
         return model
 

python/llm/src/ipex_llm/transformers/npu_pipeline_model/convert_pipeline.py

@@ -195,7 +195,8 @@ def convert_llm(model: torch.nn.Module,
                 group_size: int,
                 qtype: str,
                 convert_model: bool=False,
-                save_directory: str=None):
+                save_directory: str=None,
+                fuse_layers: int=None):
     # whether to set layernorm weight as const
     layernorm_const = os.environ.get("IPEX_LLM_NPU_LAYERNORM_CONST", "1") == "1"
     if group_size == 0:
@@ -216,7 +217,8 @@ def convert_llm(model: torch.nn.Module,
                                n_splits_linear,
                                n_splits_down_proj,
                                group_size,
-                               save_directory)
+                               save_directory,
+                               fuse_layers=fuse_layers)
         return 0
     if model.config.model_type == "llama":
         with tempfile.TemporaryDirectory() as temp_dir:
@@ -422,18 +424,22 @@ def convert_llm_for_deploy(model: torch.nn.Module,
                            n_splits_linear: int,
                            n_splits_down_proj: int,
                            group_size: int,
-                           save_directory: str=None):
+                           save_directory: str=None,
+                           fuse_layers: int=None):
     os.mkdir(save_directory)
     weight_dir = os.path.join(save_directory, "model_weights")
     os.mkdir(weight_dir)
     layernorm_const = os.environ.get("IPEX_LLM_NPU_LAYERNORM_CONST", "1") == "1"
 
     if model.config.model_type == "qwen2":
-        if model.config.hidden_size == 1536:
-            # Qwen2-1.5B-Instruct
-            fused_layers = 1
+        if group_size == 0:
+            if model.config.hidden_size == 1536:
+                # Qwen2-1.5B-Instruct
+                fused_layers = 1 if fuse_layers is None else fuse_layers
+            else:
+                fused_layers = 2 if fuse_layers is None else fuse_layers
         else:
-            fused_layers = 2
+            fused_layers = len(model.model.layers) if fuse_layers is None else fuse_layers
         update_dict = {"kv_len": kv_len,
                        "num_head": model.model.layers[0].self_attn.num_heads,
                        "head_dim": model.model.layers[0].self_attn.head_dim,
@@ -469,20 +475,31 @@ def convert_llm_for_deploy(model: torch.nn.Module,
         embedding_post = False
         cos_sin_input = False
         use_prefill_sdp = False
-        if model.config.vocab_size == 32000:
-            # for Llama2-7B
-            fused_layers = 4
-            use_prefill_sdp = True
-        else:
-            if model.config.intermediate_size == 8192:
+        if group_size == 0:
+            if model.config.intermediate_size == 11008:
+                # for Llama2-7B
+                fused_layers = 4 if fuse_layers is None else fuse_layers
+                use_prefill_sdp = True
+            elif model.config.intermediate_size == 14336:
+                # for Llama3-8B
+                fused_layers = 2 if fuse_layers is None else fuse_layers
+                use_prefill_sdp = True
+            elif not hasattr(model.model.layers[0].self_attn.rotary_emb, "cos_cached"):
                 # llama3.2 1B & # llama3.2 3B
                 embedding_post = True
                 cos_sin_input = True
-                fused_layers = 2
+                fused_layers = 2 if fuse_layers is None else fuse_layers
             else:
-                # for Llama3-8B
-                fused_layers = 2
+                fused_layers = 2 if fuse_layers is None else fuse_layers
+        else:
+            if model.config.intermediate_size in [11008, 14336]:
+                # for Llama2-7B & Llama3-8B
                 use_prefill_sdp = True
+            elif not hasattr(model.model.layers[0].self_attn.rotary_emb, "cos_cached"):
+                # llama3.2 1B & # llama3.2 3B
+                embedding_post = True
+                cos_sin_input = True
+            fused_layers = len(model.model.layers) if fuse_layers is None else fuse_layers
         update_dict = {"kv_len": kv_len,
                        "num_head": model.model.layers[0].self_attn.num_heads,
                        "head_dim": model.model.layers[0].self_attn.head_dim,
@@ -518,7 +535,10 @@ def convert_llm_for_deploy(model: torch.nn.Module,
                             save_directory, weight_dir, transpose_value_cache, max_prompt_len,
                             group_size, layernorm_const, "prefill")
     elif model.config.model_type == "minicpm":
-        fused_layers = 4
+        if group_size == 0:
+            fused_layers = 4 if fuse_layers is None else fuse_layers
+        else:
+            fused_layers = len(model.model.layers) if fuse_layers is None else fuse_layers
         update_dict = {"kv_len": kv_len,
                        "num_head": model.model.layers[0].self_attn.num_heads,
                        "head_dim": model.model.layers[0].self_attn.head_dim,