Fix accessing final norm for Gemma-3 models

src/python/py/models/builder.py

@@ -429,7 +429,7 @@ def make_genai_config(self, model_name_or_path, extra_kwargs, out_dir):
             },
         }
 
-        if self.window_size is not None and self.window_size > 0:
+        if self.ep == "NvTensorRtRtx" and self.window_size is not None and self.window_size > 0:
             genai_config["model"]["decoder"]["sliding_window"] = {"window_size": self.window_size, "slide_key_value_cache": False, "slide_inputs": False}
 
         if self.ep != "cpu":
@@ -2591,31 +2591,27 @@ def make_model(self, input_path):
     def has_final_norm(self, module, orig_model):
         # Find where the language model is stored to check attributes. Some classes
         # store the language model in a different attribute than `model.model`.
-        if hasattr(orig_model, "language_model"):
-            # Model is multimodal
-            # Note: This case is checked first because the `language_model` attribute and the `base_model` attribute
-            # exist for both multimodal models and PEFT models. However they represent different classes and their attributes
-            # differ.
-            model = orig_model.language_model
-        elif hasattr(orig_model, "base_model") and hasattr(orig_model.base_model, "model"):
-            if hasattr(orig_model.base_model.model, "model"):
-                # Model is from PEFT
-                model = orig_model.base_model.model
-            else:
-                # Model is text-based only.
-                model = orig_model.base_model
+        if orig_model.__class__.__name__.startswith("Peft"):
+            # Model is from PEFT
+            model = orig_model.base_model.model
         else:
             model = orig_model
 
-        # Hugging Face names
+        # Hugging Face names (all models loaded with AutoModelForCausalLM.from_pretrained)
+        #
+        # hf_norm:                        for most models
+        # hf_final_layernorm:             for Phi-2
+        # hf_transformer_final_layernorm: for ChatGLM-3
+        # hf_language_model_norm:         for Gemma-3 multimodal (4B, 12B, 27B)
         hf_norm = hasattr(model, "model") and hasattr(model.model, "norm") and module == model.model.norm
         hf_final_layernorm = hasattr(model, "model") and hasattr(model.model, "final_layernorm") and module == model.model.final_layernorm
         hf_transformer_final_layernorm = hasattr(model, "transformer") and hasattr(model.transformer, "encoder") and hasattr(model.transformer.encoder, "final_layernorm") and module == model.transformer.encoder.final_layernorm
+        hf_language_model_norm = hasattr(model, "model") and hasattr(model.model, "language_model") and hasattr(model.model.language_model, "norm") and module == model.model.language_model.norm
 
-        # GGUF names
+        # GGUF names (all models loaded with GGUFModel.from_pretrained)
         gguf_final_norm = hasattr(model, "final_norm") and module == model.final_norm
 
-        hf_names = [hf_norm, hf_final_layernorm, hf_transformer_final_layernorm]
+        hf_names = [hf_norm, hf_final_layernorm, hf_transformer_final_layernorm, hf_language_model_norm]
         gguf_names = [gguf_final_norm]
         return any(hf_names + gguf_names)
 
@@ -3135,15 +3131,14 @@ def __init__(self, config, io_dtype, onnx_dtype, ep, cache_dir, extra_options):
         self.attention_attrs["scale"] = config.query_pre_attn_scalar ** -0.5
         self.is_local = lambda layer_id: layer_id % 2 == 1
 
-
     def make_layernorm(self, layer_id, layernorm, skip, simple, location):
         if "final_norm" in location:
             # Set cast for final LayerNorm since it is a special case and not covered in `make_layer`
             self.layernorm_attrs["cast"]["root_input"] = False
         super().make_layernorm(layer_id, layernorm, skip, simple, location)
 
     def make_layer(self, layer_id, layer):
-        # Gemma2 decoder layer is typically defined as:
+        # Gemma-2 decoder layer is typically defined as:
         # input_layernorm --> attention --> post_attention_layernorm --> pre_ffn_layernorm --> MLP --> post_ffn_layernorm
 
         # Adjust LayerNorm attributes because of extra LayerNorms inserted
@@ -3600,7 +3595,7 @@ def make_layer(self, layer_id, layer):
 class Gemma3Model(Gemma2Model):
     def __init__(self, config, io_dtype, onnx_dtype, ep, cache_dir, extra_options):
         super().__init__(config, io_dtype, onnx_dtype, ep, cache_dir, extra_options)
-        self.is_local = lambda layer_id: bool((layer_id + 1) % config.sliding_window_pattern)
+        self.is_local = lambda layer_id: bool((layer_id + 1) % 6)
         self.rope_local_theta = config.rope_local_base_freq
         self.make_rotary_embedding_multi_cache()
 
@@ -3809,15 +3804,15 @@ def create_model(model_name, input_path, output_dir, precision, execution_provid
             onnx_model = Gemma3Model(config, io_dtype, onnx_dtype, execution_provider, cache_dir, extra_options)
             onnx_model.model_type = "gemma3_text"
         elif config.architectures[0] == "Gemma3ForConditionalGeneration":
-            print("WARNING: This is only generating the text component of the model. Setting `--extra_options exclude_embeds=true` by default.")
-            extra_options["exclude_embeds"] = True
             text_config = config.text_config
             for key in text_config:
                 if not hasattr(config, key):
                     setattr(config, key, getattr(text_config, key))
             if precision == "fp16":
                 print("WARNING: This model loses accuracy with float16 precision. Setting `--precision bf16` by default.")
                 onnx_dtype = ir.DataType.BFLOAT16
+            print("WARNING: This is only generating the text component of the model. Setting `--extra_options exclude_embeds=true` by default.")
+            extra_options["exclude_embeds"] = True
             onnx_model = Gemma3Model(config, io_dtype, onnx_dtype, execution_provider, cache_dir, extra_options)
         elif config.architectures[0] == "GraniteForCausalLM":
             onnx_model = GraniteModel(config, io_dtype, onnx_dtype, execution_provider, cache_dir, extra_options)