fix npu lm_head cpu condition

python/llm/example/NPU/HF-Transformers-AutoModels/Multimodal/minicpm-llama3-v2.5.py

@@ -66,6 +66,7 @@
         intra_pp=args.intra_pp,
         inter_pp=args.inter_pp,
         transpose_value_cache=not args.disable_transpose_value_cache,
+        modules_to_not_convert=['vpm', 'resampler']
     )
     tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
 

python/llm/src/ipex_llm/transformers/npu_models/convert_mp.py

@@ -42,18 +42,31 @@ def optimize_llm_pre(model: torch.nn.Module, qtype):
             from ipex_llm.transformers.models.baichuan import pre_compute_inv_freq
             model.apply(pre_compute_inv_freq)
 
+    # MiniCPM-V 2.6 and minicpm-2b must put lm_head on CPU now
+    cpu_lm_head = (
+        (model.config.model_type == "minicpmv" and model.config.hidden_size == 3584 and
+         model.config.vocab_size == 151666)
+        or (
+            model.config.model_type == "minicpm" and model.config.num_hidden_layers == 40
+        )
+        or os.environ.get("IPEX_LLM_CPU_LM_HEAD", "0") != "0"
+    )
+
     if model.config.model_type == "minicpmv" and hasattr(model, "llm"):
         # MiniCPM-V
         if model.config.hidden_size == 2304 and model.config.vocab_size == 122753:
+            # MiniCPM-V 2
             model.llm.config.model_type = "minicpm"
         elif model.config.hidden_size == 3584 and model.config.vocab_size == 151666:
+            # MiniCPM-V 2.6
             model.llm.config.model_type = "qwen2"
         elif model.config.hidden_size == 4096 and model.config.vocab_size == 128256:
+            # MiniCPM-V 2.5
             model.llm.config.model_type = "llama"
         model = model.llm
 
     # lm_head to cpu optimization
-    if os.environ.get("IPEX_LLM_CPU_LM_HEAD", "0") != "0":
+    if cpu_lm_head:
         # disable the optimization by default
         from ipex_llm.transformers.low_bit_linear import SYM_INT4, SYM_INT8
         if qtype == "sym_int4_rtn":